Enabling seamless electromobility through smart vehicle-grid … · 2020. 5. 3. · H2020 GV-8-2015...

H2020 GV-8-2015 Electric vehiclesrsquo enhanced performance and

integration into the transport system and the grid

Enabling seamless electromobility through smart vehicle-grid integration

Project Nordm 713864

D96 - Final exploitation framework Market analysis project impact and sustainability plan

Responsible BCNEco

Contributors All partners

Document Reference D96

Dissemination Level Public

Version Version 10

Date 03052020

D96 ndash Report on the final exploitation framework Version 11 Date 13092019

2

Executive Summary

This deliverable describes the final results and decisions taken by the partners with respect to exploitation activities of the ELECTRIFIC project In order to reach these results partners were requested to have a market-oriented mind putting themselves in the shoes of a potential customer that would like to adopt ELECTRIFIC in order to release current business pains Additionally partners used as basis for this analysis the preliminary results described in the previous deliverable of this work package (D94 - Initial description of the project impact and business models definition)

On the one hand taking into account the project value propositions and their related business models we assessed the maturity of the market in which the different functionalities of ELECTRIFIC can be offered Impact of the ELECTRIFIC solutions within these markets is also described On the other hand we grouped these functionalities we identified the owner of their intellectual property and finally there is a description of how these owners plan to exploit them The document finishes with an analysis of the project sustainability plans


3

Contributors table

DOCUMENT SECTION AUTHOR(S) REVIEWER(S)

I Introduction Mariacutea Peacuterez (RDGfi) Susanna Garciacutea Larios (BCNEco)

II Correlation among the exploitation elements described in

this deliverable

Mariacutea Peacuterez (RDGfi) Susanna Garciacutea Larios (BCNEco)

III Market Analysis

III1 Methodologies Sonja Klingert (UNIMA) Mariacutea Peacuterez Jeremy Wautelet ( RDGfi)

III2 Market Maturity Model

III21 MMM structure

Benedikt Kirpes Marc Langhorst Sonja Klingert Florian Kutzner (UNIMA) Xavier Guarderas (BCN) Markus Eider Nicki Bodenschatz (THD) Philipp Danner Wolfgang Duschl (BAG) Johannes Riese (HTB) Michael Achatz (E-Wald) Jan Mrkos (CVUT) Jindrich Muller

(eSumava)

Mariacutea Peacuterez Jeremy Wautelet (RDGfi)

Mariacutea Peacuterez Jeremy

Wautelet (RDGfi)

III22 Product-based Weighting of Areas

Benedikt Kirpes Marc Langhorst Sonja Klingert (UNIMA) Markus Eider Nicki Bodenschatz (THD) Philipp Danner (BAG) Jeremy Wautelet (RDGfi)

III23 Results Market Maturity for ELECTRIFIC

products

Marc Langhorst Benedikt Kirpes (UNIMA)

III3 Impact Analysis

Marc Langhorst Sonja Klingert Lukas Weiss Fabian Seitz (UNIMA) Philipp Danner Wolfgang Duschl (Bayernwerk) Dominik Danner Hermann de Meer (UNI PASSAU) Markus Eider Nicki

Bodenschatz (THD)


IV ELECTRIFIC business models to Exploitable components

IV1 ELECTRIFIC exploitable components

IV11 Direct from the ELECTRIFIC

solution IP table defined by all partners

Susanna Garciacutea Larios (BCNEco) Mariacutea Peacuterez (RDGfi) Dominik

Danner (UNI PASSAU)

IV12 Indirect from the ELECTRIFIC solution

- Tool analyze dynamic pricing

Michal Jakob Jan Mrkos (CVUT) Susanna Garciacutea Larios (BCNEco) Mariacutea Peacuterez (RDGfi) - Event-driven

solution Gunther Verhemeldonck (RDGfi)


4

IV2 ELECTRIFIC capitalization on

Knowledge

All written by Robert Basmadjian (ENERGIS)

Susanna Garciacutea Larios (BCNEco) Mariacutea Peacuterez (RDGfi) Dominik Danner (UNI PASSAU)

IV3 OpenAPIs from ELECTRIFIC

Robert Basmadjian (ENERGIS) Markus Eider (open source) (THD)


Danner (UNI PASSAU)

V Partners Exploitation activities

V1 RDGfi Mariacutea Peacuterez (RDGfi) Philippe Guillen (RDGFI)

Susanna Garciacutea Larios (BCNEco) Mariacutea

Peacuterez (RDGfi)

V2 UNIMA Benedikt Kirpes Celina Kacperski Sonja Klingert Florian Kutzner

(UNIMA)

V3 ENERGIS Robert Basmadjian Frederic Wauters (ENERGIS)

V4 CVUT Michal Jakob Jan Mrkos (CVUT)

V5 THD Markus Eider Nicki Bodenschatz (THD)

V6 UNI PASSAU Dominik Danner Hermann de Meer (UNI PASSAU)

V7 HTB Michael Siepmann Klaus Kohlmayr Johannes Riese (HTB)

V8 BCNEco Javier Guarderas (BCNEco)

V9 Bayernwerk Philipp Danner Wolfgang Duschl (Bayernwerk)

V10 E-WALD Michael Achatz Franz Gotzler (E-WALD)

V11 e-Sumava Juraj Donoval (e-Sumava)

VI Exploitation activities

VI1 Bayernwerk pilot Philipp Danner (Bayernwerk) Susanna Garciacutea Larios (BCNEco) Mariacutea

Peacuterez (RDGFI)

VI2 InterConnect project Demonstrator

in France Mariacutea Peacuterez (RDGFI)

Susanna Garciacutea Larios (BCNEco) Dominik

Danner (UNI PASSAU)

VI3 TMB exploitation Javier Guarderas (BCNEco) Susanna Garciacutea Larios (BCNEco)

VI4 EV Corporate fleet

Mariacutea Peacuterez (RDGFI) Susanna Garciacutea Larios (BCNEco) Dominik

Danner (UNI PASSAU)

VI Conclusions Sustainability plan

Mariacutea Peacuterez (RDGFI) Robert Basmadjian (ENERGIS)


Danner (UNI PASSAU)

Vll References Susanna Garcia na


5

Table of Contents

I INTRODUCTION 11

I1 Purpose and organization of the document 11

I2 Scope and audience 11

II CORRELATION AMONG THE EXPLOITATION ELEMENTS DESCRIBED IN THIS DELIVERABLE 12

III MARKET ANALYSIS 13

III1 Methodologies 13

III11 Market maturity models 13

III12 Impact Analysis 14

III2 Market Maturity Model 14

III21 MMM structure (parameters and collected data) 17

III22 Product-Based Weighting of Areas 33

III23 Results Market Maturity for ELECTRIFIC products in Germany Spain and Czech Republic 41


III31 Mobility App 45

III32 Smart Charging Solution 54

III33 Charging Scheduler 61

IV ELECTRIFIC BUSINESS MODELS TO EXPLOITABLE COMPONENTS 68

IV1 ELECTRIFIC exploitable components 68

IV11 Direct from the ELECTRIFIC solution 68

IV12 Exploitation results derived from the ELECTRIFIC solution 70

IV2 ELECTRIFIC capitalization on knowledge 72

IV21 Consultancy 72

IV22 Processed data 72

IV23 New advertisement channel 72

IV3 OpenAPIs from ELECTRIFIC 72

V PARTNERS EXPLOITATION PLANS 74

V1 RDGfi 74

V11 Description of the Intellectual Property of RDGfi 74

V12 Exploitation strategy 74

V2 University of Mannheim 77

V21 Description of the Intellectual Property of UNIMA 77


V3 ENERGIS 79

V31 Description of the Intellectual Property of ENERGIS 79


6


V4 Czech Technical University in Prague 81

V41 Description of the Intellectual Property of CTUV 81


V5 Deggendorf Institute of Technology 83

V51 Description of the Intellectual Property of THD 83

V52 Exploitation plan 83

V6 University of Passau 84

V61 Description of the Intellectual Property of Uni Passau 84


V7 Has-to-be GmbH 84

V71 Description of the Intellectual Property of Has-to-be GmbH 84


V8 Agegravencia drsquoEcologia Urbana de Barcelona (BCNecologia) 85

V81 Description of the Intellectual Property of BCNecologia 85


V9 Bayerwerk AG 85

V91 Description of the Intellectual Property of Bayernwerk AG 85


V10 E-WALD GmbH 86

V101 Description of the Intellectual Property of E-WALD GmbH 86


V11 e-Šumavacz sro 87

VI EXPLOITATION ACTIVITIES 88

VI1 Bayernwerk pilot 88

VI11 Areas and assets 88

VI12 ELECTRIFIC components 90

VI13 Analysis and results 91

VI2 InterConnect project Demonstrator in France 93

VI3 TMB exploitation 94

VI4 EV Corporate fleet B2B model proof-of-concept 94

VII CONCLUSIONS SUSTAINABILITY PLAN 95

VIII REFERENCES 96

IX APPENDIX 97

IX1 Data Collection 97

IX11 Area EV amp Fleet 97

IX12 Area Grid amp Energy 105

IX13 Area Charging Infrastructure 108


7

IX14 Area Consumers amp Society 117

List of Figures

Figure 1 ELECTRIFIC exploitation roadmap 12

Figure 2 E-MMM country profile template 15

Figure 3 E-MMM and the ADAS Component 15

Figure 4 E-MMM and the Smart Charger Component16

Figure 5 E-MMM and the Charging Scheduler component 16

Figure 6 Aggregated Market Maturity levels for the ADAS App in Germany 42

Figure 7 Aggregated Market Maturity levels for the ADAS App in Spain 42

Figure 8 Aggregated Market Maturity levels for the ADAS App in the Czech Republic 43

Figure 9 Aggregated Market Maturity levels for the Smart Charger in Germany 43

Figure 10 Aggregated Market Maturity levels for the Smart Charger in Spain 44

Figure 11 Aggregated Market Maturity levels for the Smart Charger in the Czech Republic 44

Figure 12 Aggregated Market Maturity levels for the Charging Scheduler in Germany 44

Figure 13 Aggregated Market Maturity levels for the Charging Scheduler in Spain 45

Figure 14 Aggregated Market Maturity levels for the Charging Scheduler in Czech Republic 45

Figure 15 Vilshofen 46

Figure 16 Transformer loading in scenario A (baseline) without ADAS The blue line marks the transformerrsquos nominal capacity of 400kVA 50

Figure 17 Transformer loading in scenario 1 with 100 adherence to ADAS 51

Figure 18 Line 24 loading in scenario 1 with 100 adherence to ADAS 51

Figure 19 Line 24 loading in scenario 6 with 65 EV penetration and no ADAS 53

Figure 20 Line 24 loading in scenario 7 with 65 EV penetration and 85 adherence to ADAS 53

Figure 21 Different types of transformer stations 56

Figure 22 BDEW H0 standard load profile for households on a working day in winter 58

Figure 23 Cumulative cost comparison for the scenario of an older existing grid 59

Figure 24 Cumulative cost comparison for the scenario of urban areas 60

Figure 25 Cumulative cost comparison for the scenario of unplanned changes in the building area 61

Figure 26 Change in ratio of renewable energy for the Spanish peninsula 63

Figure 27 Visualization of scheduled charging processes 67

Figure 28 Producerconsumer event store overview 70

Figure 29 Actors interact with each other by sending messages to each other 71

Figure 30 GFI EampU Sector IDCard 2019 75

Figure 31 Gfi EampU Sector Key References 2019 75


8

Figure 32 Gfi Smart Mobility use cases 76

Figure 33 Screenshot of Gfis booklet on electromobility 76

Figure 34 EnergisCloud AI-Modeling platform 81

Figure 35 Location of the Bayernwerk pilot (marked in green) 88

Figure 36 Charging need (upper part) and charging flexibility (lower part) extracted from ZE service data collection 89

Figure 37 Installed smartphone with running the ELECTRIFIC App 90

Figure 38 Bayernwerk pilot assets structure shown in Energis 91

Figure 39 Example of the analysis of fleet usage 92

Figure 40 Improvement of the intake of renewable energies in the Bayernwerk fleet 93

Figure 41 InterConnect project French pilot with electromobility solutions 93

Figure 42 ELECTRIFIC for EV Corporate fleet PoC94

Figure 43 Examples of ADAS for EV corporate driver94

List of Tables

Table 1 Parameters Area Electric Vehicles and Fleets 17

Table 2 Country level classification (Electric Vehicles and Fleet) 19

Table 3 Parameters Area Energy Supply amp Grid 21

Table 4 Country level classification (Energy Supply amp Grid) 23

Table 5 Parameters Area Charging Infrastructure 24

Table 6 Country level classification (Charging Infrastructure) 27

Table 7 Parameters Area Consumers amp Society 28

Table 8 Country level classification (Consumers amp Society) 31

Table 9 Simulation Scenarios without ADAS 48

Table 10 Simulation Scenarios with ADAS and Sensitivity Analysis 49

Table 11 Renewable optimization by using the charging scheduler 64

Table 12 Charging price optimisation by using the charging scheduler 65

Table 13 Charging infrastructure at Bayernwerk pilot areas88


9

Table of Acronyms and Definitions

Acronym Meaning

ADAS Advanced Driver Assistant System

BCNecologia Agegravencia drsquoEcologia Urbana de Barcelona

BDEW German Federation of Energy and Water

BEV Battery electric vehicle

CH Charging at a home

CPCS Charging at a public charging stations

CW Charging at a workplace

CDRs Charging data records

CO Consumers amp Society

COA CO Approval of E-Mobility

COD CO Demand for E-Mobility

COE CO Environmental Awareness

COl CO Information Level of Users

CO2 Carbon dioxide

CP Charging profiles

CS Charging station

CSB CS Business

CSI CS Intelligence

CSL CS Legislation and Government

CSP CS Public Network

CSS CS Standardization

CSMS Charging Station Management System

CSO Charging Station Operator

CTAPD Computer to Assist Persons with Disabilities

CVUT Czech Technical University

CZE Czech Republic

DSO Distribution System Operator

EFO Electric fleet operator

E-MMM ELECTRIFIC Market Maturity Model

ENE Energy Supply

ENG Power grid situation

ENL Legislation

ENT Technology and standardization

ES Energy service

ESP Espantildea


10

EUC Car Engine Unit Control

EV Electric vehicle

EVC Cost of Electro Mobility

EVG Governmental Legislation Incentives and Goals

EVO Offer Supply of EVs

EVS EV sharing

EVT EV Technology

GER Germany

GMP German mobility panel

IDCard Identification card

IP Intellectual property

ITC Information Technologies and Communication

KBA Kraftfahrtbundesamt

kVA kilowatt

kW Kilowatt

KPI Key Performance Indicator

LEDs Lights to energy-efficient

Load profile List of energy values with start and end times for each charging points

LV Low volt

MV Megavolt

OCPP Open Charge Point Protocol

PCS Public Charging Stations

PoC Proof-of-concept

PV Photovoltaic Energy

PV Present Value

REN Renewable

SMAG Smart Grid Architecture Model

SoC State of Charge

THD Technishe Hochschule Deggendorf

TMB Transports Metropolitans de Barcelona

TSO Transmission System Operators

Uni Passau University of Passau

UNIMA University of Mannheim

WEB Ul Web Unit Interface


11

I INTRODUCTION

I1 Purpose and organization of the document

This deliverable is part of the exploitation activities of the project aiming at defining the elements for facilitating the market adoption of the ELECTRIFIC results It is the third exploitation deliverable after D94

The purpose is to provide final findings on three key elements Market Analysis including the Market Maturity Model and the Impact Analysis Exploitable results and its market strategy and Sustainability plan

The document is organized based on the abovementioned main blocks with an introductory overview detailing how these elements fit into the overall ELECTRIFIC exploitation strategy

Section II Correlation between the exploitation elements described in this deliverable

Section III Market Analysis

o Methodologies

o Market Maturity Model

o Impact Analysis

Section IV ELECTRIFIC Business Models to Exploitable Results

o ELECTRIFIC exploitable components

o ELECTRIFIC services

o OpenAPIs

Section V Exploitation activities

Section VI Sustainability Plan

I2 Scope and audience

The deliverable is publicly released It is intended to be used by interested parties to 1) understand whatrsquos the final offering of the project whatrsquos expected to reach the market partners will offer it and via which channels 2) assess the market maturity related to the eMobility ecosystem and how ELECTRIFIC can impact it


12

II CORRELATION AMONG THE EXPLOITATION

ELEMENTS DESCRIBED IN THIS DELIVERABLE

The ELECTRIFIC project had a duration of three years The project tasks were performed based on iterations in which one iteration increased the level of complexity of the previous and at the same time capitalized on lessons learn in order to fine-tune the final results

In ELECTRIFIC the first market related action was performed by WP2 as part of the definition of the functional framework of the project In the deliverable D22 ndash Initial description of scenarios business requirements and use cases the ecosystem of ELECTRIFIC was

analysed understood (baseline scenario) and extrapolated into a view consistent with the ELECTRIFIC objectives and its software solution In addition it also comprised an analysis of contracts among the stakeholders how they price the services that they trade amongst each other and finally how the end-users of the ELECTRIFIC ADAS can be incentivized to follow the guidelines for optimized behaviour

Based on these findings deliverable D92 - Initial market analysis and first standardization actions analysed the market conditions in the e-mobility ecosystem and identified potential markets for the ELECTRIFIC solution This first iteration of the market analysis focused on analysing the current conditions and estimating future market conditions (qualitative market analysis) in the e-mobility sector

These results were the foundation for the identification of the business models in D94 - Initial description of the project impact and business models definition deliverable (Section I) These

models were complemented by the first finding on the market maturity model that helped defining how mature potential ELECTRIFIC markets in different countries are with respect to the adoption of the project results

In this deliverable the business models are transformed into potential market offering extracted from the project and each of them is inserted into the maturity model to understand the actual market maturity to an specific project result and therefore to be able to analysis its potential impact

Figure 1 ELECTRIFIC exploitation roadmap


13

III MARKET ANALYSIS

The market analysis in ELECTRIFIC consists of two parts that complement each other

1 ELECTRIFIC Market Maturity Model (E-MMM)

2 Impact Analysis

The E-MMM is aimed at assessing the market maturity in the three partner states where ELECTRIFIC trials take place differentiated according to the three main product components developed by ELECTRIFIC ELECTRIFIC ADAS Smart Charger and Charging Scheduler

The impact analysis is aimed at assessing the technical and economic impact in some example cases in order to give an idea of how the ELECTRIFIC components could save increase the share of renewables or grid stability in specific cases

In this chapter first the general methodologies for market maturity models and impact analysis

are explained and then the results for both the E-MMM and the impact analyses are presented

III1 Methodologies

III11 Market maturity models

Maturity models have been used frequently to examine the maturity level of a product system technology or a process especially in the field of IT management The idea of a maturity model is to break down the overall maturity of a system into a handful of different domains which are then analysed separately For each dimension various stages of maturity are defined by creating scenarios according to the maturity level which are then evaluated via objective criteria subjective expert opinions or a combination thereof Due to their flexible framework maturity models can be applied in many different settings if used to assess the maturity of a market for a product they are called market maturity models

De Bruin Freeze Kaulkarni and Rosemann [BFKR05] emphasize that the purpose of a maturity model can be either descriptive prescriptive or comparative in nature The application of a model that is recognized as single point encounters with no establishment for improving maturity or providing relationships to performance is defined as descriptive The descriptive model is suitable for evaluating the here-and-now situation A prescriptive model specifies importance on the domain relationships to business performance Furthermore it implies how to approach maturity improvement to affect business value positively If a model enables benchmarking across regions or industries it is characterized as a comparative model [BeKP09 BFKR05 PCCW93] Even though these types of maturity models can be identified as separate it is argued that they primarily represent evolutionary phases related to the lifecycle of a maturity model [RoBH04] First a maturity model is characterized as descriptive to develop a richer understanding of the as-is domain situation After this a maturity model can then be evolved into becoming prescriptive and then further be developed into being comparatively De Bruin Freeze Kaulkarni and Rosemann [BFKR05] have created a framework that forms a comprehensive basis to guide the development of a maturity model through the three phases Furthermore there exist six main phases within the generic framework of developing a maturity model

Phase 1 ndash Scope The first phase when developing a maturity model is to establish the scope [BeKP09 PCCW93 RoBH04] An essential part of this phase is to determine which focus the model should have as well as the intended audience

Phase 2 ndash Design The second phase is to define a design or architecture for the maturity model that determine the basis for further development and application [BFKR05] Additionally it should be designed to meet the needs of the audience


14

Phase 3 ndash Populate In phase three it is vital to recognize what needs to be measured in the maturity assessment and how this can be measured [BeKP09 PCCW93 RoBH04]

Phase 4 ndash Test After the maturity model is populated it needs to be tested for consistency and relevance Furthermore it is vital to test both the construct of the maturity model and the validity reliability and generalizability [BFKR05]

Phase 5 ndash Deploy In this phase it is essential that the model should be made available for application and to confirm the extent of the generalizability of the maturity model [BFKR05]

Phase 6 ndash Maintain The goal of the maturity model has a significant influence on the resources vital to maintaining growth and application of the mode [BFKR05]


Impact analyses are carried through based on simulations A simulation is the mimicry of real-world operations of a system through the application of models It is frequently used when there is either no production environment available where it is possible to try out different operation modes or if the use of an existing production environment for the experimental analysis of various operation modes carries too much risk ELECTRIFIC experiences the combination of both issues on the one hand the power grid is the backbone for economy and society on the other hand exploring the impact of an increasing diffusion of electric mobility is not possible as for the time being EVs are still an exceptional view on European roads Therefore three exemplary cases with different levels of detail have been analysed


The EU project ELECTRIFIC aims at seamlessly integrating electric mobility into the electricity grid by enabling users to adjust their routing and charging behaviour to the requirements of both grid-friendliness and battery-friendliness and to maximize the intake of renewables while respecting the personal constraints of the user One important feature to make electric mobility users adapt their plans is a set of incentive structures consisting of an optimized combination of psychological and materialfinancial incentives targeted at a specific user group The ELECTRIFIC project has developed a tool box offering basically 3 components the ADAS green navigation system the smart charger and the charging scheduler In order to assess the chances for such a product suite on several European markets the E-MMM was developed according to the procedure described above as a comparative model As mentioned this type of model allows not only to describe the current market for ELECTRIFIC in a specific region but to compare the market maturity of different regions and components

In the preceding deliverables E-MMM was developed until the above-mentioned PHASE 3 This means that the relevant areas were determined ie the EV area the areas for energy supply and charging stations and finally the area representing the maturity of consumers and society in general Subareas were identified for all of them and metrics determined The current and last iteration of the market analysis thus started on this basis including an outline how data collection might be implemented

The result is a profile that captures the maturity of each country for each component as shown in Figure 2


15

Figure 2 E-MMM country profile template

In order to link the E-MMM and the ELECTRIFIC Business Models the general areas and subareas had to fine-tuned The value proposition of the business models aka the products offered are basically combinations of the ELECTRIFIC components in different packages marketing channels or other characteristics Therefore for each ELECTRIFIC component a specific market maturity has to be determined in each country This was done when aggregating the different categories in the subareas into exactly one maturity level for the areas EV grid CS and consumers The results of applying metrics and categorizing these into levels are summed up after adding weights to each category which are specific for each product component The specific weights applied in order to ldquopersonalizerdquo the market maturity of a partner nation for a specific component can be found in the appendix The general idea is explained in the following section Please note that the colouring of the areas is just imaginary in order to give an idea how this might look like in the end

Mapping the ADAS Component with the E-MMM

Figure 3 E-MMM and the ADAS Component

The ADAS component can comprise a lot of different functionalities Not taking into account other components like the smart charger ADAS is a routing tool that helps EV drivers find charging options with high renewable shares on a route In order to do this the areas of the E-MMM need not be very much developed ndash obviously there need to be some EVs operated by potential customers and a minimum density of CS that are integrated into some maps that can serve as information sources for the ADAS In order to calculate and present via ADAS the greenest route defined as the route with the highest consumption of non-renewables the requirements to society and grid are much higher as the more transparent ADAS icons in Figure 3 show


16

Mapping the Smart Charger component with E-MMM

Figure 4 E-MMM and the Smart Charger Component

Figure 4 shows that the requirements for market conditions from the part of the smart charger are rather high Offering a smart charger component will only be necessary in low-voltage grids where the penetration rate of EVs is considerably high as is the maturity of the charging infrastructure Especially the grid situation must allow for integrating flexible loads and obviously the higher the volatility in the local grid from renewables the more the compensating measures by controlling EV charging is called for However as the split row in level 4 intends to indicate as local low voltage grids are very heterogeneous in Europe these are not conditions that apply on state level but rather on low voltage grid level For this component however the consumers and society need to be mature only with regards to the acceptance of the EV technology ndash as the smart charger approach is mediated to the user via tariffs and cost differences it is not necessary for the final customers to be aware of environmental issues

Mapping the Charging Scheduler with the E-MMM

Figure 5 E-MMM and the Charging Scheduler component

The preconditions to offer a charging scheduler on a market for EV fleet management tools are comparably low Figure 5 highlights that contrary to the other components the charging scheduler only needs the presence of EV fleets in order to offer one core functionality which is the fleet management of charging processes with respect to various different objectives like battery friendliness Battery friendliness is implemented in a rule-based manner and thus does not need any interaction beyond the EV fleet and battery history data However also in this case the component can live up to its potential only if the communication of data between the CS and the EVs flows freely In case the CS might communicate for instance variable prices to the charging scheduler Therefore the transparent version of the charging scheduler icon depends on a high level (3-4) of maturity of the CS infrastructure


17

III21 MMM structure (parameters and collected data)

III21a Area Electric Vehicles and Fleets [MarkusNicki]

The MMM area Electric Vehicles and Fleets consists of an analysis of the country fleet in terms of number and price of BEVs their usage in car-sharing and BEV technology readiness Additionally governmental goals and incentives are included since this tells about the willingness of technological improvement in the electro mobility sector The Electric Vehicles and Fleet area considers only BEVs without any hybrid or plug-in hybrid vehicles

III21a1 Relevant parameters

Table 1 contains all parameters to determine the market maturity of the MMM area Electric Vehicles and Fleets It consists of the subareas ldquoOffer Supply of EVsrdquo (EVO) ldquoEV sharingrdquo (EVS) ldquoEV Technologyrdquo (EVT) ldquoCost of Electro Mobilityrdquo (EVC) and ldquoGovernmental Legislation Incentives and Goalsrdquo (EVG) Most parameters such as EVO01 allow a precise comparison between countries However since EV manufacturers usually operate on international level there may be similar levels for neighboring countries One example for this is EVT01 as the top ten sold EV models can be the same in the countries Hence the average EV driving range of the most popular models is equal

Table 1 Parameters Area Electric Vehicles and Fleets

Sub-Area ID Parameter

Name Description

Metric

(Classification) Level 0 Level 1 Level 2 Level 3 Level 4

Offer Supply of EVs

EVO01 Ratio of BEVs

This parameter describes the existing ratio of BEVs in the country fleet

Ratio of BEVs in country fleet vs all existing cars in country fleet []

lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02

Ratio of EVs registered bought in 2018

The share of BEVs in new registrations of cars

Ratio of BEV registrations vs overall car registrations []

lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Sharing EVS01 Customer ratio

Describes the ratio of drivers in the country who use car-sharing

Number of customers of car-sharing vs the number of citizens

lt 1 lt 5 gt= 5 gt= 10 gt= 20


18

with driving license []

EVS02 Car-sharing Providers

Number of car-sharing providers

Absolute number of car-sharing providers in the country

lt 5 gt= 5 gt= 20 gt= 40 gt= 80

EVS03 Ratio of BEVs in car-sharing

Ratio of BEVs in total car-sharing vehicles (from all providers)

Amount of BEVs vs overall amount of cars in the countryrsquos car-sharing fleet []

lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology

EVT01 BEV driving range

This parameter describes the average driving range provided by most BEVs in the country registered in a year

The average BEV driving range of the top ten sold BEVs sold in the country [km]

lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs

This parameter describes the share of V2G-BEVs registered in a year

Ratio of V2G ready BEVs in the top ten sold BEVs in the country []

lt 10 gt= 10 gt= 20 gt= 40 gt= 60

Cost of Electro Mobility

EVC01 BEVICV retail price ratio

Describes the ratio of retail price between BEVs and their ICV counterparts (metric to be applied to small + mini cars medium cars and large + other cars)

Price of BEV price of ICV []

gt 50 10-20 0 lower lower

EVC02 BEVICV service cost ratio

Compares the average service cost between BEVs and ICVs (metric to be applied to small +

Average Price of BEV service average price of ICV service []

lt80 lt60 lt50 lt45 lt40


19

mini cars medium cars and large + other cars)

Govern-mental Legislation Incentives and Goals

EVG01 Fuel taxation

Describes the value of average fuel (dieselpetrolgas) taxation

Percentage of additional fuel taxation since the year 2000 []

lt 150 gt= 150 gt= 200 gt= 250 gt= 300

EVG02 Purchase incentive

The average price incentive when buying and registering a BEV

Amount of price incentive vs average purchase price of BEVs []

lt 10 (rising)

lt 15 (rising)

lt 20 lt 15 (falling)

lt 10 (falling)

EVG03 EV registrations

The country goals of BEVs registrations compared to overall car registrations


(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings

This parameter describes the ratio of CO2 savings compared to the year 2000

Metric tons of CO2 to be saved in current year vs metric tons CO2 to be saved in the year 2000 []

gt 25 gt 50 gt 60 gt 80 gt 80

III21a2 Parameter country level classification

For each country the level classification of each parameter is presented in the table below The respective collection of data can be found in section IX (Appendix)

Table 2 Country level classification (Electric Vehicles and Fleet)

Sub-Area ID Parameter Name GER

Level

ESP

Level

CZE

Level

Offer Supply of EVs EVO01 Ratio of BEVs 0 0 0

EVO02 Ratio of EVs registered bought in 2018 0 0 0


20

EV Sharing EVS01 Customer ratio 0 1 0

EVS02 Car-sharing Providers 1 1 0

EVS03 Ratio of BEVs in car-sharing 0 1 1

EV Technology EVT01 BEV driving range 2 1 2

EVT02 V2G ready EVs 1 0 3

Cost of Electro Mobility EVC01 BEVICV retail price ratio 0 0 0

EVC02 BEVICV service cost ratio 1 0 1

Governmental Legislation

Incentives and Goals

EVG01 Fuel taxation 0 3 0

EVG02 Purchase incentivation 1 4 3

EVG03 EV registrations 0 1 0

EVG04 CO2 savings 0 1 0

III21b Energy Supply amp Grid

The area of Energy Supply amp Grid covers the fields of electricity generation (eg electricity mix) electricity pricing (eg amount and dynamicity) and

electricity transportation (eg power grid infrastructure monitoring capabilities) All of them are covered from technology and legislation side However the analysis only provides a high-level view on the topics and thus the classification of laws and technologies include a scope for improvement The area of Energy Supply amp Grid is divided in four sub-areas which are described in the following section in more details

III21b1 Relevant parameters

In Table 2 the relevant sub-areas of the energy supply and grid area are shown They include ldquoTechnology and standardizationrdquo (ENT) ldquoPower grid

situationrdquo (ENG) ldquoEnergy Supplyrdquo (ENE) and ldquoLegislationrdquo (ENL) All sub-areas are further divided in different parameters which are on the one

hand representative for the usage of the ELECTRIFIC solution in the specific sub-area and on the other hand feasible to measure

Most of the metrics are quantitative metrics which can be calculated Some parameters are not so easy to quantify and thus have a qualitative metric as described in the corresponding table section


21

Table 3 Parameters Area Energy Supply amp Grid

Sub-Area ID Parameter Name

Description Metric

(Classification)

Level 0 Level 1 Level 2 Level 3 Level 4

Technology Standardization

ENT01 Grid usage behaviours data

BDEW profiles vs smart meter by comparison of data driven model vs real load

Accuracy of the load behaviour model

No profile

Standard load profiles


Regional adjusted profiles

Real meter data of gt 90

ENT02 Grid monitoring Monitoring grid related information on MVLV level

Rollout status of smart meters with a communication interface

0-20 20-40

40-60

60-80

80-100

ENT03 Reliable and secure communication

How is the communication to controllable consumption devices What security measures are taken

Qualitative metric Level 0 correspond to no special security measures or no publicly available documentation of such (security through obscurity) Level 1 corresponds to a separate Public-Key infrastructure installed between the metering device and the backend Level 2 corresponds to a separate communication network eg power line communication and dedicated fiber glass Level 3 include implemented access control of different market actors to the meter values Level 4 corresponds to the goal of the user being the administrator of the data (how can access the data)

Power grid

situation

ENG01 Distributed PV peak power in MVLV

Pressure of PV systems on the grid that might be compensated locally

PV peak power in watts per capita

0-100 100-200 200-300 300-400 400+

ENG02 Installed grid

infrastructure

Compare the amount and size of installed grid infrastructure to the amount of population

Qualitative metric

The length of power lines on HV MV and LV in kilometres per 1000 capita as well as the number of LV transformer per 1000 capita are evaluated to extract a first idea of the need for controlled charging stations


22

Energy Supply ENE01 Electricity price flexibility

Ratio of energy working price to total electricity price for normal households (3-4 Persons and 4000kWhyear)

Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15

ENE02 Amount of renewable energy

Optimization

potential on

renewable energy

production

Percentage renewable of yearly energy consumption

0-15 15-30 30-45 45-60 gt 60

Legislation ENL01 Allowed interaction in the distribution grid

Which stakeholder is able to control the controllable consumption devices of end-users (DSO TSO ES other)

Qualitative metric

The more stakeholder can control the consumption devices the more

open a system is On the other hand the more stakeholder more

coordination is required For ELECTRIFIC it needs to be determined if

which stakeholders could integrate grid related solutions of ELECTRIFIC

ENL02 Definition of a controllable consumption device

Definition in the law or service description at the DSOs

binary no yes

ENL03 Legal basis for dynamic pricing

Are there legal concepts for dynamic pricing or controllable consumption devices

binary no yes

III21b2 Parameter country level classification



23

Table 4 Country level classification (Energy Supply amp Grid)


Level

ESP

Level

CZE

Level


ENT01 Grid usage behaviours data 3 4 3

ENT02 Grid monitoring 0 4 0

ENT03 Reliable and secure communication 4 3 3

Power grid situation ENG01 Distributed PV peak power in MVLV 4 1 1

ENG02 Installed grid infrastructure 3 3 2

Energy Supply ENE01 Electricity price flexibility 3 2 2

ENE02 Amount of renewable energy 2 2 0


1 2 2


3 4 3

ENL03 Legal basis for dynamic pricing 0 3 0

III21c Charging Infrastructure [Johannes]

III21c1 Relevant parameters

The charging infrastructure covers various areas that are important for the successful implementation of Electrific Public or semi-public charging stations were considered relevant as they are the only ones offering charging possibilities for all drivers Comparability of the available charging stations in the countries under consideration (Germany Spain Czech Republic) is achieved through quantification technical and economic aspects as well as administrative factors In addition different levels of the individual criteria illustrate the possibilities of differentiation For example the efforts made by politicians to promote electromobility in the individual countries are clearly comparable The possibilities offered by the state of technology can provide an overview of progress in the individual countries


24

Table 5 Parameters Area Charging Infrastructure


Description Metric

(Classification)


Public Network CSP01 CS Coverage

Relation of CS to EV

Defines the CS-Coverage in Relation to

the EVs

No of CS in relation to EV

EV CS

50 EV CS 40 EV CS 30 EV CS 20 EV CS 10 EV CS

CSP02 CS Distribution

Outlook of the potential

of the countries

No Of CS population

lt50 CS per 10000 people

lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people

Public Network CSP03 Charging Rate

Average Charging

Rate (ldquoSum Charging

Power Speed in kW No of

CSrdquo) Differentiated into AC and

DC

Average charging rate as indicator for the

status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC

CS Intelligence CSI01 Smart vs Dumb CS

The steps to make a CS smart and

useful for the market Electrific

Defined by the features

Steps in the development of

charging stations

considering them smart

OCPP 12 OCPP 15 direct paymentA

d-hoc charging

OCPP 16 (fully

functional Reservatio

n is possible) PlugampChar

OCPP 20 automated inductive charging


25

and functions the OCPP-

Version gives to the CS

ge (ISO 15118)

Business CSB01 Impact of personal

offers

The possibilities of demand-

response and of personal

offers

The functionality to create user-oriented prices

static prices (per kWh)

prices per session idle fees

timelocation based prices

(within one network)

Event-based pricing

Reservations dynamic

prices

CSB02 Availability of easy

payment

The possibility of

easy payment at the CS for Ad-hoc-

Charging

The steps of possibilities of

payment

RFID-Card of own EMP

addition App

addition Roaming

Addition PayPal

Addition Credit Card

CSB03 Combined charging

technology

Public CS networks like

Roaming

Availability of Roaming networks

0 of CS in roaming networks




100 of CS in

roaming networks

CSB04 Incentivized charging options

Correlation with CSP and

DSO data

Importance of charging options

at CS

widely used fast

charging

widely used green

charging

widely used battery-friendly

charging

widely used load-

management

widely used v2g

Standardization CSS01 Hardware compatibility with EVs

Cable and Plug Type

Compatibility

Steps of standardization

No standardiza

tion in connectors

1 Connector

as minimum

Connectors are

standardised not all

CS

Connectors are

standardised many CS

Connectors are

standardised in all CS


26

standardised

implement them

implemented

implement them

Legislation and Government

CSL01 Combined legal

assessment

Existence of incentives

and penalties like towing

illegal parked cars at public

CS

Country specified a

number of CS per EV (true

false) Gas stations

have to have CS (true false) Penalty for

blocking parking spaces at CS (true false)

Incentivessubsidies to buyset up a CS (true

false)

0x true 1x true 2x true 3x true 4x true

CSL02 Subsidy in the

development of

charging infrastructur

e (Governme

nt and investment) to CS area

Total investment in

CS infrastructure economic

power of the country

Criteria level is based on

- the price of the CS

- the height of the Subsidy

- the purchasing power of

companies and people

No investment whatsoever

10 25 40 50 of the price of CS


27

III21c2 Parameter country level classification


Table 6 Country level classification (Charging Infrastructure)


Level

ESP

Level

CZE

Level

Public Network

CSP01 CS Coverage 4 4 4

CSP02 CS Distribution 0 0 0

CSP03 Charging Rate 3 2 NA

CS Intelligence CSI01 Smart vs Dumb CS 2 1 1

Business

CSB01 Impact of personal offers 2 1 1

CSB02 Availability of easy payment 4 3 1

CSB03 Combined charging technology 3 0 1

CSB04 Incentivized charging options 2 2 1

Standardization CSS01 Hardware compatibility with EVs 4 2 3


CSL01 Combined legal assessment 2 2 1

CSL02

Subsidy in the development of charging

infrastructure (Government and

investment)

3 3 2


28

III21d Consumers amp Society

The area ldquoConsumers amp Societyrdquo reflects the non-technical maturity of potential ELECTRIFIC markets This includes the attitude of potential EV users and customers regarding sustainability topics in general and electric mobility specifically Regarding the consumer the attractiveness of EVs and awareness of environmental change necessity is of importance Further we need to assess what costs and benefits the consumer is likely to accept regarding to mobility as well as the monetary power and trust in future technology Taken together this metric summarizes the willing- and preparedness of the individual and the (political) society towards EV technology

III21d1 Relevant parameters

In Table 7 the relevant parameters for the area Consumers amp Society are explained and categorized into four sub-areas (demand for e-mobility environmental awareness information level of users and approval of e-mobility)

Table 7 Parameters Area Consumers amp Society


Name Description

Metric


Demand for E-Mobility

COD01 Purchase intention

Covers the question of

how likely the next car will be an EV

[Likert 1-5]

(ELECTRIFIC survey) 1-18 18-26 26-34 34-42 42-5

COD02 Purchase

power

Based on the adjusted

gross disposable income of

households per capita

2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent

electromobility is seen as the

[Likert 1-5]



29

future of transportation

Environmental Awareness

COE01 Awareness of

CO2 emissions

Being aware of emissions produced by

eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)

Compiled from different questions about an

awareness of emissions and the connection with eg the

electricity mix increased consumption imports from

distant countries health impact

1-18 18-26 26-34 34-42 42-5

COE02 Knowledge

about climate change

Being aware of the effect of

humans on climate

[Likert 1-5]

(ELECTRIFIC survey)

Compiled from different questions

1-18 18-26 26-34 34-42 42-5

COE03 NEP scale

New Ecological Paradigm

(NEP) scale statements with Likert scale [1-5]

(ELECTRIFIC survey)

Pro-ecological behaviour

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30

COE04 Level of recycling activities

Recycling rate of municipal

waste Source lt14

14-27

27-41

41-54

gt54

Information level of users

COI01

User Knowledge

Gap EV Usage

Knowledge Gap Fear of

using EVs (perceived by the potential

user)

[Likert 1-5]

(ELECTRIFIC survey) 42-5 34-42 26-34

18-26

1-18

COI02

User Knowledge

Gap EV Technology

Knowledge Gap too high-

tech (perceived by the potential

user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18

Approval of e-mobility

COA01 Acceptance

of constraints

Acceptance of constraints (charging time cost range etc)

[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18

COA02 Attractiveness of EVs

General attractiveness

of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5

COA03 Attractiveness of EV

technology

Attractiveness of EV

technology (acceleration

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)

COA04 Prestige Prestige of driving EVs

(by being early adopter

perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5

III21d2 Parameter country level classification

For each country the level classification of each parameter is presented in the table below The respective collection of data can be found in section IX (Appendix) and is mainly based on the conducted survey across the three countries

Table 8 Country level classification (Consumers amp Society)


Level

ESP

Level

CZE

Level


COD01 Purchasing intention 1 2 1

COD02 Purchasing Power 3 2 2

COD03 Future viability of EV 2 3 2


COE01 Awareness of CO2 emissions 3 3 2

COE02 Knowledge about climate change 3 3 2

COE03 NEP scale 4 3 3

COE04 Level of recycling activities 4 2 2

COI01 User Knowledge Gap EV Usage 3 2 2


32


COI02 User Knowledge Gap EV Technology 2 2 2


COA01 Acceptance of constraints 2 2 1

COA02 Attractiveness of EVs 2 3 2

COA03 Attractiveness of EV technology 2 3 2

COA04 Prestige 2 2 2

III22 Product-Based Weighting of Areas

For each of the products (ADAS App Smart Charger Charging Scheduler) the major preconditions are considered in order to justify different weights for the weighted average of each area For the three main products an individual weighting is determined to reflect the unique requirements of the products in the markets

III22a Weightings for ADAS App

III22a1 ADAS ndash Weightings Area ldquoEV amp Fleetrdquo


Name

Justification for Weight (Preconditions)

Weight

Offer Supply of EVs

EVO01 Ratio of BEVs Number of potential

customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing

EVS01 Customer ratio - 0

EVS02 Car-sharing

Providers

- 0


- 0

EV Technology EVT01 BEV driving range 0

EVT02 V2G ready EVs - 0



Number of potential customers

005



005

Governmental Legislation Incentives and Goals

EVG01 Fuel taxation Interest in EVs 01

EVG02 Purchase incentivation


01

EVG03 EV registrations Number of potential customers

02

EVG04 CO2 savings Interest in EVs 01

III22a2 ADAS ndash Weightings Area ldquoEnergy amp Grid


Name


Weight


ENT01 Grid usage

behaviours data

- 0


34

ENT02 Grid monitoring Grid-friendly and

coordinated charging

potential

03


Secure charging 01

Power grid situation

ENG01 Distributed PV

peak power in

MVLV

Green and grid-friendly

charging potential

01

ENG02 Installed grid infrastructure

Need for coordinated charging

01

Energy Supply

ENE01 Electricity price flexibility

Incentives 01


Green charging potential 02

Legislation

ENL01 Allowed interaction in the distribution grid

- 0


- 0


Incentives 01

III22a3 ADAS ndash Area ldquoCharging Infrastructurerdquo


Name


Weight

Public Network

CSP01 CS Coverage Flexibility in suggestions 01

CSP02 CS Distribution Availability of charging

options

01

CSP03 Charging Rate - 0

CS Intelligence CSI01 Smart vs Dumb

CS

Additional functionality 04

Business

CSB01 Impact of personal offers

Incentives 005

CSB02 Availability of easy payment

Attractiveness of public charging

01

CSB03 Combined charging technology

Availability of charging options

005


Incentives 005


35

Standardization

CSS01 Hardware compatibility with EVs


005


CSL01 Combined legal assessment


005

CSL02 Subsidy in the development of charging infrastructure (Government and investment) to CS area


005

III22a4 ADAS ndash Area ldquoConsumers amp Societyrdquo


Name

Justification for Weight

(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01

COD02 Purchase power Amount of potential

users

005

COD03 Future viability of EV

Amount of potential users

01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005

COE02 Knowledge about climate change


005

COE03 NEP scale


01


- 0


COI01 User Knowledge Gap EV Usage

App functionality 01

COI02 User Knowledge Gap EV Technology


005


COA01 Acceptance of constraints




01

COA03 Attractiveness of EV technology


01


36

COA04 Prestige Amount of potential users

01

III22b Smart Charger

III22b1 Smart Charger ndash Area ldquoEV amp Fleetrdquo


Name


Weight

Offer Supply of EVs

EVO01 Ratio of BEVs Size of controllable

Power

05

EVO02

Ratio of EVs

registered bought

in 2018

Size of controllable

Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0

EV Technology EVT01 BEV driving range

Impact on charging behaviour and thus on the reaction of the Smart Charger

03




- 0


- 0


EVG01 Fuel taxation - 0


- 0

EVG03 EV registrations - 0

EVG04 CO2 savings - 0

III22b2 Smart Charger ndash Area ldquoEnergy amp Grid


Name


Weight

Technology Standarization

ENT01 Grid usage

behaviours data

- 0

ENT02 Grid monitoring Monitoring power quality

and grid capacity

03


37


Transmission of measurement data and control signal

015



peak power in

MVLV

Evaluates the need for a

Smart Charger

015


Evaluates the need for a Smart Charger

015

Energy Supply


Evaluates the possible size of rewards for grid friendly actions

005


To optimize on 005

Legislation


Legal framework for Smart Charger

005



005



005

III22b3 Smart Charger ndash Area ldquoCharging Infrastructurerdquo


Name


Weight

Public Network

CSP01 CS Coverage Impact location 01

CSP02 CS Distribution - 0

CSP03 Charging Rate Amount of power the Smart Charger could control

01


CS Hard requirement for a Smart Charger

07

Business


- 0


- 0


- 0


One option for rewarding Smart Charger actions

01


38

Standardization


- 0



- 0


- 0

III22b4 Smart Charger ndash Area ldquoConsumers amp Societyrdquo


Name


Weight


COD01 Purchase intention - 0

COD02 Purchase power - 0


- 0


COE01 Awareness of CO2

emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0



Smart Charger may increase charging time

10


- 0


- 0

COA04 Prestige - 0


39

III22c Charging Scheduler

III22c1 Charging Scheduler ndash Area ldquoEV amp Fleetrdquo


Name


Weight

Offer Supply of EVs

EVO01 Ratio of BEVs Possibility for country fleet optimization

015

EVO02 Ratio of EVs registered bought in 2018

Possibility for country fleet optimization

01

EV Sharing

EVS01 Customer ratio Requirement for tours 005


Potential customers 01


Size of optimisation problems

015

EV Technology

EVT01 BEV driving range Value of optimisation degree

015

EVT02 V2G ready EVs Value of optimisation degree

01



Relevant for amortisation 005


- 0


EVG01 Fuel taxation Relevant for amortisation 01


Possibility for country fleet optimisation

005



III22c2 Charging Scheduler ndash Area ldquoEnergy amp Grid


Name


Weight



Accurate grid load forecast as prerequisite

01

ENT02 Grid monitoring Accurate grid load forecast as prerequisite

004


Stability of the system 004



Important for optimisation 018


40


Important constraint 018

Energy Supply


Important constraint and for optimisation

018



018

Legislation


- 0

ENL02

Definition of a controllable consumption device

- 0


Prerequisite for price optimisation

01

III22c3 Charging Scheduler ndash Area ldquoCharging Infrastructurerdquo


Name


Weight

Public Network

CSP01 CS Coverage Size of charging infrastructure

005

CSP02 CS Distribution Size of charging infrastructure

005

CSP03 Charging Rate Important constraint 02

CS Intelligence

CSI01 Smart vs Dumb CS


Business


Constraint for optimisation

005


- 0


Important for schedule creation including public CS

005


Price optimisation 01

Standardization





- 0

CSL02 Subsidy in the development of charging infrastructure (Government and

Strategic infrastructure planning

01


41

investment) to CS area

III22c4 Charging Scheduler ndash Area ldquoConsumers amp Societyrdquo


Name


Weight


COD01 Purchase intention EFO goal 006

COD02 Purchase power

Important EFO goals

01


01


COE01 Awareness of CO2 emissions

01


EFO goal 006

COE03 NEP scale EFO goal 006


- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01

COA04 Prestige EFO goal 006

III23 Results Market Maturity for ELECTRIFIC products in Germany Spain and Czech Republic

The following chapter combines the parameter country classification levels and weightings from the previous chapters For each area a weighted average is calculated leading to the maturity level for the product in this area eg

MLArea = EV T01 times 04 + EV T02 times 01 + EV T03 times 01 + EV T04 times 04

The result is the maturity of the markets in Germany Spain and the Czech Republic for the respective products ADAS Smart Charger and Charging Scheduler For each country and for each product a graphical model is created that shows the maturity of the market in the areas Electric Vehicles amp Fleet Energy Supply amp Grid Charging Infrastructure and Consumer amp Society

The results show that the conditions on the German market are well under way The maturity levels reach 1 or even 2 in all areas but Electric Vehicles amp Fleet In Spain the market is similar mature only here there is additional pent-up demand in the area Charging Infrastructure The Czech Republic has pent-up demand in all areas for all products only reaching level 0 or 1 To


42

sum it up all three states need improvement in all areas Even Spain and Germany do not reach the highest level 4

III23a ADAS ndash Market Maturity in Germany Spain Czech Republic

The maturity of the German market reaches for the ADAS App in the area Energy Supply amp Grid level 1 The conditions on the market regarding the Areas Charging Infrastructure and Consumer amp Society have a maturity level of 2

For the ADAS App the maturity of the Spanish market is in the area Energy Supply amp Grid with a level of 2 slightly better than the German market The Charging Infrastructure area on the other hand with level 1 is less suitable for the app than the German market The maturity of the Consumer and Society area roughly corresponds to the German market

The maturity of the market in the Czech Republic is rather different and reaches level 0 in all areas for the ADAS App

Figure 6 Aggregated Market Maturity levels for the ADAS App in Germany

Figure 7 Aggregated Market Maturity levels for the ADAS App in Spain


43

Figure 8 Aggregated Market Maturity levels for the ADAS App in the Czech Republic

III23b Smart Charger ndash Market Maturity in Germany Spain

Czech Republic

Looking at the maturity of the markets for the Smart Charger the result is as follows Disregarding the Electric Vehicles and Fleets area the market environment in Germany reaches a maturity level of 2 Spain is very similar to the German market In addition to the weakness in Electric Vehicles and Fleets Spain is also reaching level 2 in Consumer amp Society and Energy Supply amp Grid However at level 1 the Charging Infrastructure in Spain is less mature for the smart charger than in Germany

As with the ADAS App the market for the Smart Charger in the Czech Republic is not mature enough and is consistently at level 0 Only the Energy Supply amp Grid segment seems to be more mature than the rest and reaches level 1

Figure 9 Aggregated Market Maturity levels for the Smart Charger in Germany


44

Figure 10 Aggregated Market Maturity levels for the Smart Charger in Spain

Figure 11 Aggregated Market Maturity levels for the Smart Charger in the Czech Republic

III23c Charging Scheduler ndash Market Maturity in Germany Spain

Czech Republic

The maturity levels in Germany Spain and the Czech Republic for the Charging Scheduler are quite like the maturity levels for the Smart Charger Spanish and German markets are quite mature reaching level 2 in all areas despite the Area Electric Vehicles and Fleets with level 0 Spain again has only a market maturity level of 1 in the Charging Infrastructure area The Czech Republic reaches level 0 in the areas Electric Vehicles and Fleets and Charging Infrastructure The Energy Supply amp Grids and Consumer amp Society areas however have for the Czech market above-average maturity for the Charging Scheduler with level 1

Figure 12 Aggregated Market Maturity levels for the Charging Scheduler in Germany


45

Figure 13 Aggregated Market Maturity levels for the Charging Scheduler in Spain

Figure 14 Aggregated Market Maturity levels for the Charging Scheduler in Czech Republic


The E-MMM is built on survey and desktop data in some case on expert questionnaires It

gives an idea about where investments into the ELECTRIFIC components will encounter

market conditions that are apt to receive and distribute this idea In addition to that a more

formal approach will show an assessment of the impact of the said components on an

economic and technical level for specific scenarios and use cases as general statements are

not possible This impact analysis is carried through in the form of a detailed simulation based

on data from a small distribution grid part in Vilshofen Bavaria for the ADAS It is also carried

through for the smart charging solution and as a small scale calculation based on experimental

data from the TMB trial for the charging scheduler

III31 Mobility App

The analysis aims to determine the effects of the ADAS App in an LV grid with different EV market penetration rates Therefore understanding the consequences on the grid like voltage drop and transformer utilization of the charging activity of one or more electric cars at the same time in this specific part of a low voltage grid and how the ADAS App can limit these effects

As already mentioned in Deliverable D94 a general impact analysis is infeasible The low-voltage networks used by the CS are very heterogeneous not only across national borders but also within the catchment area of a DSO due to different old structures and geographical circumstances Furthermore the different network expansion policies between the EU states counteract a general impact analysis Accordingly a simulation-based impact analysis based


46

on a small distribution grid in the trial area of the ELECTRIFIC where monitoring devices were installed to create a realistic simulation

Figure 15 Vilshofen

The simulation that was operated rebuilds a partial area of the South Bavarian town Vilshofen which is a trial area for the ELECTRIFIC project The area is about 0125 km2 large and spans between the towns train station and river Danube as can be seen from in figure 15 In the mapped area there are 22 households and 21 industriesbusinesses of interest to the analysis 383 people are estimated to work in Vilshofen only 24 of which are estimated to live directly in Vilshofen While the transformer is located next to the train station the charging stations can be found close to it The already installed charging stations (in red cycles) are placed near to the transformer where the charging stations from Bayernwerk are located at the parking lot and the E-WALD charging stations are located at the south-east side of the transformer in direction to the train station All in all Vilshofen has five CS and an additional wallbox at Bayerwerkrsquos customer centre

The grid is heterogeneous consisting of a variety of aluminium and copper cables There is one transformer in the grid The peak load of this transformer without EVs is at 200kVA and has a nominal capacity of 400kVA The nominal voltage is 230400V and the nominal frequency is 50Hz

The power grid of Vilshofen was simulated with the power simulation tool PowerFactory from DIgSILENT The tool provides Quasi-Dynamic Simulation which allows for a time dynamic simulation and analysis of generation transmission distribution and industrial systems settings It calculates maximum and minimum voltage levels of different assets based on given load profiles From these results problems can be derived and it can be inferred whether a grid could bear a specific EV penetration rate

III31a Low-voltage grid constraints

The following three thresholds must be considered when simulating the grid of Vilshofen the transformer load the worst-case voltage in the grid and cable loads

Transformer load The transformerrsquos nominal capacity is 400kVA Exceeding this

value would overheat the transformer and could cause damage

Worst-case voltage According to EN50160 the 10min mean voltage values must not

deviate more than

10 from the nominal voltage ie the voltage magnitude must lie between 09 and 11pu [Euro10] Due to the fixed ratio in the low voltage transformer only 5 are used for the low voltage grid as best practice in low voltage grid planning


47

Cable loads Loads on all cables should not be above 100 of their capacity dor a

longer time

III31b Enhanced charging station infrastructure

The current CS situation was extended in the simulation undertaken previously in the ELECTRIFIC project (see deliverable D94) One 15kW wallbox was added for each business so that employees can charge their EVs Furthermore each private home was equipped with one CS

Earlier simulation runs (see deliverable D94) showed that with increasing EV penetration rates problems arise in terms of CS availability As a result of this the clash rates increased Clash means that a driver who wants to charge his vehicle encounters an occupied CS and thus is prevented from charging at this CS Clashes are always given as a percentage of charging attempts relative to the sum of all charging processes and attempts Since these clashes increased significantly with increasing EV penetration rates the actual demand on the grid could not be portrayed In order to solve this problem further CSs were added Each private home was equipped with a CS Further public CSs were added Adding these CSs reduces the clashes to no more than 10 for all charging options at home at work and in public The threshold here is 10 because during the creation of the charging profiles (CPs) customers are randomly distributed to the charging stations until all are assigned In this process clashes cannot be precluded In addition the effects of clashes on the network are not estimated high enough to justify a very unrealistic number of CSs

III31c Load profiles EV energy demand and driving and

charging behaviour

A vital element of the simulation are load profiles and more specifically CPs Load profiles of households and businesses were derived in the ELECTRIFIC project using measured data from the actual low-voltage grid in Vilshofen (Bavaria) and data from the German Federation of Energy and Water (BDEW) [Bund17]

In order to derive EV energy demands data from the German Mobility Panel (GMP) and the Kraftfahrtbundesamt (KBA) on car model properties and quantities were analysed [Kiti12 Kraf00] By analysing the data of newly registered vehicles in the first half of 2018 the specific EV mix for Germany is calculated This approximated EV mix has an average energy consumption of 166 kW per 100 km and an average battery capacity of 372 kWh Derived from these numbers an average reach of 2241km is assumed ((100km166kW)372kWh) The data obtained in this way serve as the foundation for the simulation

The driving behaviour was deduced from data of the German Mobility Panel In their surveys from 201516 and 201817 more than 1000 persons participated and contain in sum more than 50000 recorded trips It contains information about routes the purpose of the trip means of transport and total distance travelled Only trips conducted by car and in regions similar to Vilshofen (by the number of inhabitants) were regarded to get relevant driving profiles

From these driving behaviours specific charging profiles were created In order to do so a fictional battery State of Charge (SoC) is introduced The SoC is based on the EV energy demand for the travelled distance and the maximum battery capacity Charging profiles are built up by timestamps that represent at which time and in what quantity electricity is conducted The trip reason stated by the driver in the GMP survey is used as the best guess for the destination which will be reached at the end of the trip These destinations are then associated with the presence of a source for charging So every arrival time that is associated with a charging source will trigger a power demand for a time interval determined by the duration in between trips (stay at the destination) or maximum SoC

Different driving destinations (home work shopping and running errands leisure and hobbies) were derived from individualsrsquo trip reasons and then mapped to the different CP categories (Charging at Home (CH) at work (CW) at public charging stations (CPCS)) The trip purposes of ldquoWorkrdquo and ldquoHomerdquo are linked to the potential charging sources of CH


48

and CW respectively For CPCS the most reasonable trip purpose of the association to charging in the dataset is chosen to be ldquoShopping running errandsrdquo and ldquoLeisure hobbyldquo Also the far-distance mid-way charging activities are added to CPCS For the remaining miscellaneous trip purposes there is assumed to be no option of charging available at the potential trip destination To reflect the properties of different charging points samples with different power levels ranging from 35kW for CH to 15kW for wall boxes at workplaces to 11kW 22kW and 50kW at PCSs were created The linearly assumed charging speed differs among the sources depending on the power drawn Charging profiles can be determined for each individual during the week for all three potential sources Arriving at a charging source the individual is assumed to trigger the charging process which will continue throughout an estimated total charging time (ldquocharging intervalrdquo) At departure time or when the SoC is 100 the charging current is reduced to zero until the next charging activity of this individual at this type of source

It is assumed that driving always needs to be finished with at least 10 SoC remaining If this is not possible charging at a PCS in the middle of the trip is simulated In addition it was assumed that due to convenience and cost reasons CPCS only takes place if the SoC would undercut a 30 threshold at the end of the next trip

For the simulation of different EV penetration rates the charging profiles are used to allocate a sample of to the different CS The number of charging is determined by the penetration rate and therefore affects the load profile of each CS and the grid load

Since the penetration rate of 100 is the most extreme level at which ADAS has to be measured the baseline without ADAS is also simulated with this rate Three different scenarios

were simulated as a benchmark with the first one (baseline scenario) including all CS categories the second one excluding CW due to the assumption that CH might be prioritized for reasons such as local photovoltaic installations (focus home scenario) and the third one excluding CH (focus work scenario) Reasons for the third scenario might be the lack of capabilities to charge at home high prices of private electricity or employers providing free charging for their employees An overview of the three simulated scenarios without ADAS can be found in Table 9

Table 9 Simulation Scenarios without ADAS

ADAS Scenario Description

Without ADAS

Scenario A ndash baseline Including CH CW

CPCS

Scenario B ndash focus home Including CH

CPCS but not CW

Scenario C ndash focus work Including CW

CPCS but not CH

To assess the impact of the ADAS app different degrees of adherence are assumed based on previous ELECTRIFIC project work (see D63 and D84) Lab trials found that 85 of the participants exposed to incentivization chose the green route suggested by the ADAS Therefore assuming incentivization works 85 of the charging processes will be shifted Assuming there is no incentivization only 59 of the charging processes will be shifted 59 is the share of people in the control group of the ADAS lab trial who chose a green route over a fast route without being incentivized at all These people were only given the choice between green and fast route without providing further information eg on how much energy is saved by choosing the green route For these simulation runs a 100 EV penetration is assumed since this is the most extreme case that ADAS can be confronted with and which it should be able to handle After the main simulation runs a sensitivity analysis is performed where both the degree of adherence to ADAS and the EV penetration rate get altered Table 10 provides an overview of all simulation scenarios


49

Table 10 Simulation Scenarios with ADAS and Sensitivity Analysis


With ADAS

Scenario 1 ndash focus work 100

adherence to ADAS CW charging processes do not get

shifted to CH

Scenario 2 ndash focus home 100

adherence to ADAS CW charging processes do get

shifted to CH

Scenario 3 ndash no incentivization

59 adherence to ADAS

59 adherence to ADAS according to control group in ADAS lab trial without

incentivization

Scenario 4 ndash incentivization 85 adherence to ADAS

85 adherence to ADAS according to treatment group in ADAS lab trial who

received a symbolic incentivization

Sensitivity Analysis

Scenario 5 ndash 15 adherence to ADAS

Only 15 adherence to ADAS according to Eco-button trial (social

norm present but no default for further information see D63 amp D84)

Scenario 6 ndash65 EV

penetration and no ADAS (baseline)

As baseline scenario 1 (without ADAS) but with lower EV penetration

rate

Scenario 7 ndash65 EV penetration and 85 adherence

to ADAS

As scenario 4 but with lower EV penetration rate (no scenario with

59 explanation will follow in 633)

III31d Results

Simulations runs are conducted with an assumed 100 EV penetration rate since this is the most extreme case that ADAS can be confronted with Furthermore the CS infrastructure is modelled in a way such that this penetration rate can be handled in terms of having enough CSs to meet demand Within a sensitivity analysis further simulations runs are conducted afterwards with different degrees of adherence to ADAS and a lower EV penetration rate

III31d1 Simulation runs without ADAS

Running the first scenario A (CW CH CPDS 100 EV penetration rate) without ADAS reveals three problem areas in the grid the transformer and the two weakest cables of the grid

The peak loading on the transformer is 522kVA which significantly exceeds the nominal capacity of 400kVA (Figure 16) The worst-case voltage drop in the grid is 13 which is a deviation of more than the allowed 5 PowerFactory the points in the grid where this worst-case voltage drop happens These are caused by one specific cable which is beyond capacity since the electricity flowing to the loads behind the cable distribution box run through it


50

Figure 16 Transformer loading in scenario A (baseline) without ADAS The blue line marks the transformerrsquos nominal capacity of 400kVA

Simulating scenario B (focus home scenario ie no CW) results in a less stressed grid Transformer loading is within its capacity The peak loading of line 28 was still at 122 since only public CSs are connected to this line which did not change in this scenario The peak loading of line 24 decreases significantly to 106 This also leads to a lower worst-case voltage drop of only 6 This is still more than the nominal capacity but it is significantly less than in scenario A Also the capacity is exceeded for a short time only which renders a replacement unnecessary

The result of scenario B already indicates that scenario C (focus work scenario) without CH and more CW would result in a worse situation for the local grid since not only commuters will charge more at work but also local inhabitants of Vilshofen will charge more at work and at PCSs Running this simulation scenario confirms this assumption The transformer loading peaks at 685 kVA the worst voltage drop in the grid is 17 and lines 24 and 28 have peak loads of 283 and 176 respectively Scenario 3 is thus the worst for the grid in terms of exceeding thresholds yet it would result in the same grid enhancement costs than scenario A

From these scenarios it can be derived that if ADAS shifted loads from work locations to other locations the LV grid might be better off This however can only be regarded as useful if we assume that the charging demand shifted away from Vilshofen does not cause grid issues elsewhere

III31d2 Simulation runs with ADAS

Scenario 1 lsquofocus workrsquo implies that no charging processes are shifted location-wise from work to home They stay at work and are only shifted timewise Furthermore we assume a 100 adherence to ADAS in this scenario

Looking at the transformer loading (Figure 17) reveals the positive impact of ADAS All peaks remain below the transformerrsquos nominal capacity of 400kVA which is represented by the blue line So no transformer enhancement would be necessary The worst-case voltage drops by 15 exceeding the 5 threshold This voltage drop is caused by line 24 the loading of which is depicted in Figure 18 It is visible that the loading of this cable is beyond 100 capacity during working days

The time-wise shift can be seen in the graph A small peek precedes each large peak (see blue circle) The remaining significant peaks can be explained by the maximum possible temporal shift which is based on the average parking time for this location This is not


51

enough time to shift the charging processes into the valleys Last line 28 shows the positive impact of ADAS since no loading is above 100 capacity which means the cable would not need to be replaced in contrast to the scenario without ADAS

Figure 17 Transformer loading in scenario 1 with 100 adherence to ADAS

Figure 18 Line 24 loading in scenario 1 with 100 adherence to ADAS

In scenario 2 (focus home) the transformer is again within its capacity and slightly lower than in scenario 1 At peak times charging processes are shifted away from CW having a positive effect The cable is now only slightly over capacity and would not need to be replaced Besides the threshold for the voltage drop can be maintained in this way However shifting these charging processes to CH affects another location in the LV grid and now stresses another cable (line 15b) However the line is still within its capacity and would not need to be replaced This scenario showed that if all EV users adhered to ADAS suggestions no grid enhancements would be necessary Unfortunately a 100 adherence to ADAS suggestions is an unrealistic assumption and the following scenarios will show the effect on the grid for differing degrees of adherence to ADAS


52

Scenario 3 builds on scenario 2 with the difference that the degree of adherence to ADAS is not 100 but only 59 The 59 adherence represents the degree of adherence to ADAS without any incentivization of the user as found in the lab trial For the simulation this means shifting only 59 of the charging processes that fall into peak times Even with only 59 adherence the transformer loading is not above capacity Evaluating the loading of line 24 shows the effect of a lower degree of adherence to ADAS While the cable is within its capacity in scenario 2 with 100 adherence to ADAS it is beyond capacity once per working day in scenario 3 The other lines are not beyond capacity Overall scenario 3 suggests that having ADAS in place but not incentivizing users to follow ADAS suggestions would result in rediced grid enhancement costs These are explained in more detail in section III31d3 At the same time one has to bear in mind where the 59 adherence to ADAS came from a control group in a lab trial which can be considered a somewhat artificial setting Thus 59 might be too optimistic Consequently scenario 5 will present simulation results with a lower degree of adherence

Scenario 4 simulates 85 adherence to ADAS The value 85 stems from the ADAS lab trial where the participants received an incentive following ADAS suggestions and chose the green route Transformer loading in this scenario is slightly over capacity on one day of a week which is still viable for the transformer This deviation from scenario 3 can be explained by how the shifting of the charging processes is implemented This is because the function determining the new charging time has a particular random nature Analysing line 24 reveals results as expected A cable is beyond capacity several times for a long enough time to blow the fuse Accordingly the scenario can be ranked between Scenario 2 (100 adherence) and scenario 3 (59 adherence without any incentives) This reveals the importance of right incentivization If the incentives were created in a way such that adherence would increase only a slight bit further a decent amount of money could be saved in this specific LV grid

Scenario 5 considers that a degree of adherence to ADAS of 59 (scenario 3) without any form of incentivization might be too optimistic and was only achieved through the artificial lab setting Outcomes of the eco-button trial are thus used to create the rationale for scenario 5 In this trial social norm incentivization (but no further incentivization) led to only 15 of the treatment group deciding for the desired option the activation of the eco-button Running the simulation one particularly pleasing result is that the transformer is never beyond capacity even for such a small degree of adherence to ADAS

All scenarios run so far are based on a 100 EV penetration rate While this shows the most extreme situation which ADAS should be able to handle it is very futuristic Therefore a less extreme situation shall be run for scenario 6 assuming a 65 EV penetration rate the mean value of different studies To have a baseline to compare to this scenario was simulated with no ADAS in place Neither the transformer nor line 28 exceed critical values when running the simulation without ADAS Line 24 however is beyond capacity at each working day even for a 65 EV penetration with the highest peak amounting to 176 of the cablersquos capacity This results in a worst-case voltage drop in the LV grid of 13


53

Figure 19 Line 24 loading in scenario 6 with 65 EV penetration and no ADAS

Since line 24 is the only problem area in the Vilshofen grid in scenario 6 without ADAS only this cable is of interest The loading of this cable in a simulation run with ADAS is depicted in Figure 20 While the effect of ADAS is clearly visible compared to Figure 19 the peak loadings on Monday and Friday are still critical It cannot be said with certainty whether these loadings would be too severe for the cable Whether they would be placed on the cable depends on the fuse through which this cable is protected Unfortunately we do not know what kind of fuse is installed at this cable and thus cannot say whether it would interrupt the current

Figure 20 Line 24 loading in scenario 7 with 65 EV penetration and 85 adherence to ADAS

III31d3 Cost Analysis

The simulation scenarios conclusively demonstrate that the impact of intelligent solutions like ADAS depends on human adherence to system recommendations While a 100 adherence to ADAS would render all grid enhancements unnecessary in this specific case an adherence of 59 (ADAS without any incentivization) would still result in necessary grid enhancement costs of around 12000euro In the above scenario 21000euro could be saved in the latter only 9000euro On the positive side even for a degree of adherence to ADAS as low as 15 (incentivized by social norms) the grid enhancement costs would not be higher than 12000euro in this specific LV grid which is the replacement of only one cable (line 24) Thus introducing


54

ADAS would still reduce the cost by 9000euro compared to scenario A (baseline) without ADAS Furthermore the effect of incentivization to increase the impact of ADAS becomes apparent In a scenario where a 65 EV penetration is assumed the 85 adherence through ADAS only slightly failed to render all grid enhancement costs unnecessary Improving incentivization only a bit further would thus most likely lead to a situation in which no conventional grid enhancements would be necessary

III31e Implications

III31e1 Theoretical Implications

The simulation runs and the sensitivity analysis shows the impact of differing degrees of adherence to ADAS This suggests further exploration of the effects of various incentivization techniques in order to make assumptions on degrees of adherence as realistic as possible Further incentivization techniques (defaults social norms) should be assessed for the specific ADAS use case and real-world experiments should follow lab trials This also includes the combination of non-financial (symbolic) and financial incentives which might lead to the highest possible degrees of adherence First experiments were carried out on these issues as specified in D63 and D84

III31e2 Practical Implications

The impact becomes evident when assessing the cost of conventional grid enhancements that would be necessary without an intelligent system like ADAS This simulation was just for one small LV grid Due to the heterogeneity of LV grids the economic costs cannot simply be scaled up to a whole state or even nation However the outcomes of this study already indicate that the cost of grid enhancements would be enormous on a nation-wide level Since it is shown that the grid issues can significantly be decreased by implementing intelligent systems like ADAS and ensuring adherence to system recommendations through behaviour steering it seems highly beneficial and economically reasonable to invest in intelligent systems to buffer grid issues The simulation has shown that the degree of adherence of individuals to recommendations of intelligent systems does have an impact when trying to buffer the load in LV grids It can consequently be recommended to not only focus on the technical solutions themselves but also on behaviour steering and charging decisions From a financial perspective it might be reasonable to elaborate on whether financial incentives would have a more significant impact than non-financial incentives and whether this effect is worth the financial effort

Furthermore the importance of carefully planned grid enhancements and designing charging infrastructures becomes apparent The problem of line 28 for example would not have happened if less PCSs had been placed behind this cable Also enhancing line 24 which might need to be enhanced in any case would imply that more CSs could be placed behind this cable once it is enhanced accordingly Thus on the one hand current LV grid infrastructure could dictate to a certain degree the locations in a LV grid where CSs should or should not be placed At the same time if grid enhancements are inevitable then enhancing a grid at one specific location would enable the installation of many CSs at this newly enhanced spot

III32 Smart Charging Solution

The possibility of load control of EV charging with the help of the Forecast Controller (proactive) and the Smart Charger (reactive) offers the potential for an alternative to regular grid enhancement In particular the two main grid constraints of voltage drop and transformer loading can be addressed with a dynamic reaction of charging operations This software-based solution has the potential to be more economical than the otherwise necessary capacity provision through grid enhancement This also includes cases in which a capacity expansion of the distribution grid cannot be realized or can only be realized by incurring high costs


55

In the considered region of Germany it needs to be distinguished between smaller grid participants such as households and larger grid participants like businesses and industry concerning the costs of grid connection For smaller grid connections there is a standard fee for each grid participants to pay to receive an initial grid connection In the German grid the associated maximum grid connection capacity is 30 kW In addition the grid participants incur additional costs for each additional kW of connected load 50 of which are paid by the grid participants the rest by the DSO and thus all participants via the grid usage fee The remaining costs are allocated to all grid participants via the grid fees This means that the necessary grid enhancement is shared with the beneficiaries Although the distribution grid operator is regulated and the costs incurred in the distribution grid are apportioned to all grid participants the incentive regulation also motivates the grid operator to forego conventional grid expansion through economic grid modernization measures such as the use of an intelligent charging algorithm For the sake of simplicity we compare the total grid expansion costs of traditional grid enhancement with the total costs of a software-based solution

A calculation of the costs for grid expansion due to e-mobility for the whole grid infrastructure is out of the scope of this report and was already done by other projectsstudies The focus is on some specific scenarios in which the usage of a software-based solution would make sense from the technical viewpoint of a grid operator The costs of some of these scenarios are later compared to highlight the economical viewpoint As the main focus of the ELECTRIFIC smart charging solution of this project is set to the low voltage grid where the charging stations are connected also the scenarios focus on low voltage grids

III32a Scenario Descriptions

In cooperation with grid planners from Bayernwerk scenarios with a potentially meaningful usage of the Smart Charging solution are evaluated below

Older existing grids When setting up new grid areas a grid planning is done with foresight

as far as possible in view of the changes to be expected in future grid load However this can only be economically implemented for a certain period Existing grids are grids in which infrastructure planning and construction projects have been completely finalized and are based on the underlying planning Older existing grids from the 1980s or earlier were planned in a period in which the infrastructure change due to renewable energies or electro mobility could not yet be foreseen and was therefore not considered Due to the possible transformer and cable limitations grid enhancement might be necessary sooner or later However this kind of grid enhancement is usually very costly if cables need to be replaced due to the underground cables mostly installed in low-voltage grids or the usage if expensive on-load-tap-changers at the transformer station to stabilize the voltage level The costs result not only from the pure material costs but mainly from the necessary excavation and service of the external service providers

Older existing grids need to be expanded for multiple reasons like renewable energy system integration (eg new PV systems) or the spread of e-mobility in form of charging EVs at home Control strategies eg ELECTRIFIC smart charging solution can be applied selectively and are able to ensure that neither the supply cables nor the transformer are overloaded This scenario is widely spread and will thus be further examined in the following

Special types of transformers Local transformers in low-voltage grids are divided into three

categories see Figure 21 Building stations compact stations and pole-top stations Building stations are accessible and managed from inside (also including tower stations) Compact stations are not accessible by persons and are managed from the outside of the cabin and pole-top stations are mounted on overhead line masts and also managed from the outside In the case of building stations and compact stations the limiting factors are the cooling capacity for heat dissipation and the ability to dissipate pressure increases in the event of an arc fault1 If sufficient space is available transformers of larger performance classes can be installed if

1 An arc fault is a high power discharge of electricity between two or more conductors


56

an upgrade is required For pole top stations the weight of the transformer is the limited factor which is why these systems are generally operated with a maximum of 250 kVA Pole top stations were usually used in rural areas and the individual grid participants usually have their own supply line (eg farmers)

Transformer building within a

tower

Transformer compact station

Pole-top transformer

httpsdewikipediaorgwikiMasttransfor

matormediaDateiMaststation_imgp7806jpg

Figure 21 Different types of transformer stations

Pole-top stations generally supply only a few widespread consumers (eg in rural areas) so that transformer overload due to electro mobility is less likely At the same time the use of such stations is decreasing more and more and they are replaced by compact or building stations For these reasons this scenario will no longer be considered in the following

Industrial park Industrial parks are grid areas in which primarily only few subscribers with larger purchased capacities are located These include industrycompany buildings (eg tradesmen) mixed areas (mixture of commercial and residential complexes) business areas (eg office and retail buildings) but also earmarked areas (eg business parks) In the case of increased share of electro-mobility situations are particularly relevant in which EVs are parked for a sufficiently long time (eg gt 30 min) so that charges might be performed Charging possibilities arise for example in gastronomy shopping buildings or employee car parks Usually these consumers are connected with sufficiently large supply lines but several consumers are connected to the same transformer Due to the increase in charging possibilities for EVs higherincreased loads are to be expected at the individual connection points

With expected higher power consumption at the individual connection points as well as parallel power consumption at different connection points industrial estates theoretically represent an interesting scenario By a meaningful grid modernization eg using an intelligent charging algorithm a grid overloading can be prevented and the available capacity in the grid is optimally distributed to the different grid connection points However as this is also possible with a local load cap and the grid connection power is usually higher than required (bigger cables at the time of constructions comes with only a minimum additional costs) this scenario is not considered further In this context (with load cap) the DSO provides a certain capacity and leaves it to the customer not to exceed this capacity eg by locally using an intelligent load control In the case of customers of the businesses the charging needs do not provide much flexibility (usually only a short visit) to be used for grid optimization

Urban Areas Urban areas are characterized by a higher density of residential and

commercial areas in which among other things commercial and residential buildings can be


57

found coupled to a connection point (business on the ground floor residential buildings on top) Due to common space limitations in urban areas parking spaces for residents and customers are usually located in underground car parks Due to the higher punctual load separate connecting cables are used directly from the transformer This potentially results in a transformer limitation However an extension of the transformer capacity can be problematic and even impossible due to space limitations (eg in the case when the transformer is part of the building)

In certain scenarios many separate transformers must serve the combination of commercial and residential complexes and the resulting strong fluctuations in consumption behaviour By modernizing the grid with an intelligent charging control the potential to save resources arises as fewer transformers are required This scenario is included in the cost comparison

Street Lighting The provision of sufficient street lighting for roads and pathways is generally

in the hands of local authorities but the management of these is often outsourced to the relevant grid operator (eg Bayernwerk) for economic reasons The latter then takes care of the maintenance of the systems This task is completely decoupled from state regulation which means that the grid operator can negotiate an individual price with the municipality for this task Depending on the type of lighting one or two supply lines are provided for the lighting (usually connected single phase to the rest of the grid) The latter is used if there is a separate daynight switching The street lighting is subject to a separate circuit where normally no other consumers or feeders are connected to this circuit Due to the pure design of the light supply there are usually no problems with current load or voltage drop during grid operation Street lighting systems are small distribution systems and would theoretically also be suitable for the use of charging points whereby the cable limitation must be observed for higher loads

Due to the low necessity of capacity of the cables for street lighting only limited free capacities are available within a street block for charging EVs (even in the case of changing the lights to energy-efficient LEDs) In addition the connections are single-phase which would lead to an unintentionally high unbalanced load For these reasons this scenario is not considered further in this analysis

Unplanned changes in building areas The construction of new grid areas (eg building area

of a village) is subject to a planning process in which the infrastructure is installed according to the existing requirements In the past there have always been external impacts that changed the development of the buildings and thus the grids developed in unplanned directions so that for example the supply position of the transformer is no longer optimally positioned Due to the above-average length of the cable a new transformer would have to be installed at a suitable grid point In addition the transformer house a new busbar and separate access to the medium-voltage network are also necessary which is associated with high costs

Due to unplanned changes in the development area and the associated development of the grids in originally unplanned directions new transformers with transformer houses busbars and medium-voltage grid access will become necessary over time By a meaningful grid modernization eg by the use of an intelligent charging algorithm the maximum load at the transformer could be reduced in addition to the compliance with the voltage limits so that no grid construction measure becomes necessary This scenario is included in the cost comparison with a specific case in lower Bavaria

In summary the three scenarios Older existing grid Urban Areas and Unplanned changes in the development area are examined in more detail In addition there is a comparison with the

costs of a completely new development grid area where the infrastructure of the grid is directly tailored to the future needs of electro mobility

III32b Cost Analysis

Traditional grid expansion mainly consists of material (eg transformer transformer cabin ) and working costs (eg transformer installation and cable connection costs excavation work ) The costs for installing a new cable (or replacing an old one) is highly depending on the


58

surface finish (tarred road is much more expensive than meadow) and can be estimated with 90euro per meter on average In this simplified calculation also the type of cable can be neglected as the main part of the costs stem from excavation work Transformer replacement work is around 2500euro plus a new transformer starting from around 6000euro for a 400 kVA and 7500euro for a 630kVA (prices from 2019) An on-load tap changing transformer which is used for stabilizing the voltage by changing the voltage transformation ratio depends on the actual need of the grid over time is around the double cost of a normal transformer A completely newly built transformer with a transformer cabin can easily climb up to 60000euro including all construction work In addition a grid connection from the medium voltage grid is required The price for this connection highly depends on the given situation and thus a generally applicable price cannot be given here After installation there are only minimal costs for checks and maintenance that are neglected in this calculation For the calculation we assume an average usage duration 30 years for transformers

For the extraction of the costs for traditional grid enhancement (technically required parts and constructions work) the grid planning tool from Bayernwerk was used This tool already includes an EON group-wide consolidated option to include the power of home charging stations The grid planning tool uses peak-power demands as limiting factors With an increasing amount of households the power demand per households decreases because of the simultaneity of power demand This method however only considers the maximum power requirement over the day Figure 20 shows the typical power usage of a household Traditional grid enhancement is planned to handle the peaks at around 1930 but it can be seen that there is a lot of opportunities to charge in off-peak times

Figure 22 BDEW H0 standard load profile for households on a working day in winter

The ELECTRIFIC software-based solution requires a measurement infrastructure connected to a data collection system for both the proactive and reactive part For the proactive part (Forecast Controller) the data is used to model the specific power grid in order to provide an estimation of future utilization This is used to shift the charging process to a more grid-friendly timeslot The reactive part (Smart Charger) uses the data to analyse the current grid situation and adjusts the running charging process in a grid-friendly manner The costs of this intelligent software-controlled solution are on the one hand hardware costs for measuring devices and installation and on the other hand running costs for data communication (249euro communication costs per month per device) and softwareserver maintenancelicense (assumed 1euro per device per month) Running costs (mainly communication costs) depend on the amount of charging stations The more charging stations the more communication costs but less software maintenance costs per charging stations (software is developed once and can be used multiple times) We assume that all CSs already have an intelligent energy meter installed (standard for newly installed meters in Germany) which are used as voltageloading measurement devices In this way only a communication and control hardware (around 120euro

Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

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Series1 500 400

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Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

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Series1 730 1285

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Series1 800 1348

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Series1 1200 1215

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Series1 1400 1240

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Series1 1500 1107

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Series1 1600 1024

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Series1 1630 1032

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Series1 1700 1099

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Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864

Series1 2015 1807Series1 2030 1727Series1 2045 1639Series1 2100 1556

Series1 2115 1489

Series1 2130 1434

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Series1 2200 1332

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Series1 2230 1205

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Series1 000 749

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aliz

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Wh

a

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59

hardware plus around 80euro installation costs) are required Communication to the charging stations need to be provided by the charging station operator In the case of households the customers Internet connection can be used For the transformer and the most critical point in the grid separate measuring devices with mobile connection and cabins are required The costs for each of the measurement cabins (including a measurement device) is assumed with 2000euro Communication costs for the transformer station and the most critical point are calculated from the estimated data transfer and are 249euro per device per month (30MB)2 in 2019 For the sake of a simple model the costs are assumed not to change over time On the other hand hardware costs are only included once and no hardware change will occur in order to compensate potential decreasing communication costs in our calculation

The number of participants in the intelligent control program is assumed to increase with the spread of EVs in an exponential way so that 100 of all households have a need on charging an EV in 2050 The 100 target was set to compare it with the currently used grid planning which is also prepared for 100 e-mobility (grid planning is planned way ahead of time)

Older existing grids As an example a small grid part of a village with 42 households in

Bavaria was taken With traditional grid expansion a transformer replacement to a transformer with more capacity and on-load tap changing capabilities would be required in order to prepare that specific low voltage grid for 100 e-mobility concerning voltage stability and capacity limits The costs for the transformer including installation would be around 17500euro In comparisons the costs for a software-based control consist of communication devices communication costs and software maintenancelicenses costs Figure 23 shows the cumulative costs of traditional grid expansion versus the costs of a smart charging solution After 29 years the running costs of the software-based solution get slightly higher than an initial grid enhancement At this time an EV spread of 88 is reached

Figure 23 Cumulative cost comparison for the scenario of an older existing grid

Urban Area For this scenario we assume an urban area that has a built-in transformer which

cannot be replaced by a new one with more capacity due to space limitations In this case a new transformer needs to be installed and the grid would be splitreordered to the old and the new transformer The costs for the new transformer is assumed to be 60000euro and the costs for the medium voltage connection and some short cables for the grid reordering 20000euro (around 220 meter of power lines) The original grid area also contains 42 households

2 O2 Internet of Things SIM cards httpsiottelefonicadeshop-iot-sim-karten)

transformer 2020 euro1750000



transformer 2023 euro1750000 transformer 2024

euro1750000 transformer 2025


























euro1750000

Traditional Grid Expansion 2020 0































hardware 2020 euro420000



hardware 2023 euro440000 hardware 2024

euro460000 hardware 2025




euro480000





euro540000


euro560000


euro600000




euro720000










communication 2020 euro5976































software maintenance 2020 euro1200

















software maintenance

2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

lost grid fees 2020 euro4200































Smart Charging Solution 2020 0































Costs of smart charging solution vs grid expansion

Traditional Grid Expansion

transformer

Smart Charging Solution

lost grid fees


communication

hardware


60

Figure 24 Cumulative cost comparison for the scenario of urban areas

Figure 24 shows the cumulative costs of traditional grid expansion versus the costs of a smart charging solution Even after 30 years the running costs of the software-based solution are only 238 of the grid enhancement option In this scenario the smart charging solution is highly recommended

Unplanned changes in the building area In this scenario a grid with an initially planned

building area of 34 households has developed in a different way The required traditional grid enhancement would include a newly build transformer at a different place (no tap changer required due to quite new lines) a new grid connection of this transformer on medium voltage and a few meters of new low voltage cables (~50 m) to restructure the connected circuits This scenario is not comparable with the software-based solution as with a new transformer the grid management possibilities (circuit switching) also increase Thus we compare the software-based solution with a smaller grid expansion solution where a bigger transformer replaces the existing one and the new cable for restructuring the grid is installed

euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50

Costs of Smart Charging Solution vs Grid Expansion


Cable

transformer



communication

hardware































euro1000000

cable 2020 euro450000

cable 2021 euro450000 cable 2022 euro450000
































































euro440000



euro460000


euro480000



euro500000


euro520000
















software maintenance 2020

euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61

Figure 25 Cumulative cost comparison for the scenario of unplanned changes in the building area

Figure 25 shows the cumulative costs of traditional grid expansion versus the costs of a smart charging solution After 28 years the running costs of the software-based solution get slightly higher than an initial (reduced) grid enhancement At this time an EV spread of 82 is reached

Completely new developed grid area In the scenario of a completely new developed grid

area the only cost saving for a smart charging solution would be a smaller transformer as the material costs of bigger cables are too small and can thus be neglected The difference in cost of the two previously mentioned transformers is only 1500euro and this difference is already overshot by the measurement hardware costs of our software-based solution This is only considering the grid limits (voltage and capacity) but not the potential a digital connected grid could provide

III32c Summary

Under the assumed exponential ramp up of share of EVs to 100 until 2050 the software-based smart charging solution can provide a cost reduction for the DSO especially in the scenario of urban areas where the only other option is expensive grid expansion with completely newly built transformer stations In all detailed scenarios that were calculated the software-based solution provides economic benefits in mid-term planning of grid expansion (planning horizon up to 25 years) especially in the considered cases of old existing grid and unplanned changes in the building area For long term planning the costs increase with the number of users of such a system

The smart charging solution provides a benefit in flexibility as the costs directly depend on the number of users compared to traditional grid enhancement where the total (high) costs are a fixed initial investment independent of the number of users This flexibility might be economically beneficial if the share of electric vehicles does not increase as expectedplanned This could happen if battery electric mobility is exchanged by hydrogen propulsion or other technologies The running costs for the software-based solution depend on the amount of charging stations that are connected to the system thus lower costs can be expected if the share of battery electric mobility is less than 100 In addition the installed ICT infrastructure could also provide additional value in cases of power quality monitoring energy theft identification or additional grid-friendly ancillary services on system level like frequency control with local congestion management

The smart charging solution might also be useful in the case when the spread of EVs increase too fast so that grid expansion is not possible in all grids at the same time (limitation of construction works) It thus might help to temporize in the grid expansion for full e-mobility

In the case of completely new developed grid areas the construction of a transformer and cables which are required anyway overtop the material costs which could be saved with software-based control In this case the usage of the smart charger solution is not recommended

In total we see that there will be a mixture of technology in the future where on the one hand traditional grid expansion is done in cases where it is economical and software-based solution help in scaling on temporal axis (development to fast for construction work) and introducing more flexibility in the grid where it is needed

III33 Charging Scheduler

The ELECTRIFIC charging scheduler is used to schedule individual EV charging processes for a whole fleet and charging infrastructure It does so by planning the available charging power to single charging processes throughout time

Moreover different optimizations can be performed during the scheduling process Besides grid-friendly and battery-friendly charging the charging scheduler can optimize for charging price (minimization) as well as for the ratio of renewable energy (maximization) in the charged


62

energy Since a fleet of EVs consumes a large value of energy if driven daily the potential for optimization is high Therefore this section provides an analysis of the optimization impact for a real EV fleet scenario

III33a Scenario Description

The impact of the charging scheduler on charging of an EV fleet can be performed using different business models As the scheduler was already used in ELECTRIFIC by the EFO TMB as an example a cost and impact analysis for TMBrsquos bus fleet is carried through A detailed description of the fleet EVs and CS infrastructure can be found in D84 section IV24 Also this impact analysis is done based on daily charging schedule inputs which were provided by TMB in the beginning of April 2019

In addition to the existing data information on the ratio of renewable energy for the Spanish peninsula was collected from Red Eleacutectrica de Espantildea3 This information has a time precision of 10 minutes and contains information about the renewable energy generation in MW

bull Wind

bull Hydro

bull Solar PV

bull Solar thermal

bull Thermal renewable

and conventional energy generation

bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle

bull Cogeneration waste

From the overall energy generation the renewable ratio is calculated solely from the renewable energy sum in the first list above divided by the energy amount of both lists (renewable and conventional) Figure 26 visualizes this ratio throughout the course of the working days in the week starting from 1st of April 2019 It can be seen that there are strong differences in profile and value between each day Thus there is a challenge in optimizing for the ratio of renewables especially with a highly dynamic energy demand from the EV fleet

3 httpsdemandareeesvisionapeninsulademandatotal


63

Figure 26 Change in ratio of renewable energy for the Spanish peninsula

Due to multiple energy contracts at the TMB premises the charging price is 79 euroctkWh from 2000 to 2315 and 59 euroctkWh from 2315 to 0600 Therefore there are only two time sections with an individual price and an optimisation in price will lead to charging processes located in the cheaper one

The daily charging scheduler inputs of TMB were enhanced by the ratio of renewable energy data as well as the charging price These were fed into the charging scheduler in order to get optimized schedules for the working days between 1st and 5th of April

It needs to be mentioned here that the charging price and ratio of renewables are parallel inputs for the charging scheduler Also there are further inputs which enable grid and battery friendliness which are not covered by this section but are explained in deliverable D53 However the degree of optimisation to one or more of these criteria can be configured by the EFO at every charging scheduler execution Thus it is possible to optimise for only one criterion such as price or to optimise on multiple criteria where the degree of optimisation is a value of 100 distributed amongst the chosen criteria An example for this would be 65 price 20 renewables 10 battery friendliness and 5 grid friendliness In the analysis of the charging scheduler impact however we used either 100 renewables or 100 price optimisation leaving the other criteria at 0

III33b Renewable Ratio Analysis

Figure 27 on the left side shows the charging processes of the fleet EVs (identifiers 8530 8610 8611 and 8612) for all five working days where the horizontal axis describes the scheduling time horizon between 2000 and 0600 in quarter hour slots (starting timestamp on the top of the subfigure) For each day the renewable percentage is provided with a colour Here a dark green colour depicts the highest renewable ratio for the day whereas a dark red colour represents the lowest value Red cells without values represent the EV tours ie when the EVs are not available for charging In contrast their charging processes are shown in blue with the charged energy per slot in kWh It can be seen that the charging processes are placed predominantly at high REN times For this scenario a 100 optimisation on REN was chosen


64

On the right side (end) of the charging schedules the average renewable ratio is shown for the values from the grid This is on one hand provided for the grid values colored from red to green The calculation for uses an averaging as shown in the equation below

=1

119899∙ sum 119877119894

119899

119894=0

where

is the average ratio of renewables for the charging scheduler time horizon

119877119894 is the ratio of renewable energies in one 15-minute slot

119899 is the number of time slots

119894 is the time slot index

On the other hand the average renewable energy value is provided with regards to the chronological position of the charging slots and the amount of energy charged for each EV throughout the scheduling time horizon Thus an average ratio or renewable energies for the generated charging schedule is calculated The following formula describes this process in more detail

119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where

119904 is the average ratio of renewables of the EV fleet charging processes generated by the charging scheduler

119898 is the number of EVs

119895 is the EV index



119864119895119894 is the overall energy consumption of an EV in a certain time slot

119877119894 is the ratio of renewable energies in a certain time slot

The results of both calculations are also shown in the first two data rows of Table 11 From these values alone it can be seen that the charging scheduler always provides a fleet charging schedule which has an improved average ratio of renewables

Table 11 Renewable optimization by using the charging scheduler

Day 01042019 02042019 03042019 04042019 05042019

REN in avg charging [] (grid information)

2435 3145 3791 4145 4591

119904 Avg REN in optimized charging []

2621 3417 4387 4463 4934

∆119877119900119901119905

Absolute REN optimization improvement []

185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65

optimization improvement []

Besides the above shown calculations the improvements in contrast to the grid information needs to be analysed In this regard the improvement in REN is shown numerically to allow a quantitative analysis Table 11 contains this evaluation in rows three and four The absolute

REN optimization improvement ∆119877119900119901119905 describes the absolute percentage improvement of the

generated schedule compared to the average grid REN

∆119877119900119901119905 = 119904 minus

Since the energy consumption and REN are different in the individual days this value varies slightly and does not allow a direct comparison between the days Therefore the relative

REN optimization 119877119900119901119905 is calculated (last row in Table 11)

119877119900119901119905 =119904

It becomes apparent that the REN optimization by the charging scheduler has a high potential A relative improvement of nearly 16 could be attained for one out of five samples

(days) Overall the average optimization for the chosen days 119900119901119905 is 10943

III33c Charging Price Analysis

To analyze the impact on charging cost a second subfigure is provided on the right hand side of Figure 27 It has a similar structure as the REN optimization chart ndash however the price as optimization criteria is shown above all day schedules since it stays the same for each day As the price drops by 2 euroctkWh at 2315 the charging processes never appear before this time Hence the optimization is fulfilled

The right side of the chart shows the average charging price using the average price within 59 and 79 euroctkWh for all 40 slots and the charged energy for the day One column further the charging price resulting from the slot positions and energies is listed This results in an improvement depicted by the cost saving in euro For the whole week of charging this results in 286 euro (see Table 12) If scaled up to the 52 weeks of a year 14872 euro can be saved It needs to be pointed out that the charging price of TMB is surpassingly low Therefore only a slight increase of price optimization can be reached here

Table 12 Charging price optimisation by using the charging scheduler

Day 01042019 02042019 03042019 04042019 05042019

Average charging price [euro]

784 220 469 526 875

Scheduled charging price [euro]

706 198 422 474 788

Cost saving [euro] 078 022 047 052 087

Since the charging processes are all scheduled for the time after 2315 the relative cost savings for each process are 10 A more dynamic pricing scheme would result in a higher variance in the relative average charging price optimization Also the price savings can be higher the more EVs are charged each day

III33d Summary

This impact analysis describes the benefits of using the charging scheduler for two criteria the maximization of REN in the charged energy of an EV fleet as well as the minimization of the charging price


66

The analysis is based on real world data (REN and charging price) of an exemplary week of EV fleet charging at the public transport company TMB in Barcelona For the five week days individual schedules were generated and analysed mathematically

Overall it can be determined that the REN intake could be maximized by up to 16 (9 on average) Next the price improvement is at 286 euro (roughly 150 euro a year) although the charging price of TMB is already low

It needs to be clarified that these results should only be respected for this specific example The optimization results of the charging scheduler are highly dependent on its input parameters and hence on the specific EV fleet charging case This means that with an unlike EV number and energy demand grid properties and CS infrastructure a different result can be expected This can result in higher improvements than shown in this impact analysis


67

Figure 27 Visualization of scheduled charging processes


68

IV ELECTRIFIC BUSINESS MODELS TO EXPLOITABLE

COMPONENTS


IV11 Direct from the ELECTRIFIC solution

The list below together with the partners that own Intellectual Property

Exploitable result (tools solutions and services) Description of

the result Intellectual Property owned by

Charging scheduler

D53

THD Energis HTB RDGFI E-WALD

- Web UI THD RDGfi E-WALD

- FMG HTB THD Energis

- Logic Unit THD E-WALD Energis

- CTAPD Energis THD

Battery health monitoring D53 THD

Smart Charger D43

Uni Passau Bayernwerk RDGfi HTB

- Event-driven solution (exploitation of data) Uni Passau (Kafka Streams) RDGfi

ADAS UNIMA RDGfi CVUT

- ADAS Mobile APP (ADAS UI) D35 D63 UNIMA RDGfi

- Agent-based e-mobility simulator D73 CVUT

- Multi-criteria Route and Charging Planner for EVs (real ADAS AI)

D73 CVUT

Allocation mechanisms

D73

CVUT Uni Passau

- Offer pricer CVUT

- Offer generator Uni PASSAU

Charging station reservation system D35 D84 Uni Mannheim RDGfi Energis


69

Dashboards for DSO EFO and EV User D35 Energis

EV static data (OpenData library) D53 Energis RDGFI

eMobility services

D35

Uni Passau RDGFI Energis

- Energy service Uni Passau (Ren calculation) RDGFI

- Cockpit service Energis RDGFI

- EV Model service Energis RDGFI

- OpenElectricityMap service (REN prod forecast) RDGFI

Forecast Controller D35 D63 Energis Bayernwerk

Symbolic and Financial Incentives Concept D63 D84 UNIMA

Psychological EV User Profiles D63 UNIMA

EMSA Architectural Model D24 UNIMA Bayernwerk Energis Uni Passau

E-MMM D96 All partners

Regional Electricity Mix Estimation Model D35 D43 Bayernwerk Uni Passau


70

IV12 Exploitation results derived from the ELECTRIFIC solution

IV12a Tool to analyse dynamic pricing possibilities to increase

usage of CS that are currently under-used

IV12a1 Short description

The goal of this analytics tool is to identify charging locations where pricing adjustments could bring the largest benefits to the CSP in terms of increased revenue andor utilization The tool does this by aggregating the CSP usage data and presenting the results on a map in a browser By being able to analyse the usage trends of its charging locations the CSP can identify charging stations with very low or very high demand for given location Based on this analysis CSP can then adjust its pricing to increase or decrease the demand for services and ultimately optimize their profit In the future pricing analytics tool can be combined with dynamic pricing algorithms developed in WP7 in order to enable automated suggestion of optimum price levels for charging services

ELECTRIFIC partners aim to engage providers of technical solutions for charging station providers in order to discuss how the dynamic-pricing-based tools for improving the utilization and profit of charging station providers could be leverad in practise

IV12a2 Intellectual property

The intellectual property in this area comprise the software implementation of the analytics tool owned by CVUT

IV12b Event-driven solution

IV12b1 Short description

An event-driven architecture is a software architecture based on the production detection and reaction to events Events can be anything from ldquosomething that happenedrdquo or ldquoa change in staterdquo It is a powerful model to let a solution be reactive This paradigm is often applied in big data projects where one needs to handle big streams of data (eg IoT data from sensors)

At the base is Apache Kafka which acts as the distributed persistent event store Producers produce events consumers consume events and both consumers and producers are completely independent from each other As data streams through the Apache Kafka topics consumers get notified in real-time about incoming events and as a result can lsquoreactrsquo on them

Event store

WEBMICRO

SERVICESCUSTOM APPS MONITORING ANALYTICS

SENSORS NOSQL HADOOP DATA LAKE ERP

Figure 28 Producerconsumer event store overview

For handling these big data streams multiple competing technologies are available It mainly depends on what you are trying to solve For the processing and analyzing of these data streams (such as time-based windowing grouping real-time querying ) you need a tool that supports streaming Examples are Kafka Streams Apache Spark AKKA hellip Typically these solutions are applied by Micro Services that act in context of a specific domain


71

Our solution combined Apache Kafka Kafka Connect Kafka Streams and AKKA as the building blocks of the event-driven architecture Kafka consumers where implemented in dedicated Micro Services which implemented both Kafka Streams (data aggregation) and AKKA The latter solution was used to model the real-life objects (measuring devices charging stations hellip) as Actors Incoming events (KPI values) are distributed to the proper Actor instances Their algorithms take into account the incoming values and react by sending messages to participating objects An example is the steering of Charging Station profiles via standardized protocols (OCPP OSCP)

Figure 29 Actors interact with each other by sending messages to each other

The Micro Services architecture that is applied to implement the various Consumers has proven its value They are setup as domain-specific and independent services that maintain their own state Services should be able to work independent of each other and have no direct runtime dependency on each other This allows each service to scale differently and apply its own release management However it is often the case these services need to work together This requires special patterns and techniques to be in place which introduces a lot of additional complexity Several design patterns are described in literature for solving these issues but luckily some commercial cloud providers already provide solutions A good example is the API Gateway which is the sole entry point towards your micro service-based backend where it manages cross-domain issues such as authentication authorization logging auditing etcetera

The future of the ELECTRIFIC solution would be to move to a commercial cloud provider and replace some components (eg the API Gateway) and to scale out the solution Also all micro services should be made truly independent of each other by introducing a brokered solution in between them Also introduction of a versioning scheme of our APIrsquos would contribute to the production readiness of the solution Another idea is to introduce event detection and store events that occur in the services andor algorithms in Kafka either for analytics or for commercial data consumers (keeping GDPR in mind) We already implement a solid security architecture but it should be improved to prevent and mitigate any security risk (eg implement TLS between producers and consumers)

The principles of the architecture can be easily applied to other domains Smart cities Industry 40 hellip All could benefit from this setup especially the modelling of real-life things in the AKKA framework has proven to be a very powerful and scalable solution It is designed for concurrency scalability resilience and reactive streaming data Via immutable message passing the system avoids locking and blocking and thus gets more done in the same time

To summarize the AKKA actor-based framework allows to

encapsulate state without locks

use a model of cooperative entities reacting to signals changing state and sending signals to drive the whole application forward

addresses the shortcomings of the traditional call-stack


72

IV12b2 Intellectual property

For the Smart Charging solution RDGfi has developed a custom Kafka Connect connector that interacts with the WinPQ MySQL database that holds the aggregated KPI values of the measuring devices installed in the field It is a specific connector that allows to stream incoming KPI values into the Kafka event store It thus acts as a Kafka producer and is a key architectural component

IV2 ELECTRIFIC capitalization on knowledge

IV21 Consultancy

We can offer consulting services around eMobility and its integrationimpact with the grid data processing and management services and new channels for eMobility actors to make their services known These services correspond to actors who themselves provide eMobility services such as charging station providers public operators and public services They can capitalize on the data to improve their action their effectiveness and increase their visibility among the public

The consultancy that will be delivered via the expertise acquired from ELECTRIFIC could also be offered to the DSO and the ES to enable them to have control of their customers and to refine their commercial or technological strategy The CSMS can also benefit from our increased technical expertise in battery charging technology

IV22 Processed data

These data give several details on mass driving habits their routing preferences charging patterns and matrices origin destination of users for example this data would be interesting for the public authorities to carry out actions that could affect mobility The processed data can also be offered to the CSP CSMS and DSO for their own data analysis and to obtain interesting information for their activities ELECTRIFIC data can also benefit commercial brands For them this information can for example inform about the opportunity of a location in relation to the attendance of their potential customers inform about the relevance of launching a new product or simply consolidate or improve their sales or marketing strategy Knowing that having reliable and processed data is a real problem at the moment the data proposed by ELECTRIFIC will be of real interest

The processed data may not be sold to customer segments but rather offered as a partnership in exchange for other services

IV23 New advertisement channel

ELECTRIFIC via ADAS constitutes a visibility channel for the CSP CSO and also the commercial entities providing EV services For the first two it allows them to inform their customers of current or upcoming promotional offers or simply new ones they want to launch For commercial entities publicity to attract more customers is the key We can differentiate between those which publicity will be based on their EV services offer (for example charging points are available in their parking area) and those that would like to use the ADAS as a means to inform about current offers or broadcast commercial events they want to organize


IV31a EV static data

Currently there exists no complete service that provides the available information about the EVs Note that the current customized solutions in the market include information on limited number of EV types and brands Within the context of the Electrifcic project numerous EVs


73

(of different brands and types and model years) where involved in different trials This necessitated the collection of relevant information about the EVs (eg battery capacity maximum power etc) Those information are stored in a database and available to the Electrific as well as external usage in the form of an open API As a future step this service could be further enhanced so that it becomes as a sort of a public library

IV31b OpenElectricyMapPercentage of Renewables

As some of the WP6 trials were designed by considering the percentage of renewables inside the German grid this required a reliable source of information Within the context of the Electrific project ldquosmardderdquo was considered at the begining as the data source to compute the corresponding percentage of renewables However soon we found out that their service was not stable in the sense that there were several days (eg even sometimes weeks) that no data was available This forced us to search for another reliable source which was supposed to be ldquoentsoecomrdquo Such a source contains information about the major european transmission system operators (TSO) Unfortunately this source too showed some unreliable behavior Consequently we designed our own customised solution when none of the above two sources have information regarding the percentage of renewables Our solution is based on taking the previous 15 days of available data (both from smardde and entsoe) and calculating the average in order to predict the corresponding percentage of renewables The algorithm is implemented and deployed within the Energy service which has an open API that can be used both by Electrific and external usersAs a future step despite the reasonable predictions that the averaging method is giving this can be furture improved through machine learning techniques such as SARIMAXIndividual exploitation strategies


74

V PARTNERS EXPLOITATION PLANS

V1 RDGfi

V11 Description of the Intellectual Property of RDGfi

- Web UI of the Charging scheduler solution

- Event-driven solution of the Smart charging solution

- ADAS mobile APP (ADAS UI)

- EV static data

- eMobility services (Energy Service cockpit service EV model service OpenElectricityMap)

V12 Exploitation strategy

From the beginning of the project the Group Gfi (hereafter Gfi) was strongly interested in analysing the commercial possibilities of ELECTRIFIC results As global ICT provider the idea of developing a new business line related to e-moblity was always considered as the company found that the market opportunities in the sector were very promising (which in fact has been assessed and confirmed during the project execution) Hence the Group Gfi is interested in commercializing ELECTRIFIC results relying on the collaboration with ELECTRIFIC partners as several results are co-owned The Gfirsquos IP (owned or co-owned) is listed below

- Web UI

- Smart charger

- ADAS Mobile APP

- Charging station reservation system

- EV static data

- EMobility services Energy service Cockpit service EV Model service and OpenElectricityMap service (REN prod forecast)

At Group level (approx 20000 employees 21 countries - httpsgfiworldfr-engroup) there are 2 main business units involved in the project results exploitation They are located in Paris belonging to the structure of Gfi Informatique which is the mother company of the Group and they define the strategy for the rest of the Grouprsquos entities

1) Public Sector and Smart Cities Unit (PS-SC Unit) httpsgfiworldfr-enofferssolutionsniveau_offre32-smart-cities

2) Energy Utilities and Chemicals Unit (EUC Unit)

Depending on the potential early adopter or customer an specific ELECTRIFIC solution will be assigned to one of them Nevertheless as the holistic approach of ELECTRIFIC should combine both ecosystems individual solutions and integrated offering will be defined As example the Smart Charging solution (co-owned with UNI PASSAU) is supposed to be incorporated by DSOs as a way of optimizing energy consumption (EUC Unit) At the same time some cities ndash as part of ldquosmart cityrdquo initiatives ndash are promoting such solutions among local energy stakeholders in order to control energy consumption in the city (PS-SC Unit)

In fact Gfi has already incorporated to their offering portfolio the ELECTRIFIC results both in terms of technical results and knowledge

Figure 30 below shows the Energy amp Utilities sector IDCard for Gfi in which Electromobility is now part of the 8 vertical solutions offered


75

Figure 30 GFI EampU Sector IDCard 2019

In Figure 31 the list of the most important references of the Group Gfi at international level are represented In the centre our reference related to ELECTRIFIC

Figure 31 Gfi EampU Sector Key References 2019


76

In terms of actions to reach our current customers and to create awareness on new potential ones some dissemination actions were launch complementing this offering We can see an example in the description of the use cases related to smart mobility in Figure 32 httpsgfiworldfr-ennewsactualite446-finding-the-way-to-smart-mobility

Figure 32 Gfi Smart Mobility use cases

Another key publicationexploitation action towards possible adopters was the release of a dedicated booklet to Electromobility Figure 33 created by the EUC Unit and edited by Gfirsquos journalists called ldquoELECTRIFIC paves the way for electromobilityrdquo httpswwwgfiworldfr-

ennewsactualite744-electrific-paves-the-way-for-electromobility

Figure 33 Screenshot of Gfis booklet on electromobility

This booklet was distributed to GFIrsquos interested customers and highly shared on social media having as sponsors Mr Jean-Franccedilois Penciolelli - Executive Director Public Sector and Franccedilois Boncenne - Executive Director Energy Utilities amp Chemicals Sector

The abovementioned portfolio around eMobility include two types of proposals First we propose all solutions and services directly extracted from the project Second we propose our customers to innovate with us around non-existing solutions that we could develop with them thanks to the sector knowledge acquired during the project


77

For the first set of proposals and for the IP which is co-owned Gfi has already reached preliminary business agreements with some partners In general when co-owners are scientific entities (eg UNIMA UNI PASSAU THD) they will not perform specific commercial actions by themselves Therefore Gfi will provide the go-to-the-market power and strategy and co-ownership will be rewarded by share on revenues requesting certain consultingdeveloping services andor funding of specific items (PhD students labs) based upon a fixed amount

As result of this strong market-oriented actions Gfi can already report some successful exploitation results

- Gfi Informaacutetica (Spain branch in Bilbao) is providing a service to IBERDROLA (world leader in wind power one of the biggest electric companies in the world and biggest energy group in Spain) for managing a service related to the 24x7 customer support at EV charging stations Itrsquos a temporary service (Iberdrola will launch the tender for the long-term service by the end of 2019) consisting of supporting CS exploitation and customer issues while charging

o Charging infrastructure end-user support when using it including as well software tools (mobile app web etc)

o Monitoring and actuation to alerts received from the charging points and support to end-user tickets (not working not available etc)

The inclusion of ELECTRIFIC in the tender was essential to demonstrate that Gfi combines knowledge about support centres and emobility Gfi plans to reply to the long-term service tender in the next few months

- InterConnect project (H2020 GA 857237) ELECTRIFIC demonstrator in France This description is included in Section VI2 As key player in this project we can find ENEDIS (the French DSO)

Once the project is finished Gfi will continue with this offering around electromobility The company will invest on efforts both technical and commercial to reach agreement with customers In parallel Gfi will try to obtain additional RampD funding (from framework programmes such as H2020 or Horizon Europe) to help financially sustaining this investment and continuing with the invaluable knowledge acquisition

V2 University of Mannheim

V21 Description of the Intellectual Property of UNIMA

The main intellectual property (owned or co-owned) of the UNIMA is listed below

ELECTRIFIC Mobile Application This application enables users to navigate with their EVs it provides charging station location feedback and charging recommendations when renewables are high

User Profiles A framework was developed categorizing users to best tailor incentives to their EV related needs and attitudes based on a variety of psychological and demographic variables

Incentives The design of incentives was specifically conceptualized to appeal to EV users and a variety of different incentives were tested to investigate their effect sizes

E-Mobility Systems Architecture (EMSA) Framework The EMSA model and framework were developed during the project and published in a scientific journal publication

Market Maturity Model (MMM) The MMM was developed in order to assess and define the maturity of e-mobility markets in the European Union It can be adapted for certain products of interest


78

ELECTRIFIC Mobile Application

The ELECTRIFIC app allows various navigation options including A to B short-trip planning but also multiple leg trips for multi destination needs It provides users with all charging station locations along the route in a certain radius along with essential charging station information such as payment options plug types and access CS selection is flexibly structured so that the route is recalculated based on CS selectiondeselection A CS reservation architecture and information flow for an OCPP-based reservation prototype are included CS provide renewables feedback in and range information based on previously user-input data

For the design and HMI of the application psychological methods and concepts were used to simplify and optimize functionalities and designs and present applicable content in the most user-centric way possible including presentation of information on screen streamlined usage and landing page designs In particular minimum necessary access to user related data is ensured

User profiles

The developed user profiles make use of a unique process of design development and validation of psychological measurement tools for understanding user groups and individual personality and social psychological facets

A behavioral science specific data analysis toolset is employed to understand constellations of motivators and barriers in combination with socio-demographic strata and shape them into clusters that form the user profiles EV users are categorized into tech-oriented utility driven cluster value-oriented ecologically driven cluster or both by which it is possible to predict their purchase intentions and match behavioral incentives This can aid in target group identification and marketing procedures

Incentives

Sets of financial symbolic and material incentives were designed In the ELECTRIFIC application a renewables incentive scheme employed traffic light incentivization with green recommendations when renewable energy was above average in the grid Vouchers were used and compared to CO2 atmosphere offset to target different user clusters to increase green charging

For charging a green weeks program was developed with an automatic mailing system that would offer customers free charging if renewables were predicted to be particularly high for the next day A material incentive was designed specifically for e-bike customers who were incentivized to charge their bicycles in route in return for vouchers offering a free cup of coffee

EMSA Framework

The EMSA framework was developed during the project to improve technical alignment between WP2 and WP3 It is based on the Smart Grid Architecture Model (SGAM) which was also used during the project phase

The EMSA framework includes the EMSA model itself guidelines and a methodology for interoperability assessment It is intended to support system engineers in modelling building and analyzing new and existing complex cyber-physical systems in the e-mobility domain

The EMSA framework was published as a paper in the scientific journal ldquoEnergy Informaticsrdquo in 2019


The University of Mannheim plans to exploit its relevant IPRs in following ways


79

ScientificResearch Purposes Findings will directly pour into our active research areas For

instance phd students will base their work on the findings of this project which will strengthen the impact of their thesis one doctoral thesis taking the use case of block chain billing processes in the EV environment with a focus on green charging is currently being created Experience gained in this project will be the source for further research projects and publications It might be the case that the respective source code from the University of Mannheim will be published as Open Source on github or similar plattforms in order to strengthen the verifiability

Academic Teaching Findings and results from the project will be used to improve courses

and other teaching activities at the university Selected tools developed in the project are already being exploited for studentrsquos final projects within the bachelor and master study programmes offered by UNIMA and follow-up academic projects are in the planning process

Industry partnerships The University of Mannheim reserves the right to use the knowledge and technologies resulting from their contribution to the project also for commercialization In the event of any commercial exploitation efforts by other consortium partners to enhance or directly market components with development participation by the UNIMA and use them for commercial activities the university shall ensure an appropriate participation in a form yet to be negotiated (eg financial support for PhD research) UNIMA aims to forge synergistic partnerships with relevant industry entities in order to unlock new opportunities to commercialize project results and findings Agreements will be defined upon specific case

Consulting UNIMA plans to capitalize on the technology developed and knowledge gained

from the project for consultancy services in the area of electromobility in particulary and energy technologies in general

V3 ENERGIS

V31 Description of the Intellectual Property of ENERGIS

- Charging scheduler

o FMG

o Logic Unit

o CTAPD


- Dashboards for DSO EFO and EV User

- EV static data (OpenData library)

- Emobility services

o Cockpit service

o EV Model service

- Forecast controller

- EMSA Architectural Model


We want to propose end-customers an advanced solution to optimise the charging of EVs according to their goals and taking buildings consumption and renewable energy production into account This advanced solution will be tailor-made and will be built via a 3 steps approach which is described in below table with estimated efforts

Step 1 Analysis of the customerrsquos environment understand their current situation

Estimated Efforts


80

(in man-days)

Which are your goals

Cost Bottom line savings in euro via lower contracted power (load-shedding)

Cost Bottom line savings in euro by shifting consumption to cheaper time periods of multi-tarif contracts

QoS Make sure EVs are charged according to service schedule

Cost Identify kWh of charging which can be sold on the upcoming flexibility market

Cost amp Green Optimise to auto-consume local PV production to charge EVsmiddot Cost Optimising energy consumption of company assets (EVs + building)

2MD

Analyse available and needed data

Organisation Enterprise with EV fleet

Charging stations Location charging points (number manufacturerModel connectors)

Charging stations management system ManufacturerModel

Charging data records (CDRs) Start end time kWh

Service Schedule of arrival amp departures of EVs

EVs ManufacturerModel Required chargings of the EVs Used charging points (fixed variable)

Energy supply Contract invoices

Local PV Production Nominal power PV production data

Automated Meter readings (EANPOD) Obtained from energy supplier DSO

Consumption building Obtained from BMSSCADA data logger

3MD

Technical Analysis of automatic execution of charging plan via remote commands (eg adaptation of charging points charging management solution) Check control mechanism to send commands to charging points (eg OCPP)

Based on the analysis either commands will be sent directly from EnergisCloud or they will be sent via a Raspicy gateway to be deployed

2MD

Step 2 Configuration of EnergisCloud for monitoring purpose

Develop and test necessary connectors

charging points

PV

building consumption

service data

12 MD

Configuration of monitoring in EnergisCloud platform (mappings assets metrics dashboards)

2MD

Step 3 Configuration of EnergisCloud for charging optimisation purpose


81

Develop remote control connector 8 MD

Design and Coding of optimisation algorithms in EnergisCloud AIampModeling Environment depending on following aspects

ObjectivesGoals to reach

Available Data set

Remote control options

Optimisation method Rule based (heuristic)

15 MD

Testing of performance of optimisation algorithm and on impact on goals

5MD

Analysis of alternative optimisation methods Reinforcement Learning (dynamic) Quadratic Programming (static) Mixed Integer Programming

To be discussed with the

customer

Total Effort 49MD

With these 3 steps the tailor-made advanced solution will be configured for the end-customer The below diagram shows how the output of the Electrific project will be part of the EnergisCloud AI-Modeling platform on top of which tailor-made advanced solutions will be built to address end-customersrsquo needs

Figure 34 EnergisCloud AI-Modeling platform

V4 Czech Technical University in Prague

V41 Description of the Intellectual Property of CTUV

Multi-Destination EV travel planner


82

The EV travel planner described in detail in Deliverable D73ndashChapter 2 enables EV drivers to plan their EV trips and charging in the way optimizing selected travel criteria including time price greenness or implicitly via pricing grid friendliness

The intellectual property in this area comprises multi-destination travel planning algorithms as well as their software implementation

Charging service offer dynamic pricer

The dynamic pricer of charging service offers is a key component of the proposed market-based mechanisms EV charging allocation mechanisms described in Deliverable D73ndashChapter 3 The dynamic pricer enables charging station providers to adaptively price their services depending on consumer demand state of the grid and their business objectives

The intellectual property in this area comprises computational techniques by which the price of charging services is determined as well as the software implementation of these techniques

Agent-based electromobility simulator

The agent-based simulation testbed enables a simulation-based assessment of charging capacity supply-demand interactions for a wide range of future electromobility scenarios

The intellectual propertymdashwhich builds on software tools developed by CVUT prior to the projectmdash comprise the software implementation of simulation components that enable to simulate charging stations and EV driver interactions with them


The CVUT aims to exploit the above described results in one of multiple was described below

Industry partnerships CVUT aims to forge synergistic partnerships with relevant industry entities in order to unlock new opportunities to commercialize its know-how in the mobility space Specifically CVUT has recently established a joint research lab with Škoda Auto that will explore how CVUTrsquos RampD results might be leveraged in Škoda Autorsquos products and services The EV travel planner will be one of the important technological results whose exploitation will be discussed

Software licensing CVUT maintains partnerships with leading companies in the field

of ICT and transport and mobility Licensing software developed in the project to these companies is an important way for exploiting CVUTrsquos results Specifically CVUT aims to enage the providers of technical solutions for CSPs in order to discuss how the dynamic-pricing-based technologies for improving the utilization and profit of CSPs could be leverad in practise

Consulting CVUT plans to capitalize on the technology developed in the project for

consultancy services in the area of electromobility Specifically CVUT has been already actively participating in the official discussions surrouding the preparation of the City of Praguersquos long-term electromobility strategy (in particular parts surrounding the development of the EV charging infrastructure) which is likely to followed by other cities in the Czech Republic

Teaching Selected tools developed in the project will be used to support studentrsquos final projects within the bachelor and master study programmes offered by CVUT (in particular the Open Informatics programme)


83

V5 Deggendorf Institute of Technology

V51 Description of the Intellectual Property of THD

The goal of the charging scheduler software is the automatic generation of an optimal schedule for the charging processes of an EV fleet This helps to reduce the workload of the EFO because the scheduling of the charging processes without the support of the software can be very complex and time consuming The optimisation criteria are minimisation of charging cost maximisation of renewable energy intake battery friendliness and grid friendliness In addition the charging scheduler reduces the stress on the power grid by restricting the accumulated charging power of all processes to the available grid capacity This tool will therefore mainly interest EFOs who would see in this tool an effective aid to the efficient management of their electric cars with the requirements that this requires The CSPs could also see an indirect interest there because a better management of the vehicles will result in a more effective distribution on the points of recharge and thus less variables to be managed by the CSP

As the charging scheduler is divided into different sub components the overall IP is distributed between the consortium partners as follows

THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10

V52 Exploitation plan

The charging scheduler will be provided as a two-option package The first option provides the full solution in which the fleet management gateway is included Thus fleet operators can provide their circumstances for schedule creation including latest EV data This combination allows the fleet management gateway to call the battery health monitoring system for battery health recommendations which will be fed into a charging scheduler call The fleet operator will in the end receive charging schedules optimised on battery friendliness The second packaging option contains the charging scheduler without fleet management gateway This option aims at fleet operators who already use an ITC solution which is capable of the fleet management gateway functionality or who are not interested in battery friendly charging schedule optimisation

Both packaging models come with an integration code for IBM ILOG CPLEX Optimisation Studio V126 (D1806LL) CPLEX is an optimisation framework to solve mixed-integer linear problems and are an essential feature of the charging scheduler in order to create EV fleet charging schedules However the CPLEX libraries themselves are not included in both packaging options as it requires a paid license to execute it

As there are other optimisation frameworks it is possible to change the integration of CPLEX to a different provider For example the Google OR-Tools suite can be used which is available under Apache 20 license Though it should be considered that the integration of Google OR-Tools implies a change of the charging scheduler An estimated three months are required to do so for an experienced programmer in order to replicate the existing code to support OR-Tools and for detailed testing

In terms of running costs operating the charging scheduler is mostly impacted by the CPLEX license fee In addition it needs to be placed on a web server which may be hosted by a commercial provider or may be part of the EFO IT infrastructure Since the charging scheduler requires 1 CPU and 4 GB RAM the hosting cost is kept minimal


84

The charging scheduler will be provided free for use and royalty-free in non-commercial solutions or demonstrators However a CPLEX license should be provided by the third-party adapting it

In the case of commercial use the charging scheduler will be provided within a non-exclusive license between the ELECTRIFIC consortium and the third party Here the CPLEX license must also be provided by the third party Depending on the application of the charging scheduler a royalty-based or overall fee can be negotiated between the ELECTRIFIC partners and the third party All licensing revenue will be fairly distributed between the consortium partners according to IP claim ratio (see subsection 2 above)

V6 University of Passau

V61 Description of the Intellectual Property of Uni Passau

The University of Passau has contributed with code and methodology to several different parts in the project The main contribution was done in the Smart Charging Solution more particular with the Smart Charger algorithms described in Deliverable D42 and D43 The main code contribution is in the core logic implementation and partial code contribution goes to the event-driven architecture in the Apache KafkaAkka environment among them time management and message exchanges

Furthermore the logic of the Offer Generator was designed and implemented by the University of Passau The core logic of the energy service and initial implementations of the Regional Electricity Mix Estimation Model are further intellectual properties The university also contributed to the design of the EMSA Architectural model which was finally published in an Open Access Journal


Some of the contributions from the University of Passau will find reuse in dissertation projects of PhD students who participated and contributed in the project Furthermore some code will be used for experiments of further energy and power system related publications where it might be the case that the respective source code from the University of Passau will be published as Open Source on github or similar plattforms in order to strengthen the verifiability

The University of Passau reserves the right to commercialise the services resulting from their contribution to the project In the event of any marketing efforts by other consortium partners to market components with development participation by the university the university shall ensure an appropriate participation in a form yet to be negotiated

V7 Has-to-be GmbH

V71 Description of the Intellectual Property of Has-to-be GmbH

In the Smart Charging solution Has-to-be has implemented load management for certain OCPP 15 compatible charging stations


Basic load management is currently used by ELECTRIFIC as well as two additional customers for their respective research projects

HTB used for beenergised was some technical information regarding the load management In parallel to the ELECTRIFIC project HTB did some own research to find out how it could be possible to work or integrate a load management function Basically it was checked which stations where able to proceed a load management and HTB developed a some kind of


85

catalogue what stations should be able to do and which functions they need to have to make a load management work efficiently Regulating the power of charging is the key aspect

On the other hand HTB tried to find out what we would need to develop within the Backend

From this very basic research HTB gathered some findings being the main important one that it was too early Most of the stations were not ready for this topic and HTB would have to develop some more components in the software to get a fundamental basic for a load management But nevertheless HTB managed to develop a load management but only in combination with a few stations This is not a relevant value for the market

Later in the project HTB worked on the charging scheduler within ELECTRIFIC but it became clear very quickly that HTB would not use this function for the company

V8 Agegravencia drsquoEcologia Urbana de Barcelona (BCNecologia)

V81 Description of the Intellectual Property of BCNecologia

BCNecologia is a public consortium comprising the City Council of Barcelona the Municipal Council the Metropolitan Area of Barcelona and the Barcelona Provincial Council As the developer of the Sustainable Urban Mobility Plan the Agency plays an important role in the planning of the mobility strategies for the city and has close relations with the main entities that manage the public transport of the city such as TMB Therefore BCNecologias commitment to ELECTRIFIC was always to manage and coordinate all activities in the development of the trials to be carried out in the city

Consequently BCNecologia has not been the intellectual developer of any tools services or technological solutions for ELECTRIFIC project


However as coordinators of the Barcelona trial with TMB we adopt the exploitation plan established by THD in the previous paragraphs And focus it exclusively to TMB and focused on the products developed during the Bus fleet trial (Charging Scheduler and Fleet Management Gateway)

However as coordinators of the Barcelona test with TMB we adopt the exploitation plan established by THD in the previous paragraphs and we address it exclusively to TMB and focus on the products developed during the test of the bus fleet (Programmer of cargo and fleet management gateway)

V9 Bayerwerk AG

V91 Description of the Intellectual Property of Bayernwerk AG

Bayernwerk was involved in the development of the models behind the Smart Charger Our expertise in power grid operation was brought into the discussion and influenced the design criteriarsquos of the Smart Charger In addition Bayernwerk tested the Smart Charger together with University of Passau and E-Wald at the Bayernwerk customer centre in Vilshofen This includes the setup up of the whole measuring infrastructure which was critical to perform the Smart Charger test

The design and concept of the proactive (grid) Forecast Controller was mainly developed by Bayernwerk Besides the implementation of grid models and grid situtation evaluation in GridLAB-D the concept of power reservation was created by Bayernwerk and implemented


86

within the ELECTRIFIC eco-system by Energis The grid models are completely owned by Bayernwerk

Concerning the SGAM effort Bayernwerk was involved in the creation of the E-mobilbity Systems Architecutre (EMSA) Model This model was finally published as paper publication at Springer Journal of Energy Informatics

Bayernwerk designed a first concept on how to estimate the regional electricity mix which will be published as a poster presentation at the 8th DACH+ Conference on Energy Informatics 2019


As the Bayernwerk pilot was running with quite good results the fleet manager decided to continue the data collection of the eight Renault Zoe cars as long as the costs are low enough There is an verbal commitment between THD and Bayernwerk so that THD will analyze the data

Another exploitation result to mention is that the gained knowledge from the EV charging tests (mainly the interference frequencies and grid perturbations of different EVs) are further used to improve the grid planning and fault allocation processes

Scientific efforts have been published already or will be further worked on in furture projects especially the regional electricity mix estimation

V10 E-WALD GmbH

V101 Description of the Intellectual Property of E-WALD GmbH


o Web UI

o Logic Unit

Data analysis

Consulting of THD members with knowledge of real life EFO fleet needs

- Charging Station reservation system

Proof of concept design

API functionality testing by involving required stakeholders

Corresponding with GDPR requirements


Collection of data and management of the correct data sets of corresponding EVs

Data analysis

- ADAS

GDPR creation and implementation to the app

Data provision


E-WALD wants to use the IP of the certain product to improve existing business models or create new business models for an existing customer group The main goal is to improve charging processes by extended features (eg reservation) or incentivized charging possibilities


87

V11 e-Šumavacz sro

e- Šumava does not own any IP in any exploitable project result Our role in the projext was related to experimenting and testing of some of the ELECTRIFIC solutions in our location

However we have learnt from this experience We faced some problems during these trials some of them actuallly linked to the e-mobility providers we have contracts with These issues will help us improving the quality of service towards our customers and the attractiveness of EVs to be used in our park


88

VI EXPLOITATION ACTIVITIES

This section contains the description of some activities already exploiting results of the project including a pilot run with a part of the electric fleet at Bayernwerk a partnership with the H2020 InterConnect project regarding a demonstrator in France the exploitation at TMB in Barcelona as part of the project trials and a proof of concept for EV Corporate fleets in Madrid

VI1 Bayernwerk pilot

Bayernwerk decided to run a pilot in their already existing electric fleet with some ELECTRIFIC components in order to show the usability of the project outcome and to emphasize the importance to have a final prototype combining multiple sub-parts of the project In total eight cars are included in this pilot using the ADAS App and the Charging Scheduler

VI11 Areas and assets

The pilot installation includes 3 out of 24 branch offices of Bayernwerk Their location is shown in Figure 35

Regensburg (headquarter) Vilshofen an der Donau (customer centre and trial area of the Smart Charger) Eggenfelden (customer centre)

Figure 35 Location of the Bayernwerk pilot (marked in green)

The charging infrastructure in the three areas Regensburg Vilshofen and Eggenfelden is as presented in Table 13

Table 13 Charging infrastructure at Bayernwerk pilot areas

CS Type Regensburg Vilshofen Eggenfelden


89

Public

1x CS 2x Type 2 (22kW) 1x CS 2x Type 2 (22kW)

2x CS 2x Type 2 (22kW)

1x Fast CS

1x CHAdeMO (50 kW) 1x CCS (50 kW) 1x Type 2 (43 kW)

Private (Wallbox)

gt 20 CS 1x Type 2 (11kW) 1x CS 1x Type 2 (11kW)


In total eight Renault ZOE from the Bayernwerk electric fleet were used As described in D53 the ZE Service was activated in all eight EVs and the charging flexibility as well as the charging need were extracted from the collected data Figure 36 shows exemplarily some extracted data of one EV The tour consumption and thus the charging needs are calculated from the difference in the battery state of charge over time and are shown in the upper part of the figure (state of charge of the battery in blue and average discharging speed and thus charging need in black) The charging flexibility is calculated from the ldquois pluggedrdquo and ldquois chargingrdquo parameters (lower part of the figure in blue) and the actual charging rate (lower part of the figure in black) from the changes of the battery state of charge

Figure 36 Charging need (upper part) and charging flexibility (lower part) extracted from ZE service data collection


90

VI12 ELECTRIFIC components

For the Bayernwerk pilot the ELECTRIFIC App as well as the Charging Scheduler are integrated in parts of the Bayernwerk electric fleet The Smart Charger was already tested within the WP4 trials in Vilshofen and is thus not part of the Bayernwerk pilot

VI12a ADAS App

As shown in Figure 37 an adjusted version of the official ELECTRIFIC app (excluding WP6 trial code including the private wallboxes of Bayernwerk and adjusted data protection agreement) is installed in the eight cars in the three areas In addition this app automatically retrieves the battery state of charge from the car via the ZE service (ELECTRIFIC Cockpit Service) Smartphone mounting devices were installed on account of this pilot in order to ensure the safety of the users

Figure 37 Installed smartphone with running the ELECTRIFIC App

VI12b Charging Scheduler

The Charging Scheduler is used to optimize on renewable energy charging cost reduction battery health increase and grid-friendliness For this optimization corresponding data needs to be provided In all three areas the calculated average renewable energy mix of Germany is used For the area Vilshofen the remaining grid capacity is calculated from the nominal grid capacity minus the actual grid usage from the measurement devices installed for the trials In Regensburg and Eggenfelden no grid data is available Only the eight cars equipped with the ELECTRIFIC app as well as the ZE Service provide enough data for optimization with the Charging Scheduler

The Charging Scheduler component and its interface via the fleet management gateway were connected to the Energis time series tool (see D35) where the anonymized data of all Bayernwerk assets are stored for the project The asset structure is shown in Figure 38


91

Figure 38 Bayernwerk pilot assets structure shown in Energis

VI13 Analysis and results

In this section a short analysis of the collected data and some results are presented

VI13a ADAS App

In the very short test period from 12072019 until 21082019 the legal text was accepted 91 times 91 times the ldquoplan a triprdquo button was clicked with in total six charging stations selections Navigation was started 31 times and successfully finished 9 times (driven distance is comparable to planned distance) After fixing a bug regarding the battery recommendations 11 battery recommendations have been shown to the users Due to data protection limits no further details are given in this deliverable


The collected data form the ZE Service was analysed to see the potential flexibility on shifting the charging process From Figure 39 it can be seen that two cars (EV 5 and EV 6) were not charged as well used very often (total tour consumption near zero) at all within the time period from 12072019 until 22082019 EV 1 has been connected to the charging point most of the time The other EVs have been connected to a charging station between 6 up to 35 of time but actually charged only 066 up to 231 of the whole time This shows that there is quite a lot of flexibility to shift the charging operation of the EVs in time without changing the usage of the EVs In case charging stations are also available in the other time the EV is parked but not plugged into a charging station the flexibility is much higher as can be seen from the pie chart representing the times in which the car was parking and not charging (blue) driving (black) and charging (green) For the optimization we used the assumption that there is a possibility to charge the EV anytime when then EV is not driving


92

Figure 39 Example of the analysis of fleet usage

Note In Figure 39 EV 5 and EV 6 were not charged at all The small amount of charging is because the state of charge is collected as integer and due to the measurement inaccuracy and our calculation method there is a change in state of charge and thus it seems that there is very little driving and charging

When optimising the charging processes to increase the intake of renewables as much as possible we get a charging plan that we can compare with the actual charging recorded from the ZE Service We thus can compute the actual improvement of the intake of renewable energies in the battery In total 25 more renewable energy could be charged if the EV is able to charge anytime when it is not driving The percentage of renewable energy in the batteries could be increased from 42 to 53 using the German wide average as data basis A detailed split to all EVs is shown in Figure 40


93

Figure 40 Improvement of the intake of renewable energies in the Bayernwerk fleet

VI2 InterConnect project Demonstrator in France

InterConnect project Interoperable Solutions Connecting Smart Homes Buildings and Grids Grant Agreement 857237 will start on Oct 1st 2019 Itrsquos a project co-funded under the

H2020-DT-2018-2020 call (Digitising and transforming European industry and services digital innovation hubs and platforms) Topic DT-ICT-10-2018-19

Gfi is part of this Consortium One of the tasks in this Consortium is to participate in the French demonstrator related to Demand flexibility for supporting the grid in normal and emergency operation - an end-users focused approach The ELECTRIFIC Smart Charging solution will be deployed and demonstrated in these sites as part of end-user solutions for reaching demand flexibility

Figure 41 InterConnect project French pilot with electromobility solutions


94

VI3 TMB exploitation

TMB exploitation is closely related to the exploitation plans and strategy of the Charging Scheduler by THD Details of the possible adoption of this solution from TMB is described in section V52

VI4 EV Corporate fleet B2B model proof-of-concept

One of the possible exploitation channels for ELECTRIFIC that Gfi has explored is the business case of corporate fleets when their vehicles are turned into EVs

This initiative was launched by Gfi Spain It included the provision of global ICT solution supporting the complete value of this business and both the technical proof of concept and the business model have been validated with key stakeholders of the sector Technically the solution is based on ELECTRIFIC

Figure 42 ELECTRIFIC for EV Corporate fleet PoC

For the PoC we have evolved the ADAS UI mobile app in a way that can be used by a driver to know wherewhen to charge the vehicle

Figure 43 Examples of ADAS for EV corporate driver

For the business model other functionalities of ELECTRIFIC were taken into account in order to provide the taxi sector with a complete and sustainable on time solution

Further details can be provided under confidential clauses


95

VII CONCLUSIONS SUSTAINABILITY PLAN

Section V described the plans from partners towards ELECTRIFIC results once the project is finished In summary there are different approaches for exploiting the project results depending on partnersrsquo nature mainly

- Commercial exploitation

o Solutions and services

o Consulting

- ScientificResearch Purposes including RampD projects that support the further evolution of project results

- Academic Teaching

Regardless the channel chosen all partners agreed that keeping the ELECTRIFIC solution up-and-running is essential Presentations to eg potential early adopters students and key stakeholders will benefit from showing working prototypes instead of static screenshots and slides

Each academic partner committed to release its results under Open Source license will decide the best channel to expose their work and under which specific license OpenAPIs will be provided via the project website

Currently the ELECTRIFIC platform is hosted in cloud infrastructure OVH provider under the following setup

One server is hosting all the tools (Redmine - our collaborative platform website and project source code)

One server for collecting data from Vilshofen measurement devices + some services deployed on the windows machine

One server dedicated to the Bayernwerk trial

Two 2 integration servers hosting the complete integrated platform (linux based)

The current contract was created by ENERGIS as technical leader of the project with a cost of 25656euromonth

Tools Production (vps499533ovhnet) 25eurom

Bayernwerk trial Mercurio (ns3133522ip-51-75-131eu) 70eurom

Windows (vps387372ovhnet) 3374eurom

Integration (vps387371ovhnet) 2782eurom

Integration 2 (ns3091873ip-54-36-63eu) 100eurom

The agreement is that from 1st October RDGfi will take over the cost of the hosting of the integration environment keeping the platform running and available for all partners for at least 2 years (Sep2021)

With respect to the first server (Tools) suppling Redmine the project website (including project results description publications deliverables etc) and the source code it will be taken over by RDGfi as well that will decide whether to migrate it to internal servers or keep it on OVH It will remain active and available for at least another 2 years in parallel to the technical platform The servers related to specific trials (Smart Charging solution and Bayernwerk trial - Mercurio) will be dismantled after data backup


96

VIII REFERENCES

[BeKP09] BECKER JOumlRG KNACKSTEDT RALF POumlPPELBUszlig JENS Developing Maturity Models for IT Management In Business amp Information Systems Engineering vol 1 (2009) No 3 pp 213ndash222

[BFKR05] DE BRUIN TONIA FREEZE RONALD KAULKARNI UDAY ROSEMANN MICHAEL Understanding the Main Phases of Developing a Maturity Assessment Model In Australasian Conference on Information Systems (ACIS) (2005) pp 8ndash19

[PCCW93] PAULK MC CURTIS B CHRISSIS MB WEBER CV Capability maturity model version 11 In IEEE Software vol 10 (1993) No 4 pp 18ndash27

[RoBH04] ROSEMANN MICHAEL DE BRUIN TONIA HUEFFNER TAPIO A Model for Business Process Management Maturity In ACIS 2004 Proceedings Paper 6 (2004) pp 1ndash7

[Euro10] European Committee for Electrotechnical Standardization (CENELEC) CENELEC - EN 50160 - Voltage characteristics of electricity supplied by public electricity networks

[Bund17] Bundesverband der Energie- und Wasserwirtschaft eV (BDEW) Standardlastprofile Strom | BDEW

[Kiti12] KIT (Institut fuumlr Verkehrswesen Karlsruher Institut fuumlr Technologie) Das Deutsche Mobilitaumltspanel (MOP) Karlsruher Institut fuumlr Technologie (KIT) (2012)

[Kraf00] Kraftfahrt-Bundesamt Neuzulassungen von Pkw in den Jahren 2006 bis 2015 nach ausgewaumlhlten Kraftstoffarten URL httpswwwkbadeDEStatistikFahrzeugeNeuzulassungenneuzulassungen_nodehtml - abgerufen am 2018-12-13 mdash Neuzulassungen


97

IX APPENDIX

IX1 Data Collection

IX11 Area EV amp Fleet

Sub-Area ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs

018 Source 0 014 Source 0 0053 Source 0

EVO02


085 Source 0 049 Source 0 027 Source

0

EV Sharing

EVS01 Customer ratio

0044 Source 0 158 Source (1)

Source (2) 1 0

There are no official data to access for the Czech Republic

0


18 Source 1 10 Source 1 4

Source

0

EVS03

Ratio of BEVs in car-sharing

21 Source 0 1360 Source 1 102 Source (1)

Source (2)

1


98

EV Technology


WLTP-Standard for models in 2019 average range 288km

Top10 EVs sold + range Renault Zoe Source (1)

range BMWi3 Source (2) range KIA Soul EV Source (3) range E-Golf Source (3) range Smart EQ ForTwo Source (4) range Smart EQ ForFour Source (5) range Nissan Leaf Source (6) range Hyundai Ioniq Electro (NEFZ) Source (7) range Tesla Model S

2

WLTP- 200-250 km average range

Source (1) Source (2)

1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km

TOP 5 EVs SOLD IN CZECH

REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99

Source (8) range VW e-UP Source (9)

EVT02 V2G ready EVs

1 out of these 10 cars is available with V2G 7 of the top 10 cars sold have this Option


1

Magnum Cap is the first

company in Spain

installing V2G technology in 2019 in Santa Perpegravetua de Mogoda and this is the first

public charging station in

Spain for this year Nissan and Iberdola

will be working

developing V2G

technology

Source 0

Only Nissan Leaf from this

top 5 sold EVs in CZ

is V2G ready

175

Source

3



Average Price of EV compared to combustion engine cars

BMW 3 Series Source (1) VW Golf Source (2) Renault Clio Source (3) Renault

0

1422 units have been

sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera

ge Price of EV compared

to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100

~58 more expensive (3548167 euro to 2233667 euro cars used for comparison small Renault zoey 29900euro Renault Clio 12390euro medium nissan leaf 36800euro Volkswagen Golf 19520euro Tesla model 3 39745euro BMW 3er 35100euro )

ZOE Source (4) Tesla Model 3 Source (5)

euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF

38435euro vs VW GOLF 25375 euro

Large TESLA model 3 110030-120480euro

engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)

ŠKODA SUPERB Source (5)

0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro


Renault ZOE vs Renault Clio ZOE costs 325 less Nissan LEAF vs VW Golf LEAF costs 196 less Tesla Model 3

Source 1

Average cost of electric

vehicles 11 years use

12900 kmyear

VW GOLF and Seat

Leoacuten Cost + taxes 39958

Gas cost 1928

Maintenance 8 937

For 2018 EV vs Diesel

Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf

vs Combustion VW Golf

Leaf cost

375 less

Source

1


102

vs BMW 3 Series Model 3 costs 161 less

50823 --gt52721 (-

360)


EVG01 Fuel taxation

The fuel taxation in Germany was 562 Eurocents (for petrol) and 378 Eurocents (for diesel) in the year 2000 It was replaced by a new German tax ldquoEnergiesteuerrdquo in the year 2006 For the year 2017 (last

Source (1)

Source (2) 0

Since January 1st

Spain increased the fuel tax from

010-022 cents Paiacutes

Vasco Cantabria Castilla y

Leoacuten Rioja and Navarra raised the

taxes levied on motor

fuels by 58 euro cents (48 euro cents plus 21 VAT)

Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103

vaild data source) the taxation is 6545 Eurocents for petrol and 4704 Eurocents for diesel


up to 4000 Euros

Source 1

PLAN MOVES 2019 Help range 700 euros for motorcycles 15000 euros for trucks buses 5000 euros for electric vehicles

2- Tax deduction of up to 30 Navarra Castilla y Leoacuten Canary

Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -

corporationcompanies - for

normal

persons

nothing

Source 3


104

Islands (0) and La Rioja

3- Plan MUS Madrid 2 million only for private residents

4- Plan RENOVE Paiacutes Vasco 5 million (3000 euro EV 2500 euros Hybrids 2250 euros Autogas Natural Gas)

5- Castilla La Mancha investment of aid for EV FROM 500000 euros (Natural Gas LPG 800 euros EV 8000 euros Hybrids 3000 euros Trucks buses 125000 euros

6- Exempt of taxes of


105

circulation for EV

7- Reduction of the toll rate of the toll roads 8- EV they are exempt from registration taxes

Plan SEE vehicles 166 million


018 Source (1)

Source (2) 0 0141210 Source 1 0053 Source 0

EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0

IX12 Area Grid amp Energy


Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106



Bayernwerk expertise (qualitative)

3 4 Source 3

ENT02 Grid monitoring

First pilots started


0 gt94 Source 4 Pilot

projects Source 0

ENT03

Reliable and secure communication

Hard requirements of BSI

Bayernwerk expertise (involved in roll-out)

4

Bidirectional communication at fast chargers only There is GDPR on the DSO side End to end Modules Crypt2pay for encryption and Hardware Security (HSM) and (SE) by ATOS

Source 3

PLC - modem GPRS DLMS

protocol

Source 3

Power

grid

situation

ENG01

Distributed PV peak power in MVLV

5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557

Source 3 HV 067 MV 604 LV 824

trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01

Allowed interaction in the distribution grid

DSO 1 DSO ADM Source 2 DSO ES Source 2

ENL02


HTNT + sect14a EnWG

sect14a EnWG 3

Ministry Order IET2902012 - New meters

must allow time

discrimination and

telemanagement

- Allow real time data collection

Source 4

Collection of Laws no 82 2011

HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW

IX13 Area Charging Infrastructure

Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS

EV Source 4 0259 CS EV

Source

Informe Comisioacuten Europea

4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab

Source 0 00001 CS hab Source 0

00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source

Own Assumption 11kW is a good basis to calculate with since most CS could work 11 kW max

3 1926 kWhCS

httpswwwelectromapsco

mmapa (estimated calulcated)

2 No reliable

source NA NA


109

For most DC it should be 150 kW max

-gt 5572 kW (calculation average of 11 amp 150 Average of AC amp DC -gtgt Average of kWCS

CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND

system providers work with OCPP 12

and 15 IBIL (EVE + REPSOL)

worked in the development of OCPP 20 and will implement

starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2

Reservation is only possible on some CS

like fast chargers or

teslaacutes superchargers

Lack of incentives Very low

possibility of influence EV

drivers

Source 1

Reservations are not currently offered in

Czech Republic by

anyone - No

standardized app yet

Source (1) Source (2) Source (3)

1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02

Availability of easy payment

Payment methods depend on the charing stations and the Backend - credit card - PayPal - App (QR-Code) - PrePaid - Charging Card

Qualitative HTB public knowledge No lists exists which could be quoted

4

Free for all users at the moment (at

public CS on the street) You

register previously

through a web service and a

card is assigned to the

user

Source 3

Legislation mandates

that charging stations have to allow

unregistered charging for example through QR

codes


1

CSB03

Combined charging technology

There are the classic roaming networks like Hubject Gireve Ladenetzde e-clearingnet PlugSurfing Stromnetz Hamnburg htb

Qualitative HTB public knowledge (regarding the Community) Source (1) Source (2)

3

To this day Spain does not

form part of any ongoing

roaming projects Endesa

(Spainacutes main DSO) is

finishing details to join Enel in a

Source 0

Hubject EVmapa

Main networks

are operated by ČEZ

EON PRE

PRE


1


112

COMMUNITY and many more smaller ones

Source (3) Source (4) Source (5) Source (6) Source (7)

roaming project

CSB04

Incentivized charging options

There are contract-based advertisements of completely green charging at many CS But those contracts mostly arent worth anything because normally its just a calculation of the DSO and for other consumers there is more energy from

Qualitative HTB private knowledge Source

2


public CS on the street) No aids on charging

domestically or on private parkings

Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01

Hardware compatibility with EVs

Type 2 (always) CCS - more and more EVs use this for the possibility of DC charging and AC charging CHAdeMO - more and more often diverse Security-Standards und Features (current error monitoring electricity meter GSM WLAN Ethernet USB RFID) Less common is Power management Type 2 and CCS are standards

Source (1)

Qualitative HTB private knowledge (my own overview over the plug types from my experience with htb No document available)

4

1 Schuko (EU Plug) 2226

national connections 2 Mennekes 2354 national connections 3 Tesla 6

4 CHAde MO-DC 20 5 CCS

Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms

mention only CCS and Type

2 Chademo

is still installed in

some places but the amount of chademo connectors is expected

to decrease

National action plan

Source 3


114


CSL01

Combined legal assessment

Country specified a number of CS per EV (false) Gas stations have to have CS (false) Penalty for blocking parking spaces at CS (true but depending on the city) Incentivessubsidies to buyset up a CS (true - 40 of net costs max 3000euroAC charge point max 10000euroDC charge point) Depends on the city and the popularity of public CS and EVs In big cities like Hamburg or Berlin there

Source (1)

Source (2)

2

Law on Climate

Change and Energy

Transition establishes

- 2022 All gas stations must incorporate electric CS

- 2040 only EV sold

- 2050 only EV vehicles

circulating

Source 2

Country specified a number of CS per EV

- false Gas

stations have to

have CS - false

Penalty for blocking parking

spaces at CS - true (under certain

conditions) Incentivessubsidies to buyset up a CS - true under

specific conditions

National

action plan

Source

green

mobility

seminar

1


115

are penalties (moneytowing) against wrong parked cars in front of a CS Its not settled in the law yet Even signage is not consistent yet There are several combination of signs but none of them is truly valid Additionally it depends on the city because the right to tow away wrong parked vehicles is in the discretion of the cities

CSL02

Subsidy in

the

developm

ent of

charging

infrastruct

- Funding of CS 200 mio for HPC 100

mio for normal CS

(Source (1))

Source (1)

Source (2)

3

27 Millon euro Total

20 GDP 700207 euro CS

Source 3

No funding for private

users Funding for municipaliti

es and

Clean mobility seminar

Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)

- 22 kW with max 3000euro

(AC) - max 100

kW with max 12000euro (DC)

- gt 100 kW with max

30000euro (DC) Source (2)

- Maybe the regulation of

parking spaces

- Cooperation

of 6 county districts for

establishment of CS

networks (source is the foundation of

E-Wald)

- The law of Electric mobility

(Elektromobilitaumltsgesetz -

EmoG) gives a frame in

publicly owned

businesses to buy EVs

request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117

which the cities and

county districts can

give advantages to EVs in the infrastructure

and traffic Possibilities

are - parking on public roads

and sidewalks - Usage of

public roads or ways

which are restricted to a

particular usecase

- Exeptions for access

restrictions or transit bans

Source (3)

IX14 Area Consumers amp Society


GER GER GER ESP ESP ESP CZE CZE CZE


118

Value Data Source Level Value Data Source Level Value Data Source Level


COD01 Purchasing intention

237 ELECTRIFIC survey

1 31 ELECTRIFIC survey


1

COD02 Purchasing Power

17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2

COD03 Future viability of

EV




2



emissions




2

COE02 Knowledge about

climate change

3596666667

ELECTRIFIC survey



2

COE03 NEP scale 4355 ELECTRIFIC survey



3


6710

Source 4 3390 Source 2 3360 Source 2


COI01 User Knowledge

Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4

Prestige 262 ELECTRIFIC survey



2


120


2

Executive Summary

This deliverable describes the final results and decisions taken by the partners with respect to exploitation activities of the ELECTRIFIC project In order to reach these results partners were requested to have a market-oriented mind putting themselves in the shoes of a potential customer that would like to adopt ELECTRIFIC in order to release current business pains Additionally partners used as basis for this analysis the preliminary results described in the previous deliverable of this work package (D94 - Initial description of the project impact and business models definition)

On the one hand taking into account the project value propositions and their related business models we assessed the maturity of the market in which the different functionalities of ELECTRIFIC can be offered Impact of the ELECTRIFIC solutions within these markets is also described On the other hand we grouped these functionalities we identified the owner of their intellectual property and finally there is a description of how these owners plan to exploit them The document finishes with an analysis of the project sustainability plans


3

Contributors table




this deliverable


III Market Analysis



III21 MMM structure


(eSumava)



Wautelet (RDGfi)




products




Bodenschatz (THD)







Danner (UNI PASSAU)






4


Knowledge






Danner (UNI PASSAU)




Peacuterez (RDGfi)


(UNIMA)












Peacuterez (RDGFI)




Danner (UNI PASSAU)




Danner (UNI PASSAU)




Danner (UNI PASSAU)



5

Table of Contents

I INTRODUCTION 11





















IV21 Consultancy 72





V1 RDGfi 74






V3 ENERGIS 79



6

















V9 Bayerwerk AG 85



V10 E-WALD GmbH 86













VIII REFERENCES 96

IX APPENDIX 97






7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


3

Contributors table




this deliverable


III Market Analysis



III21 MMM structure


(eSumava)



Wautelet (RDGfi)




products




Bodenschatz (THD)







Danner (UNI PASSAU)






4


Knowledge






Danner (UNI PASSAU)




Peacuterez (RDGfi)


(UNIMA)












Peacuterez (RDGFI)




Danner (UNI PASSAU)




Danner (UNI PASSAU)




Danner (UNI PASSAU)



5

Table of Contents

I INTRODUCTION 11





















IV21 Consultancy 72





V1 RDGfi 74






V3 ENERGIS 79



6

















V9 Bayerwerk AG 85



V10 E-WALD GmbH 86













VIII REFERENCES 96

IX APPENDIX 97






7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


4


Knowledge






Danner (UNI PASSAU)




Peacuterez (RDGfi)


(UNIMA)












Peacuterez (RDGFI)




Danner (UNI PASSAU)




Danner (UNI PASSAU)




Danner (UNI PASSAU)



5

Table of Contents

I INTRODUCTION 11





















IV21 Consultancy 72





V1 RDGfi 74






V3 ENERGIS 79



6

















V9 Bayerwerk AG 85



V10 E-WALD GmbH 86













VIII REFERENCES 96

IX APPENDIX 97






7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


5

Table of Contents

I INTRODUCTION 11





















IV21 Consultancy 72





V1 RDGfi 74






V3 ENERGIS 79



6

















V9 Bayerwerk AG 85



V10 E-WALD GmbH 86













VIII REFERENCES 96

IX APPENDIX 97






7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


6

















V9 Bayerwerk AG 85



V10 E-WALD GmbH 86













VIII REFERENCES 96

IX APPENDIX 97






7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


7


List of Figures

































8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


8













List of Tables















9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120


9


Acronym Meaning














CO2 Carbon dioxide


CS Charging station

CSB CS Business

CSI CS Intelligence








CZE Czech Republic




ENE Energy Supply


ENL Legislation


ES Energy service

ESP Espantildea


10


EV Electric vehicle




EVS EV sharing

EVT EV Technology

GER Germany






kVA kilowatt

kW Kilowatt




LV Low volt

MV Megavolt





PV Present Value

REN Renewable


SoC State of Charge








11

I INTRODUCTION







o Methodologies


o Impact Analysis




o OpenAPIs






12












13

III MARKET ANALYSIS



2 Impact Analysis





III1 Methodologies







14












15







16








17








Name Description

Metric


Offer Supply of EVs

EVO01 Ratio of BEVs



lt 5 gt= 5 gt= 10 gt= 20 gt= 40

EVO02




lt 10 gt= 10 gt= 20 gt= 30 gt= 50




lt 1 lt 5 gt= 5 gt= 10 gt= 20


18





lt 5 gt= 5 gt= 20 gt= 40 gt= 80




lt 10 gt= 10 gt= 20 gt= 30 gt= 50

EV Technology




lt 150 gt= 150 gt= 250 gt= 350 gt= 400

EVT02 V2G ready EVs



lt 10 gt= 10 gt= 20 gt= 40 gt= 60











19



EVG01 Fuel taxation



lt 150 gt= 150 gt= 200 gt= 250 gt= 300




lt 10 (rising)

lt 15 (rising)


lt 10 (falling)




(government goal)

lt 1 1 ndash 5 ~ 20 ~ 50 ~70

EVG04 CO2 savings



gt 25 gt 50 gt 60 gt 80 gt 80





Level

ESP

Level

CZE

Level




20























21



Description Metric

(Classification)






No profile







0-20 20-40

40-60

60-80

80-100




Power grid

situation




0-100 100-200 200-300 300-400 400+


infrastructure


Qualitative metric



22



Ratio of energy

to the total

electricity price

gt 60

45-60 30-45 15-30

0-15


Optimization

potential on

renewable energy

production


0-15 15-30 30-45 45-60 gt 60



Qualitative metric







binary no yes



binary no yes




23



Level

ESP

Level

CZE

Level










1 2 2


3 4 3






24



Description Metric

(Classification)





the EVs


EV CS




of the countries

No Of CS population


lt200 CS per 10000

people

lt300 CS per 10000

people

lt400 CS per 10000

people

lt500 CS per 10000

people


Average Charging




DC


status of charging

infrastructure

Average rate of 37

kWh

11 kWhAC

0 kWh DC

11 kWhAC 50 kWhDC

22 kWhAC

150 kWhDC

22 kWh AC

450 kWh DC






charging stations



d-hoc charging

OCPP 16 (fully





25



ge (ISO 15118)


offers



offers






Event-based pricing


prices


payment

The possibility of


Charging


payment


addition App

addition Roaming

Addition PayPal



technology


Roaming






100 of CS in

roaming networks



DSO data


at CS

widely used fast

charging

widely used green

charging


charging

widely used load-

management

widely used v2g


Cable and Plug Type

Compatibility


No standardiza

tion in connectors

1 Connector

as minimum

Connectors are


CS

Connectors are


Connectors are



26

standardised

implement them

implemented

implement them



assessment




CS

Country specified a


false) Gas stations




false)



development of


e (Governme


Total investment in











27





Level

ESP

Level

CZE

Level

Public Network





Business








CSL02



investment)

3 3 2


28







Name Description

Metric






[Likert 1-5]


COD02 Purchase

power




2017

Avg Income

Source lt11000euro

11000euro-17000euro

17000euro-24000euro

24000euro-31000euro

gt31000euro

COD03 Future

viability of EV

To what extent


[Likert 1-5]



29



COE01 Awareness of

CO2 emissions


eg cars foreign

imports

[Likert 1-5]

(ELECTRIFIC survey)





1-18 18-26 26-34 34-42 42-5

COE02 Knowledge



humans on climate

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5

COE03 NEP scale



(ELECTRIFIC survey)


[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34 34-42 42-5


30



waste Source lt14

14-27

27-41

41-54

gt54


COI01

User Knowledge

Gap EV Usage



user)

[Likert 1-5]


18-26

1-18

COI02

User Knowledge

Gap EV Technology



user)

[Likert 1-5]

(ELECTRIFIC survey)

42-5

34-42

26-34

18-26

1-18


COA01 Acceptance

of constraints


[Likert 1-5]

(ELECTRIFIC survey)


42-5 34-42 26-34 18-26 1-18



of EVs

[Likert 1-5]

(ELECTRIFIC survey)

1-18 18-26 26-34 34-42 42-5


technology



[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26 26-34

34-42

42-5


31

ease of use etc)



perceived prestige)

[Likert 1-5]

(ELECTRIFIC survey)


1-18 18-26

26-34

34-42

42-5





Level

ESP

Level

CZE

Level












32













Name


Weight

Offer Supply of EVs


customers

02

EVO02

Ratio of EVs

registered bought

in 2018

Number of potential

customers

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0






005



005





01


02




Name


Weight


ENT01 Grid usage

behaviours data

- 0


34



potential

03


Secure charging 01



peak power in

MVLV


charging potential

01



01

Energy Supply


Incentives 01



Legislation


- 0


- 0


Incentives 01



Name


Weight

Public Network



options

01



CS


Business


Incentives 005



01



005


Incentives 005


35

Standardization



005




005



005



Name


(Preconditions)

Weight


COD01 Purchase

intention

Amount of potential

users

01


users

005



01


COE01 Awareness of

CO2 emissions

Amount of potential

users

005



005

COE03 NEP scale


01


- 0






005






01



01


36


01




Name


Weight

Offer Supply of EVs


Power

05

EVO02

Ratio of EVs

registered bought

in 2018


Power

02

EV Sharing


EVS02 Car-sharing

Providers

- 0


- 0



03




- 0


- 0




- 0





Name


Weight


ENT01 Grid usage

behaviours data

- 0


and grid capacity

03


37



015



peak power in

MVLV


Smart Charger

015



015

Energy Supply



005


To optimize on 005

Legislation



005



005



005



Name


Weight

Public Network




01



07

Business


- 0


- 0


- 0



01


38

Standardization


- 0



- 0


- 0



Name


Weight





- 0



emissions

- 0


- 0

COE03 NEP scale - 0


- 0



- 0


- 0




10


- 0


- 0

COA04 Prestige - 0


39




Name


Weight

Offer Supply of EVs


015



01

EV Sharing






015

EV Technology


015


01





- 0





005





Name


Weight




01


004







40



Energy Supply



018



018

Legislation


- 0

ENL02


- 0



01



Name


Weight

Public Network


005


005


CS Intelligence



Business



005


- 0



005



Standardization





- 0



01


41




Name


Weight




Important EFO goals

01


01



01


EFO goal 006



- 0



Important EFO goals

01


01



EFO goal 006


Important EFO goals

01


01








42









43



Czech Republic





44




Czech Republic




45













III31 Mobility App




46


Figure 15 Vilshofen









deviate more than



47


longer time





charging behaviour







48








Without ADAS


CPCS


CPCS but not CW


CPCS but not CH



49



With ADAS



shifted to CH



shifted to CH




incentivization











rate


to ADAS



III31d Results






50










51






52






53







54


III31e Implications









55












56



tower












57














58





Series1 015 676

Series1 030 608

Series1 045 549

Series1 100 499

Series1 115 462

Series1 130 436

Series1 145 419

Series1 200 408

Series1 215 401

Series1 230 396

Series1 245 394

Series1 300 391

Series1 315 388

Series1 330 386

Series1 345 383

Series1 400 383

Series1 415 384

Series1 430 388

Series1 445 393

Series1 500 400

Series1 515 409

Series1 530 431

Series1 545 477

Series1 600 558

Series1 615 680

Series1 630 828

Series1 645 980

Series1 700 1115

Series1 715 1216

Series1 730 1285

Series1 745 1327

Series1 800 1348

Series1 815 1354

Series1 830 1348

Series1 845 1331

Series1 900 1307

Series1 915 1277

Series1 930 1246

Series1 945 1215

Series1 1000 1190

Series1 1015 1173

Series1 1030 1162

Series1 1045 1157

Series1 1100 1157

Series1 1115 1161

Series1 1130 1170

Series1 1145 1187

Series1 1200 1215

Series1 1215 1254

Series1 1230 1296

Series1 1245 1330

Series1 1300 1348

Series1 1315 1342

Series1 1330 1317

Series1 1345 1280

Series1 1400 1240

Series1 1415 1202

Series1 1430 1168

Series1 1445 1137

Series1 1500 1107

Series1 1515 1079

Series1 1530 1055

Series1 1545 1035

Series1 1600 1024

Series1 1615 1022

Series1 1630 1032

Series1 1645 1056

Series1 1700 1099

Series1 1715 1160

Series1 1730 1237

Series1 1745 1326

Series1 1800 1423

Series1 1815 1524

Series1 1830 1622

Series1 1845 1712

Series1 1900 1789

Series1 1915 1847

Series1 1930 1882

Series1 1945 1889

Series1 2000 1864


Series1 2115 1489

Series1 2130 1434

Series1 2145 1384

Series1 2200 1332

Series1 2215 1272

Series1 2230 1205

Series1 2245 1133

Series1 2300 1057

Series1 2315 980Series1 2330 902

Series1 2345 825

Series1 000 749

Wat

t n

orm

aliz

ed fo

r 10

00 k

Wh

a

con

sum

pti

on


59







































euro1750000








































euro480000





euro540000


euro560000


euro600000




euro720000



























































2037 euro106800


2038 euro121200


2039 euro136800


2040 euro154800


2041 euro174000


2042 euro195600


2043 euro219600


2044 euro246000


2045 euro276000


2046 euro308400


2047 euro344400


2048 euro385200


2049 euro429600


2050 euro480000

































































transformer


lost grid fees


communication

hardware


60





euro-

euro10000

euro20000

euro30000

euro40000

euro50000

euro60000

euro70000

euro80000

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

2036

20

38

20

40

20

42

20

44

20

46

20

48

20

50



Cable

transformer



communication

hardware































euro1000000


































































euro440000



euro460000


euro480000



euro500000


euro520000

















euro1200


euro2400


euro4800


euro7200


euro9600


euro12000


euro15600


euro19200


euro22800


euro27600


euro32400


euro38400


euro44400


euro50400


euro57600


euro64800


euro73200


euro82800


euro94800


euro108000


euro122400


euro138000


euro154800


euro174000


euro195600


euro219600


euro247200


euro277200


euro310800


euro348000


euro388800
































































































cable

transformer


lost grid fees

communication


hardware


61





III32c Summary










62





bull Wind

bull Hydro

bull Solar PV

bull Solar thermal



bull Nuclear

bull Fuel gas

bull Coal

bull Combined cycle





63








64


=1

119899∙ sum 119877119894

119899

119894=0

where






119904 =1

119898sum

sum 119864119895119894 ∙ 119877119894119899119894=0

sum 119864119895119894119899119894=0

119898

119895=0

where










Day 01042019 02042019 03042019 04042019 05042019


2435 3145 3791 4145 4591


2621 3417 4387 4463 4934

∆119877119900119901119905


185 272 595 318 343

119877119900119901119905

Relative REN

10764 10865 11572 10767 10747


65





∆119877119900119901119905 = 119904 minus



119877119900119901119905 =119904







Day 01042019 02042019 03042019 04042019 05042019


784 220 469 526 875


706 198 422 474 788

Cost saving [euro] 078 022 047 052 087


III33d Summary



66





67



68


COMPONENTS






Charging scheduler

D53





- CTAPD Energis THD


Smart Charger D43







D73 CVUT


D73

CVUT Uni Passau

- Offer pricer CVUT




69



eMobility services

D35













70













Event store

WEBMICRO






71











72




IV21 Consultancy



IV22 Processed data









73





74


V1 RDGfi





- EV static data




- Web UI

- Smart charger

- ADAS Mobile APP


- EV static data









75





76









77


















78




User profiles



Incentives



EMSA Framework







79








V3 ENERGIS



o FMG

o Logic Unit

o CTAPD





o Cockpit service

o EV Model service






Estimated Efforts


80

(in man-days)







2MD












3MD



2MD



charging points

PV


service data

12 MD


2MD



81




Available Data set



15 MD


5MD



customer

Total Effort 49MD







82


















83





THD ndash 60

Energis ndash 10

E-WALD ndash 10

GFI ndash 10

HTB ndash 10







84










V7 Has-to-be GmbH







85












V9 Bayerwerk AG





86








V10 E-WALD GmbH



o Web UI

o Logic Unit

Data analysis








Data analysis

- ADAS


Data provision




87

V11 e-Šumavacz sro




88













89

Public



1x Fast CS


Private (Wallbox)






90



VI12a ADAS App







91




VI13a ADAS App





92





93








94












95





o Consulting


- Academic Teaching

















96

VIII REFERENCES










97

IX APPENDIX

IX1 Data Collection



r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Offer Supply of EVs

EVO01 Ratio of BEVs


EVO02



0

EV Sharing



Source (2) 1 0


0



Source

0

EVS03



Source (2)

1


98

EV Technology





2



1

WLTP -

Standart for model

s in 2018 avare

ge range

264 km


REPUBLIC Source (1)

VOLKSWAGE

N eGOLF Source (2)

BMW i3

Source (3)

NISSAN LEAF

Source (4)

HIUNDAI IONIQ

Source (5)

KIA SOUL Source (6)

2


99


EVT02 V2G ready EVs



1


company in Spain




will be working

developing V2G

technology

Source 0



is V2G ready

175

Source

3





0


sold representing

an increase of 708

Small RENAULT

ZOE 21630

ANFAC

Source

0

Avera


to combustion

RENAULT ZOE Source

(1) RENAULT

CLIO Source (2)

NISSAN LEAF Source

(3)


100



euro vs RENAULT

CLIO 19030 euro

NISSAN LEAF

34360euro vs NISSAN

Micra 21250 euro

Medium VW e-GLOF



engine

cars ~

553 more expensive

small RENAULT

ZOE - 3670

0euro Rena

ult Clio - 1195

0euro mediu

m NISSAN

LEAF -

36970euro VW

GOLF -

16800euro

large TESL

A

VW GOLF Source (4)


0


101

MODEL 3 -

not oficially sell

in Czech

rep (Austr

ia 59300euro)

ŠKODA

SUPERB - 3074

0euro



Source 1



12900 kmyear

VW GOLF and Seat


Gas cost 1928

Maintenance 8 937


Source (1)

Source (2)

Instituto

para la

Diversificaci

oacuten y el

Ahorro de

Energiacutea

0

Nissan Leaf


Leaf cost

375 less

Source

1


102


50823 --gt52721 (-

360)


EVG01 Fuel taxation


Source (1)

Source (2) 0

Since January 1st







Source 3

Only eco fee euro O

400 euro euro

1 200 euro

euro 2 120

euro

Source

0


103



up to 4000 Euros

Source 1



Asociacioacuten

de

Constructor

es

Europeos

de

Automoacuteviles

(ACEA)

Ver maacutes en

wwwmotorpasioncom

4

up to 13000euro -


normal

persons

nothing

Source 3


104







105

circulation for EV




018 Source (1)


EVG04 CO2 savings

86 mil

(~18)

Source (1)

Source (2) 0

76 Mill metric tons

Source 1

45 milion

s metric tons

Source 0



Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level


106




3 4 Source 3





projects Source 0

ENT03




4


Source 3


protocol

Source 3

Power

grid

situation

ENG01


5469 Wcapita

Source 4 1018

Wcapita Source 1

1930 Wcapita

Source 1

ENG02

Installed

grid

infrastructur

e

HV 138 MV 612 LV 1392 trafo 557


trafo 620 Source 3

HV 116 MV 678 LV

1299 trafo 035

Source 2


107

Energy

Supply





Legislatio

n

ENL01



ENL02


HTNT + sect14a EnWG

sect14a EnWG 3


must allow time

discrimination and

telemanagement


Source 4


HDO amp

TDD

Source 3


Supplier

awattar Source 0

Real Decreto

2162014

The Voluntary

Price for

Small

Consumers

Source 3

Collectio

n of

Laws

no3142

009 sect23

Source 0


108

(PVPC) is

than 10 kW


Sub-Area

ID Paramete

r Name

GER

Value

GER

Data Source

GER

Level

ESP

Value

ESP

Data Source

ESP

Level

CZE

Value

CZE

Data Source

CZE

Level

Public Network

CSP01 CS

Coverage 0103 CS


Source


4 011 CSEV Source

PRE employees

4

CSP02 CS

Distribution

0000103 CS hab


00000445 CShab

Source PRE

employees

0

CSP03 Charging

Rate

7812 AC-CS

1126 DC-CS

8972 CS combined

Source


3 1926 kWhCS



2 No reliable

source NA NA


109



CS Intelligence

CSI01 Smart vs

Dumb CS

HTB and the

hardware

manufacturer

s we are

mostly

working with

communicate

via OCPP

15

sometimes

16 Most of

our

functionalities

in the

Software

(which can

be classified

as a standard

today)

wouldnt be

available

without 15

Most of the

stations have

Qualitati

ve HTB

private

knowled

ge

2

Most BACKEND




starting this year

Source 1

15 is standard

some CSP have

implemented 16

PRE employees

1


110

the possibility

for direct

paymentAd

hoc charging

Business

CSB01

Impact of personal

offers

- Startstop of

charging

process from

the App or

the Backend

- Short term

reservation

possible

- Scan the

QR-Code on

the station

and

chargepay

with the App

- Register as

customer

with

AppWebsite

- Stationlist

and map

- White label

(for the

CPOMSP)

- many

languages

available

Defintion of

Qualitati

ve HTB

public

knowled

ge Our

direct

custome

rs get to

know all

of those

things

when

we do a

worksho

p or

negotiat

e a

contract

2






drivers

Source 1


Czech Republic by

anyone - No



1


111

tariffs to

make people

drive away

after they

charged or to

charge the

standing time

CSB02




4



register previously



user

Source 3




codes


1

CSB03




3






Source 0

Hubject EVmapa

Main networks


EON PRE

PRE


1


112



roaming project

CSB04




2




Source 2

Some operators

have green

charging option

PRE employees

1


113

fossil sources used

Standardization

CSS01



Source (1)


4




Combo2-DC 19

Source (1)

Source (2)

2

Czech legal norms


2 Chademo



to decrease


Source 3


114


CSL01



Source (1)

Source (2)

2

Law on Climate

Change and Energy



- 2040 only EV sold


circulating

Source 2


- false Gas

stations have to

have CS - false




specific conditions

National

action plan

Source

green

mobility

seminar

1


115


CSL02

Subsidy in

the

developm

ent of

charging

infrastruct


mio for normal CS

(Source (1))

Source (1)

Source (2)

3



Source 3



es and


Source (1)

Source (2)

2


116

ure

(Governm

ent and

investmen

t)


(AC) - max 100





parking spaces

- Cooperation


establishment of CS


E-Wald)




publicly owned


request 212017NP

ŽP 100 mil Kč in

total Loans for

purchases are also

offered by the

government


117









particular usecase



Source (3)





118







1


17971 euro

Source

3 19336 euro Source

2 17971 euro

Source

2


EV




2



emissions




2


climate change

3596666667

ELECTRIFIC survey



2




3


6710




Gap EV Usage




2


119


Gap EV Technology




2


COA01 Acceptance of

constraints




1





2

COA0

3


technology




2

COA0

4




2


120

Enabling seamless electromobility through smart vehicle-grid … · 2020. 5. 3. · H2020 GV-8-2015...

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