Summer School Smart Energy Systems 2013 Energy … · 5 AIFB + IAI + FZI Research Question /...

KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association

Institute for Applied Informatics and Formal Description Methods (AIFB) FZI Research Center for Information TechnologyInstitute for Applied Computer Science (IAI)

www.kit.edu

Summer School Smart Energy Systems 2013Energy Informatics

Hartmut Schmeck (KIT)

AIFB + IAI + FZI2

Brief intro: Hartmut Schmeck

Chair of Applied Informatics at the Institute AIFB ( University mission of KIT)Director at Institute for Applied Informatics (IAI) ( Helmholtz mission of KIT)Director at Forschungszentrum Informatik (FZI) ( Innovation mission of KIT)

Research Area: Efficient algorithmsBio-inspired optimization (genetic algorithms, ant colony optimization,…)Organic ComputingSmart energy systems (“Energy Informatics”)

Major projects (small selection):MeRegio(Mobil) – using ICT to improve the energy system, integrate EVs and PHEVs into the energy system ( iZEUS, CROME, …)Organic Traffic Control – self-organizing, adaptive traffic light controlObservation and Control of Collaborative Systems– fundamental research on self-organizing systems, generic observer-controller architectures, collaborative learning

KIT Focus: COMMputation (= COMMunication + COMputation)

Hartmut Schmeck | Summer School SES

AIFB + IAI + FZI3

Major Projects …


AIFB + IAI + FZI4

Research Question / Scenario

Pilot Region with ~ 1000 Participants (Freiamt + Göppingen)

(5 chairs at KIT:

Energy Economics, Informatics, Telematics, Management, Law)

• Optimize power generation & usagefrom producers to end consumers

• Intelligent combination of newgenerator technology, DSM and ICT• Price signals for efficient energy

allocation• Combined Heat and Power

• MEREGIO-Certificate: Best practice + information dissemination

Objectives

Partners

Moving towardsMinimum Emission Regions

Energy Technology• Smart Metering• Hybrid Generation• Demand Side Management• Distribution Grid Management

Energy Markets• Decentralized Trading• Price incentives at the power plug• Premium Services• System Optimization

ICT• Real-time measurement• Safety & Security• System Control & Billing• Non Repudiable Transactions


AIFB + IAI + FZI5

Research Question / Scenario

• Intelligent & efficient integration of electric vehicles into the grid

• Technology assessment & feasibility under real life conditions

• Seamless integration into MEREGIO pilot region

• Center of competence at KIT (demo and research lab)

Objectives

Partners

Methodology• Computer Simulations• Field trial with about 100 PEV• Living Lab

(11 chairs at KIT: Electrical Engineering (2), Energy Economics, Informatics (5), Telematics, Management , Law)

[source: EnBW AG]

ICT forElectromobility


AIFB + IAI + FZI6

Further projects

iZEUS – intelligent Zero Emission Urban System (BMWi – ICT 4 EM II)

CROME – Cross-border Mobility with Electric VehiclesFrench-German „electric mobility“

Interoperability of infrastructures

Driving patterns and user response to service infrastructure

Leading Edge Cluster :Electric mobility south-west: road to global market

Integration of vehicle technology, energy technology, information and communication technology as enablers of electric mobility

4 innovation areas: vehicle, energy, information & communication, production

Projects within I&C:intermodal electric mobility, e-fleet management, smart grid integration, green navigation

Helmholtz Energy Alliance „Technologies for Future Energy Grids“

HeGrid – Hybrid Energy Grid Management (EIT ICT Labs activity)




www.kit.edu

Motivation and Need for Energy Informatics

AIFB + IAI + FZI8

European Energy Targets:

Strategic Energy Technology Plan 20-20-20:

March 2007: EU targets to be met by 2020:20% reduction of EU greenhouse gas emissions (relative to 1990)

20% share of renewables of overall EU energy consumption

20% increase in energy efficiency. (relative to 1990)

More ambitious targets of Germany:

Fall 2010:

30% renewables by 2020, 50% by 2030, 80% (??) by 2050

Spring 2011: “Energiewende”

Highly accelerated replacement of nuclear power with renewables (by 2022)


For a sailor, “wenden” means tacking: There won’t be

just one tack!

AIFB + IAI + FZI9

If a tack is not done properly …


AIFB + IAI + FZI10

Problems:Fluctuations – in Demand and Supply

Variations at different time scales, only partially predictable

How to deal with fluctuations? demand and supply management

How to compensate for a „dead calm“??

Dead Calm

Small Scale Short Term Variations

Mismatch


AIFB + IAI + FZI11

German power generation

PVWind

others

Fossile

Nuclear


AIFB + IAI + FZI12

Problems: Power Generation on 27.6.2011, 22.1.2012

27.6.: PV 12 GW, wind 2 GW (peak), nuclear 10,3 GW (steady)

22.1.: PV . 1,8 GW, wind 22 GW (peak), nuclear 5,7 GW (steady)

(source: http://www.transparency.eex.com/de/)


AIFB + IAI + FZI13

Problems with Power Generation : actual and planned on 24.4.2013

24.4.13.: actual: PV 23 GW, wind 6,4 GW (peak),

24.4.13, planned: PV . 17,3 GW, wind 5,9 GW (peak),

How do you deal with these deviations between planned and actualproduction?(source: http://www.transparency.eex.com/en/ )


AIFB + IAI + FZI14

Study on Energy Situation 2050 (Meteorological Base Year 2007)

Source: Fraunhofer IWES

Pow

er in

GW

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec


AIFB + IAI + FZI15

Problems due to decentralization:bottlenecks in the low voltage distribution grid

Local voltage increasedue to PV power infeed

Local voltage decreasedue to EV charging

These visualizations are a result of E-Energy project MeRegio.


AIFB + IAI + FZI16

Impact of PV power input on voltagein the low power grid

Problem: all PV panels of one segment are in sync!

voltage PV power


AIFB + IAI + FZI17

Energy Management:Balancing Demand and Supply

Traditional:

Demand cannot be controlled.

Electricity cannot be stored.

Principle: Supply follows demand(Spinning reserve: Primary, secondary, …)

Future:

Supply only partially controllable and decentralized

Potential reversal of power flow

New Principle:Demand has to follow supply!

Requires more flexible demand

SupplyHV

MV

LV

Demand

SupplyHV

MV

LV

Demand

SupplyHV

MV

LVDemandSupply

SupplyHV

MV

LVDemandSupply


AIFB + IAI + FZI18

Integrated Future Energy System

Transmission grid

Distribution grid

Information flow(Energy information network with distributed system intelligence)

Energy flow (electricity)

Flaute

Kurz‐ und längerfristige Fluktuationen

Ungleich‐gewicht

Spannungserhöhung durch PV

Spannungsabfall durch E-Auto


Collection grid

AIFB + IAI + FZI19

Physical power grid

ExtraHigh

VoltageLevel

Privatkunden

Industriekunden

Gewerbekunden

High

VoltageLevel

Medium

VoltageLevel

LowVoltage

Level

SLP

SLP

SLP

RLM

RLM

Energy ‐provider

Transmission System ‐Operator

Distribution System‐Operator

Metrering System‐Operator

Energy ‐Consumer / provider

SLP


AIFB + IAI + FZI20

Communication in Current Energy Market

Private customer

Industry customer

Business customer

Energy ‐provider

Transmission System‐Operator

Distribution System‐operator

Metering point‐operator

Energy consumer‐and provider

MSB

MSB

MSB

VNB(DSO )

VNB(DSO )

ÜNB(TSO )

pro ‐vider‐

‐‐

provider

Control power plant RLM

LM


AIFB + IAI + FZI21

Communication in Tomorrow‘s Power Grid

Energieerzeuger‐Und ‐verbraucher

Regelkraftwerk


AIFB + IAI + FZI22

Numerous possibilities for vulnerabilities ...

d


Regelkraftwerk

AIFB + IAI + FZI23

Where should “system intelligence” be located?What do we have to communicate?

Power flowCommunication

Power provider (utility)EEX or other markets

Substation(transformer) (20kV / 0,4kV)

PV-panels

E-car

Power generators

BGM

WaMastove

PV

CHP

DSL

CB

IM


AIFB + IAI + FZI24

Wind Power Plant

Integrated Hybrid Gridspower gas heat

Power storage

Power plants

Power transmission grid

H2 / methanation

Bio-PPSteam Power Plant

Gas transmission gridGas storage

Thermal storage

Power distribution grid Gas distribution grid

Heat distribution gridPower storage

Wind

CHP boilerPV E-Mobility H2-

Mobility

biogas Gas bufferH2 / methanation

Bio-CHP

Heat pump

GT

GT

power management

gas management

heatmanagement

Gas &Steam PP

Integrated energymanagement


AIFB + IAI + FZI25

Integrated Energy Management Systems

Balancing demand and supply within each grid

Energy conversion in between gas, power, and heat

„real conversion“ of power to gas e.g. by electrolytic methods (H2) and methanation in order to consume overflow of power supply from wind power plants

„virtual conversion“ of power to gas in bivalent systemse.g. by switching between gas boiler and electric boiler

Interoperability of energy management systems for power, gas, and thermal grids ( standardized interfaces? project HeGrid)

Integrated energy information grid with distributed system intelligence in order to increase the efficiency, flexibility, and stability of the combined grids.


AIFB + IAI + FZI26

Tomorrow’s Energy Management

Challenges

Discover and exploit degrees of freedom and leeway for demand (and supply) management.

Need for autonomic/organic energy management without reducing personal comfort or industrial productivity

Develop new ways of storing (electric) energy

Batteries

Power to gas to power

Virtual storage

Strong need for intelligent demand and supply management to increase the reliability of power supply in spite of fluctuating, decentralized and uncontrollable generation of power from renewable sources.

Strong need for load flexibility and load shifting


AIFB + IAI + FZI27

Requirements on information about the grid

Information on the current state of relevant components

Energy consumers

Energy producers

Storage („Prosumer“)

Grid components (Substations, cables,…)

Which information?

Voltage (+ current? + frequency?)

Power

Phase shifts (cos )

Current degrees of freedom wrtAmount of consumption / production

Time period for consumption / production

For whom?

Next level, needing this information for decisions on scheduling components or, more general, on managing the energy system.


AIFB + IAI + FZI28

Requirements on communication

Exchange information on the current state with those who need this information for performing their duties:

Next level energy manager (distribution system operator, energy supplier (trader), demand side manager,…)

Partner in the energy system who is involved in cooperation.

Communicate derived data:demand/supply forecast values, price and control signals

“Day-ahead”, “intra-day”,… price signals

Control signals for the anticipated use of components in the energy system


AIFB + IAI + FZI29

Typical questions

Which and how much information is needed?

On each consumer/producer or aggregated over many?

On which time scale? Real-time? Every second, minute, x minutes, hours?

How to communicate?

Using the power grid (Powerline? Digital current?)

Using a data grid (fibre? DSL? phone? Public vs. private data grid?…)

Wireless (WLAN, GSM, GPRS, UMTS, LTE, zigbee, …)?

Where is the information processed?

Decentralized (within a house? an EV? a grid segment?)

Centralized (in a balancing group? at the power supplier?At the distribution system operator?)

Which system architecture is needed?

Central, decentral, hierarchical,

Tree-like, mesh-like topology?

Parallel, multi-core, distributed, cloud, real-time?


AIFB + IAI + FZI30

Typical questions

Which are the most appropriate concepts for the control of demand and supply of electrical (and thermal) energy?

Market mechanisms

Exact or approximate planning and optimisation

Strictly local versus strictly global versus hierarchical, Trade-offs

What are the objectives?Balancing demand and supply

Generation of system services (reactive power, balancing energy,..)

Reduction of energy consumption, efficiency vs. flexibility

What about data protection and privacy??Anonymisation / Pseudonymisation

Traceability

Which problems arise wrt security and safety?

Access protection, detecting and preventing attacksDependability / manipulation of data,

Robustness issues (wrt missing data, erroneous data, …)

Stability


AIFB + IAI + FZI31

Some essential tasks for ”ICT for energy”(or for “Energy Informatics”)

Communication of essential information between the relevant „entities“ in thegrid

Efficient and dependable information processing for various tasks

Prediction of power demand and supply

Modeling and simulation of the energy sytem

Planning and optimisation of grid operations

Support of market operations (trading agents,…)

Demand and supply management

Control of batteries of various types of EVs (charging, power-feedback)

Virtualisation of components (virtual power plants, virtual storage, …)

Support of system services (reactive power, demand response,…)

Support of emergency actions (failures, power outages,…)

Authentication, roaming, accounting, billing,…

Various services for integration of electric mobility into the grid

Various issues wrt security and safety


AIFB + IAI + FZI32

Necessary competences in Informatics

Algorithms and data structures, optimisation methods

Information processing in distributed and parallel systems

Communication protocols

Software architectures

Service-oriented computing (Cloud etc.)

Concepts for self-organising systems(autonomic/organic computing, multi agent systems, architectures and methods)

Validation / verification of software + hardware

Security concepts, cryptographic protocols, access protection

Dependability, fault tolerance, trustworthiness, robustness, stability

…


AIFB + IAI + FZI33

The evolution of the grid: the Internet of Energy

The electricity grid: from a rather unflexible consumption-orientedsupply chain with passive consumers …

… to a new dynamic interaction architecture with active „prosumers“

Needs the real-time crosslinking of all grid components and participantscombined with an intelligent self-organisation!


Generation Transfer Consumption

Generation Transfer

Consumption Storage

AIFB + IAI + FZI34

What do we mean by “the Internet of Energy”?

There is a global addressing scheme (IP-based) for uniquely addressing all the components of the energy grid.

There is a common protocol (TCP/IP and related) for communicatinginformation between the components of the energy grid

There are standard services for operating the energy grid.


Generation Transfer

Consumption Storage

AIFB + IAI + FZI35

Remarks

The challenges of the upcoming and inevitable transformation of theenergy system needs a transdisciplinary approach

Informatics ( new discipline „Energy Informatics“)

Power Engineering

Control Engineering

Communication Engineering

Law

Without significant contributions from Informatics (or „ICT“) thistransformation will not be feasible.

E-Energy animation: see http://www.e-energy.de/en/animation/ for a nice and comprehensive animation of the German view of a future smart grid

Animation by acatech (in German): http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/E-Energy/acatech_smartgrids_09web_120127.mp4




www.kit.edu

Electric Mobility and Smart Homes

AIFB + IAI + FZI37

German National Development Plan for Electric Mobility

Phase 1

Market-/ Technologie-preparation

Phase 2

Market development

Phase 3

Volume market

2009 - 2011

2016 - 2020

2011 - 2016

Goal for 2020:

• 1 Mio. E-Vs in DE

• DE is lead market forE-Mobility

Development of battery technology and competencecenters in DE

Provisioning of an interoperable and large-scalecharging infrastructure

Series production of Battery electric vehicles (BEV) andPlug-In electric vehicles (PHEV)

Development of business models

Development of battery technology and competencecenters in DE

Provisioning of an interoperable and large-scalecharging infrastructure

Series production of Battery electric vehicles (BEV) andPlug-In electric vehicles (PHEV)

Development of business models

2030: 6 Mio EVs30 % renewable energy


AIFB + IAI + FZI38

Relevant Properties of Electric Vehicles

Germany, 2008 (mobility survey): Average daily car usage < 1 h, 94% of trips < 50 kmAverage net capacity of currently available EVs: 20 kWh

At 1 Million BEVs (German objective for 2020):available storage capacity of ~ 20 GWh

At charging/discharging power of 3.7 kW: ~ 3.7 GW potential power

Consequently: high demand for power, potentially also high supply (if power feedback is possible)

Average time for charging:Single phase 3.7 kW: 5 to 7 hours.Three phase 10 kW: ~ 2 hours (but high risk of grid overload!)

Potential of high flexibility for load shifting, but also potential of high peak load!

Using intelligent control leads to high potential for stabilizing the grid.


AIFB + IAI + FZI39

Integration Strategies: Load Balancing Potential

-50

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06

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12

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Po

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P in

kW

EV <-> Grid Exchange Charging/Infeed

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100

1500

0:0

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06:

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12:

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18:

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00:

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original grid load curve

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Controlled EV charging

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Po

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k

W resulting load curve

1 2

3 4

Uncontrolled EV energy charging


Flexibility of battery charging could allow togenerate a range of different demand profiles

AIFB + IAI + FZI40

Energy Smart Home Living Lab at KIT

Energy Mgmt. Energy Mgmt. System


AIFB + IAI + FZI41

Solar inverterSmart Meter

EMP

Charging station

Intelligent appliances CHP

EMP

EMP

A/C

Observes and controlselectric/thermal

consumers & providers

EMP

EMS

KIT Energy Smart Home LabEnergy

Management Panel (EMP)

Visualisation ofenergy usage

Discover userpreferences


AIFB + IAI + FZI42

KIT- Smart Home ScenarioDecentralized power plants

Smart meter

Inhouse touchscreens

Personal computer / smart phones

Sensors

Intelligent and classic household appliances

Personal charging station

Energy provider

Car driver

data


AIFB + IAI + FZI43

KIT- Smart Home Scenario

Intelligent appliancesCommunicate with central control and with each other.

Know (and communicate) their current state.

May respond to control.

Electric carConnected to the home as a mobile storage

Bidirectional utilization (charging/discharging)

Large consumer/supplier

Decentralized power generation (PV/CHP)

PCM elements in ceiling (cooling) (Phase-change material)

Simulation component (“4-Quadrant amplifier”)

Reduced but effective interaction between human, home management, and devices

Discover and exploit degrees of freedom for energy control

Home Device

Human


AIFB + IAI + FZI44

MeRegioMobil - Electric VehiclesOpel Meriva MeRegioMobil (3)

A-Class E-CELL (Daimler)

bidirectional power connection

~ 40 Smart ed

uni-directional power connection


AIFB + IAI + FZI45

Current KIT Research Demonstrates how Electric Cars Could Support the Electricity System.


Mercedes-Benz E-CELL incl. feed-in function

KIT Smart Home

Opel Meriva BEV incl. feed-in functionCharge Stations

(by EnBW and KIT)

research onvehicle to home and vehicle to grid

Charge protocol: ISO15118enhanced for feed-in control

AIFB + IAI + FZI46

Stakeholders of ISO15118 are Customers, OEMs and the Unbundled Energy Utilities.


Energy Storage

Generation

Energy Trader

Energy suppliersPrivate Customers,

possibly with decentralized generation

Business customers

(Transmission) System operator

(TSO)

Distribution system operator

(DSO) Public charging infrastructure P

ictu

re s

ourc

e La

dest

atio

n: E

nBW

feed-infeed-inconsumptionconsumption

Balance responsible parties

Car manufacturers(OEMs)

AIFB + IAI + FZI47

An Important Success Factor is the Availability of Supported, Practical, International Standards.

ISO 15118 describes how communication of battery electric cars and charge stations should work

Current participation in ISO 15118 joint working group

OEMs

Energy utilities


Source: Tim Schlüsener (Daimler AG), Impulsvortrag SmartCharging, Lebenswelt Elektromobilität, Mannheim. 10.09.2011

Automotive suppliers

Universities/Research

AIFB + IAI + FZI48

Car Manufacturers are Focused on Battery Protection and Rentable Value Added Services.

Battery protectionNo external direct controlSoC and SoH information not accessible to third partiesCharge session incl. battery care

User experienceSupport maximum range

Value added servicesPreconditioning of the vehicle cabin is a comfort and range extending feature

Chevrolet OnStar/MyVoltMyFord, Nissan Leafsmart/Mercedes E-CELL

Routing servicesEnergy supplier, All-inclusive packages


(right pic): Hawk Asgeirsson (DTE Energy Co.), Plug-in Electric Vehicle Activities and AMI, EPRI IWC Infrastructure Working Council. Tempe, AZ, USA. 08.12.2010

Sources: (left pic.): Chevrolet OnStar Android App

Smart Phone Apps with preconditioning control

AIFB + IAI + FZI49

Electric Cars Will Support Optional Departure Time Settings by the Driver (in car, by phone, www).


ImmediateCharge starts upon plug-in.Delayed – Departure TimeCharge your vehicle based on departure time.Delayed – Rate and Departure TimeCharge your vehicle based on departure time and utility rates.

Customer portal „MY VOLT“of GM / Chevrolet (since 2010)

Pic.Source: www.autoparts-marketplace.com/2011/09/resins-aim-to-eliminate-corrosion-issues-in-automotive-applications.html

AIFB + IAI + FZI50

External Control Signals Will Be Sent by a DSM

Common term Demand Side ManagerFocusing on demand

Prosumers not considered (PV, CHP)

Enhanced DSM, also managing decentralized generation(Decentralized) Demand and Supply Manager: DDSM


Home Energy Management

Energy Supplier

DDSM Function

Publiccharge spots

Private chargespots

Business and semi-public charge spotsBusiness Energy

Management

Charge station operator

AIFB + IAI + FZI51

Charge Sessions Consist of Start, Charging, End and Optional Feed-In.

Time t

PowerP


Plug in vehicle

Departure and start trip

Power consumption

Optional feed-in

End of charge

AIFB + IAI + FZI52

V2G-Enabled Vehicles Could Provide Negative and Positive Balance to the DDSM.

Positive balance energy

Negative balance energy


Which parameters are necessary to fit the requirements of all stakeholders?

Time t

PowerP

AIFB + IAI + FZI53

Charge Station and Grid Set the Technical Constraints

Maximum power charge station

Time t

PowerP


Technical maximum possible power

Load balancing of stations / overload protection

Overload

Maximum power V2G charge station

Data flow1

AIFB + IAI + FZI54

The Vehicle Knows a Set of Technical and User Requirements Affecting the Charge Session.

Maximum power charger

Filling minimum energy amount

Minimum power of charge system

Cabin Pre-conditioning

Departure Time

Time t

PowerP


Vehicle driver defines a minimum energy amount (e.g. to reach next hospital)

Minimum power – EV needs at least energy to run the charge system

Pre-conditioning during the charge session (save energy, extend range)

Departure time is known, Charge 100% SoC

Maximum feed-in power inverter

AIFB + IAI + FZI55

The Charge Schedule Is Used to Abstract allEV-Internal Boundary Conditions.

Time t

PowerP


Data flow

2

Minimum power

Maximum power

Preferred power EV

Maximum power V2G

Energy amount

Energy amount V2G

EV starts charging.

AIFB + IAI + FZI56

The DDSM Sums up all EVs and Computes Necessary Load Shifting Actions.

Time t

PowerP

Purchaised powerresp. scheduled power

Short-time load prediction

supply surplus

excess demand


AIFB + IAI + FZI57

Time t

PowerP

Minimum power

Maximum power

Preferred powerEV

Preferred powerDDSM

Necessary Load Shifting Will Be Realized by EVs resp. by all Controllable Devices.


AIFB + IAI + FZI58

Time t

PowerP

The DDSM Sends a Particular Preferred Load to Every Selected EV (incl. Feed-in, If Necessary).


3Data flow

Preferred power DDSM

AIFB + IAI + FZI59

A New Charge Schedule Will Be Sent by the EV Which Depends on the Preferred Power by DDSM.

Time t

PowerP


Data flow

4

Minimum power

Maximum power

New, preferred power

Maximum power V2G

Energy amount

Energy amount V2G

AIFB + IAI + FZI60

Resulting Optimized Battery Charging for EVs

power[kW]

t0 teocEVMinPowerDischarge

EVSEMaxPowerDischarge

EVMaxPowerDischarge

EVSEMaxPower

EVMaxPower

EVMinPower

ScheduledPower

PMax

EVSEMinPower

PMaxDischarge

Shifitngcharging /

dischargingtimes

Grey area = tech. flexibility

PPreferred

Time [h]

Constrained by power grid, charging station, EV, and user (driver) towards a standardized protocol ISO 15118


AIFB + IAI + FZI61

Remarks on EV Charging

Batteries of electric vehicles will provide significant load flexibility.

The load management of BEVs requires a sophisticated protocol fornegotiating the load profile wrt the requirements of all stakeholders.

Several other approaches to load management have been suggested, based on specific scenarios

At private charging spots: supporting local V2G applications

At semi-public charging spots: supporting demand response applications

For fleet management: probably the first major users of BEVs will be in commercial traffic.

Inductive charging will provide interesting alternative to cable-basedcharging.


AIFB + IAI + FZI62

KIT- Smart Home Scenario

Intelligent appliancesCommunicate with central control and with each other.

Know (and communicate) their current state.

May respond to control.

Electric carConnected to the home as a mobile storage

Bidirectional utilization (charging/discharging)

Large consumer/supplier

Decentralized power generation (PV/CHP)

PCM elements in ceiling (cooling) (Phase-change material)

Simulation component (“4-Quadrant amplifier”)

Reduced but effective interaction between human, home management, and devices

Discover and exploit degrees of freedom for energy control

Home Device

Human


AIFB + IAI + FZI63

Hardware in the Loop Simulation

grid simulation(power factory)

4Q-controller

data

sensor


AIFB + IAI + FZI64

Focus of our Approach

Demand-side load managementAppliances are re-scheduled based on external 24h-signal

Management of the load- and feed-back-process of the electrical vehicle


AIFB + IAI + FZI65

Classification by Degree of Freedom

Degree of FreedomCapability of re-scheduling the appliances’ work-item

ClassificationAppliances with a poor degree of freedom

Re-scheduling not possible

Re-schedulable appliancesBidirectionally on the timeline

(e.g. deep freezer, electric heating)Backward on the timeline

(e.g. hot-water boiler)Forward on the timeline

(e.g. washing machine and dish washer)


AIFB + IAI + FZI66

Classification of Household Appliances

per

man

ent

serv

ice

dee

pfr

eeze

r

el.

heat

ing/

air

cond.

war

m w

ater

boile

r

tim

ed s

ervi

ce

dis

hw

asher

was

hing

mac

hine

drye

r

controllable

un-p

redi

ctab

le

multim

edia

light

ing

smal

l

appl

ianc

es

pred

icta

ble

stov

e

heat

ing

plat

e

smal

l

appl

ianc

es

appliance

observable


AIFB + IAI + FZI67

24-Hour Signal

Time dependent price signal Communicated by the energy provider (e.g. multicast)

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10

20

30

40

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

rating

Source (pic): EnBW MeRegio


AIFB + IAI + FZI68

Typical Schedule

A typical schedule is predicted by the Energy Management System based on a large amount of measurement data

0

500

1000

1500

2000

2500

3000

3500

4000

4500

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

breadmaker

stove

dishwasher

toaster

stove

deepfreezer

toaster

washingmachine

dishwasher


AIFB + IAI + FZI69

0

500

1000

1500

2000

2500

3000

3500

4000

4500

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

breadmaker

stove

dishwasher

toaster

stove

deepfreezer

toaster

washingmachine

dishwasher

0

500

1000

1500

2000

2500

3000

3500

4000

4500

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Intelligent Demand Management

Original schedule

Optimizedschedule

Bread machinestarts earlier

Dishwasherpostponed Washing machine

and dish washerpostponed

Stove usagecannot be moved

therefore: power infeed from car

battery!

Battery may berecharged at

night


AIFB + IAI + FZI70

Discovering Degrees of Freedom:Energy Management Panel

Transparent information on energy consumption

Discover and specifydegrees of freedom forenergy consumption


AIFB + IAI + FZI71

Energy Management PanelEnergy Consumption and PV-System View


AIFB + IAI + FZI72

Energy Management PanelDevice Dashboard

Kitchen


AIFB + IAI + FZI73

Energy Management PanelExample Tumble Dryer

Kitchen

Automatic start: 18:00h

Programmedstate

Forcedstart

Degree of freedom


AIFB + IAI + FZI74

Energy Management PanelEnergy Supply and High Power Consumers

Source (PCM pic): http://www.cryopak.com/phase-22-insulated-shipping/

Phase changematerial in the ceiling


AIFB + IAI + FZI75

Energy Management PanelEnergy Flows and Electric Vehicle


AIFB + IAI + FZI76

Energy Management System – EMS

Observe+ Predict

Learn +Control

Communicate

Observe+ Control


AIFB + IAI + FZI77

Origin of Organic ComputingWorkshops of the GI-/ITG-Sections on Computer Engineering in 2002

• Information technology is movingtowards the ubiquitous networked computer.

• Complex ubiquitous systems need new concepts for organization and user interfaces to remain manageable and controllable.

• Future computer systems have to be designed with respect to human needs.

• Future computer systems have to be robust, adaptive, and flexible.

• Future computer systems have to be self-organized but trustworthy.

• Systems having these properties show life-like behavior

Hence, we call them Organic Computer Systems.

• Based on range of other initiatives: ubiquitous, autonomic, …


AIFB + IAI + FZI78

It is not the question, whether adaptive and self-organising systems

will emerge, but how they will be designed and controlled.

Organic Computing

Self-adaptive? Emergent control?Self-organized?

Policies?Feedback control?

Autonomic?Self-managing?

Feedforward control?

Model-predictive?

Model-based?

Controlled-self-organized?

You-name-it…

Meta-data?


AIFB + IAI + FZI79

DFG priority program 1183 „Organic Computing“ (2005 – 2011)3 phases of two years each, around 18 projects, ~2 Mio € per year

www.organic-computing.de/SPP

German Framework for Research on OC :Christian Müller-Schloer, Hartmut Schmeck, Theo Ungerer

Nature

Dissip. Struct.AntsSwarmsBrain….

(1) OC-Principles

AutonomySelf-x.EmergenceAwarenessCooperationCompetition…

(2) OC-Technology

OC-toolbox

Basic technologiesObserver/ControllerGuardingHelper ThreadsEmbedded LearningComplete system arch.…

(3) TechnicalApplications

CarOfficeTelecomFactoryHomeHealth&CareEnergy...

Emergence Self-x


AIFB + IAI + FZI80

IBM’s MAPE cycle for autonomic computing

MonitorAnalyze

Knowledge(called “autonomic element”)

System under observation and control (SuOC)

A set of interacting elements/agents.

Does not depend on the existence of observer/controller.

Distributed and/or central observer/controller-architecture

Driven by external goals

Multilevel organization

(Generic) Concepts for Control of Self-organising Systems

K

productive systemsensors actuators

SuOCinput output

goals

organic system

controllerobserver

obse

rves

controls

selects observation model

reports

observation model

systemstatus

□Plan□Execute


AIFB + IAI + FZI81

Generic O/C-ArchitectureJ.Branke, M.Mnif, C. Müller-Schloer, U. Richter, H. Schmeck 2006

model selection

observer

pre-processor

data analyzer

monitor

emergence detector 1

aggregator

individual datasystem data

emergence

detector 2

...

time-space-pattern

raw datapredictor

statistics

...

cluster prediction

log file

system under observation and control (SuOC)

model of observation

select

select

select

select

controller

mapping

action Ai

actionparameters

situation

parameters

goal / objectives

system status

evaluation

history

history

∆t

fitness

situatio

n p

arameters

action selector

Ci Ai

Fi

adaptation modulesimulation model

monitor

filter

analyzeadapt

mapevaluate

store

predict

direct


AIFB + IAI + FZI82

1. Central: One observer/controller for the whole system.

2. Distributed: An observer/controller on each system component.

3. Multi-level: An observer/controller on each system element as well as one for the whole system.

Realisation of OC systems

• adaptive• top-down

• self-organising• bottom-up• emergent control

• controlled self-organising

• bottom-up / top-down

observer controller

SuOC

observer controller

SuOC

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O CSuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C

SuOC

O C


AIFB + IAI + FZI83

Layer 0 orsdetectorstraffic

traffic light

controller

traffic light

controllerLayer 0 orsdetectors

traffic

traffic light

controller

traffic light


traffic

traffic light

controller

traffic light


traffic

traffic light

controller

traffic light


traffic

traffic light

controller

traffic light

controllerLayer 0

orsdetectors tp

system component

ercontroller

componentp

componentoutput

model selection

observer

pre-processor

data analyzer

orm

onitor


aggregatoraggregator

individual datasystem data


...

time-space-pattern

raw data

predictor

statistics

...

cluster prediction

elog file

system under observation and control (SuOC)

controllercontroller

mapping

action Ai

nactionsituation

parameters

situation

parameters

objectives

ationevaluation

history

history

∆t

fitness

situatio

n p

arameters

action selector

Ci AiFi

adaptationsimulation

observation model

observation model

select

select

select

select

Layer 1

1Controller 1

“LCS”er 1observer 1rule set

detector

informationparameters

Layer 1

1Controller 1


detector


Layer 1

1Controller 1


detector


Layer 1

1Controller 1


detector


Layer 1

1Controller 1


detector


Layer 1 1controller 1er 1observer 1rule set

detector


Layer 2 er 2observer 2

ratorrule generatorEA

simulator

user

new rulesituation,

quality of control



simulator

user

new rulesituation,

quality of control



simulator

user

new rulesituation,

quality of control



simulator

user

new rulesituation,

quality of control



simulator

user

new rulesituation,

quality of control

Layer 2er 2observer 2


simulator

user

new rulesituation,

quality of control

Different view on O/C-architecture

Action generation

Action selectionand evaluation

Internal system control

On-line learning loop

Off-line learning loop


AIFB + IAI + FZI84

Organic Computing …

… has been applied to broad range of technical applications, in particular to

Traffic control (self-adaptive traffic lights, self-organizing progressive signal systems, routing recommendations,…)

Production systems (adaptive production lines, fault tolerance, reconfiguration)

Smart camera systems (supervisory applications)

Network control

Systems on chip

Energy management and control


AIFB + IAI + FZI85

Architecture of Energy Management System

Combination withEEBus (Kellendonk)


AIFB + IAI + FZI86

EEBus and O/C-Architecture


AIFB + IAI + FZI87

Improving self-supply by load management

Ratio of self-supply with power from PV and -CHP(typical profile of a 5-person household, without stationary batteries)

Non-optimizedoptimized

Source: F. Allerdingweeks of a year


AIFB + IAI + FZI88

Improving self-consumption by loadmanagement

Ratio of self-consumption of power from PV and -CHP(typical profile of a 5-person household, without stationary batteries)

nicht optimiertoptimiert

Source: F. Allerding

Non-optimizedoptimized

weeks of a year


AIFB + IAI + FZI89

Distributed Optimisation (F. Allerding)

18:00

17:00

SubproblemWaMa

Subproblem

DishWa

SubproblemCHP

Glob

al optim

isation

partial solutionWaMa

partial

solution

DishWa

partial

solution CHP

Start: 16:00

Start: 15:30

Start: 15:30 & 17:002h

Goals and external signals


AIFB + IAI + FZI90

Distributed optimisation: process view

TPTP

SP

TPTP

SP

SPA

OptOpt

SP

Evolutionary Algorithm


AIFB + IAI + FZI91

Evolutionary Algorithm

Selection

Recombination

Mutation

EvaluationGenerate Iintial

population

Choose

Best solution

Stopping criteria?

Coded valuesubproblem a

Coded valuesubproblem b

Individual


AIFB + IAI + FZI92

Constructing an abstract subproblem

Bit vector of length k

Evaluation function- Input: k bit

- Output: resulting loadprofile

Back transformation- Input: k bit

- Output: concrete solution(e.g. starting time)

Stores the current solution vector(e.g. representing a strarting time oa a device)

Determines the resulting load profile of thehousehold component based on the currentvalue of the bit vector.

Generates a concrete instruction for thehousehold component from the current valueof the bit vector.(e.g. the starting time)


AIFB + IAI + FZI93

Subproblem in more detail

rj dj

time

power

Degree of freedom tDoF

dj-pj

Dividing tDoF into timeslotsand mapping to k bit

=> Shifting the start by t*

Bit vector of length k

Evaluation function

Back transformation function

010011 => rj + t* = tstart

Subproblem forcomponent j

rj :Release Time of component j

dj : Deadline for component j

pj : duration of task of component j

Example:rj = 14:00hValue of bit vector is number of minutesk = 6 bit (= degree of freedom max. 63 min)bit vector = 010011 ( t*= 19) => starting time for component j : 14:19h

t*: Shift of starting time tstart

tStart


AIFB + IAI + FZI94

Generating the individuals

SubproblemWaMa

SubproblemDishWa

SubproblemCHP

10 bit

8 bit

43 bit

10 bit8 bit

43 bit

Individual


AIFB + IAI + FZI95

Optimisation in more detail

Initial/current population

Selection

Recombination/Mutation

SubproblemWaMa

SubproblemDishWa

SubproblemCHP

Evaluation


AIFB + IAI + FZI96

Optimisation in more detail

SubproblemWaMa

SubproblemDishWa

SubproblemCHP

Fitness function

Evaluation of the individual

P

P

P

t

t

t

P

t


AIFB + IAI + FZI97

Stopping the optimisation

Initial/current population

Selection

Evaluation

Stoppingcriteria

Best individual of last generation

Recombination/Mutation


AIFB + IAI + FZI98

Back transformation

SubproblemWaMa

SubproblemDishWa

SubproblemCHP

Decodesolution

Decodesolution

Decodesolution

at local O/C-unit Real starting times


AIFB + IAI + FZI99

FZI House of Living Labs (HoLL)


Space > 2.000 m² for lab-, office- and presentation purposes

Extension and integration of existing FZI Living Labs

AAL - Ambient Assisted Living

Automotive

MobileIT / SatNav

Service Robotics

New FZI Living Labs

smartEnergy

smartHome

smartAutomation

smartMobility

Innovations at the interfaces of the research topics

Implementation and Evaluation of „real-life“-scenarios

AIFB + IAI + FZI100

FZI House of Living Labs (HoLL)


LL smartHome

Office

LL smartAutomation

LL smartEnergy

LL smartMobility

Presentation

AIFB + IAI + FZI101

HoLL - smartEnergy


LL smartHome

LL smartMobility

LL Service-Robotic

LL smart Automation

Energy Management (EM)

Utility, DSO Grid’s state User

LL smartEnergy

AIFB + IAI + FZI102

Decentralized energy supplyPhotovoltaic (15 kWp)

Stationary electrical storage (30 kWh)

CHP-unit (5,5 kWel, 12 kWth)

Adsorption chiller (9 kWth)

Condensing boiler (100 kWth)

Mobile energy storage via bidirectional connected EV

Thermal energy storage (decoupling of supply and demand related to heating and cooling)


Intelligent consumptionLoad shifting of electrical and thermal appliances in the smartHome

Respecting user constraints

Sensors and actuators for automatic EM of electrical and thermal consumers in the office (building systems for heating, cooling and electrical consumers)

smartAutomation (monitoring and load shifting of diverse processes)

smartMobility

smartEnergy Infrastructure

Integrated Energy Management System across the whole HoLL by flexible combination of energy supply, consumption, and storages

• Development of homogenous interfaces for the system integration

AIFB + IAI + FZI103

Communication


EIB/KNX Zigbee HabiTEQPLC

Miele@home

EMS (for office and residential buildings)

REST(XML, HTTP)

electric and thermal energy supply

smart and conventionalhousehold appliances

electrical and thermal energy storages

flexible power consumption

system services

A/C

usergrid operatorelectric utility

smart meter

smart plugs

AIFB + IAI + FZI104

Controlled self-organizing energymanagement

internet

Shift imbalances to a later point

Handle device restrictions decentralised

consumptionproduction

time

po

wer

Organic / Autonomic energy managementSignificant reduction of the need

for balancing energyPrimary control by Elite GroupSecondary control by pool

“elite group”


AIFB + IAI + FZI105

Summary ICT 4 EV & Smart Home

Power generation from renewable sources needs ICT fornew approaches to energy management.

Electric vehicles will generate significant capacity for power storage –leading to additional demand and supply of power.

Integration of EVs into smart home environments allows for intelligent balancing of power demand and supply and for new power system services.

An “Internet of Energy” will have to cope with similar safety and security problems as the “Internet of Data”.


AIFB + IAI + FZI106

Summary on „Energy Informatics“

Tomorrow‘s energy system crucially depends on appropriateinformation at the right time, at the right place, with appropriate content.

Future energy management and grid control systems need an adequate integration of control and information processing.

An appropriately designed energy information and control grid will beone of the backbones of future integrated energy systems

Stability and security of are major challenges for future highlydecentralised energy grids

Those who are experts in both

- information and communication technologyand

- control and power engineering

will have a brilliant future!!


AIFB + IAI + FZI107

Contact Address

Prof.Dr. Hartmut SchmeckKIT Campus SouthInstitute AIFB76128 KarlsruheGermany

[email protected]: +49-721 608-44242Fax: +49-721 608-46581

www.aifb.kit.edu www.iai.kit.edu www.fzi.de

www.commputation.kit.edu

http://meregio.forschung.kit.edu/english/

http://meregiomobil.forschung.kit.edu/english/

www.izeus.kit.edu


Summer School Smart Energy Systems 2013 Energy … · 5 AIFB + IAI + FZI Research Question /...

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Transcript of Summer School Smart Energy Systems 2013 Energy … · 5 AIFB + IAI + FZI Research Question /...