Recharge the Future Interim Findings - WordPress.com · Presentation Overview • UK Power Networks...

Recharge the Future – Interim FindingsJack Lewis Wilkinson, Smart Grid Development Engineer, UK Power Networks

Celine Cluzel, Director, Element Energy

Tristan Dodson, Senior Consultant, Element Energy

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2017. UK Power Networks. All rights reserved

Presentation Overview

• UK Power Networks

• The importance of load forecasting

• Recharge the Future: Context, Objectives & Scope

• Key Findings

–Charger Use Study

–Load Growth in London

– Preview of EV Impact

• Next Steps

2017. UK Power Networks. All rights reserved3

About UK Power Networks

Measure Data % of industry

End customers 8.2m 28%

Population served c.20m -

New metered connections* 46,000 32%

Distributed generation connected 8.5GW 31%

ED1 totex allowance (2012/13 prices) £6,029m 25%

Energy distributed 84.8TWh 28%

Peak demand 16GW N/A

Distribution Network Operator

- South East & East of England

- North, East, South & Central London

Will power almost all EVs in licence area

* Average per annum 2010/11-2014/15


Developing accurate &

granular EV load

forecasts

Creating a toolbox for

network planners to

provide fast and

affordable EV

connections

Promoting partnerships

between transport, energy

& City stakeholders, and

encouraging cross

learning

EV Readiness

Pillars


Recharge the Future: Context

• Government Targets:

–All new cars to be electric by 2040

• EVs to have larger impact on load growth

• More accurate peak load forecast needed

• Impact & strategy over next regulatory period (2023-2030) needs to be

reassessed

2018 High Scenario 2030

1%< 32%

EV Parc Share - DfT


The Importance of Load Forecasting

Reinforcement – upgrading the capacity of network assets to accommodate load growth

1. Regulatory period settlements

– Long term forecasts are used to estimate the level of reinforcement spend needed to accommodate growth over a regulatory period

2. EHV Reinforcement Planning

– Traditional reinforcements can take 3-5 years to complete

– Load growth forecasts used to plan these ahead of need, to avoid firm capacity shortfalls

3. LV & HV Visibility Deployment – New EV readiness strategy

– Reinforcements take months to complete so are done reactively

– EV readiness strategy - Forecasts highlight high risk areas, where visibility is installed to identify where & when reinforcement is needed


Recharge the Future: Key Modelling Requirements

• Must be able to model changing load growth on individual

assets.

• It must therefore take into consideration geospatial variations

in:

– EV uptake

– Utilisation of different charging infrastructures

– Charging profiles

Share of cars parked on-street by MSOA.

Source: Element Energy for TfL


Recharge the Future: Project Overview

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1. Charger Use

Study

2. Modelling Tool

Enhancement3. Impact Analysis

➢ Investigate EV charging

behaviour through literature

review, expert consultation

and additional data analysis.

➢ Consolidate findings and

propose recommendations for

modelling charging behaviour.

➢ Update the EV module of the

Element Energy Load Growth

Model, by integrating the findings

from the Charge Use Study

➢ Accurately reflect geospatial

variation in charging behaviour

➢ Develop scenarios to explore

future EV environments

➢ Use updated Element Energy

Load Growth Model and

developed scenarios to conduct

impact analysis on the UKPN

network (work carried out by

UKPN and Imperial College

London)


Charger Use Study: Overview

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Literature Search

• Revealed 71 papers on EV charging behaviour

Literature Review

• Assessed papers for relevance against research questions

• Recorded sources of real-world data used

• Identified 23 real-world EV trials

Data Review

• Assessed data sources for applicability to research questions

• Reviewed identified trial documentation

Knowledge Gap Identification

• Identified gaps in literature for answering research questions

Stakeholder Consultation

• Interviewed industry stakeholders to fill knowledge gaps

Additional Data Analysis

• Reanalysed existing trial data for aspects not originally reported

• Sourced and analysed additional real-world datasets

Datasets analysed

• Plugged-in Places (2010-13)

• Low Carbon London (2013-14)

• My Electric Avenue (2014-16)

• ESB ecars (Nov 2016-Sep

2017)

• ZapMap (Mar 2017 – Mar 2018)

• Electric Nation (interim, Apr

2017 – Feb 2018)


Charge Use Study findings: Home charging

• For commuters, plug-in events are concentrated in the early evening when they arrive

home from work

• BEVs and PHEVs plug in at similar times

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Average plug-in start times, from the Electric Nation interim data


Charge Use Study findings: Home charging

• PHEVs charge at home more often than BEVs

• Average kWh per charge is strongly correlated with battery capacity

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Average home charges per day


Charge Use Study findings: Slow/fast public (≤22kW)

• On weekdays, plug-in start times peak in the morning, midday and evening

• On weekends, there is a broad peak in plug-in events around early afternoon

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Average plug-in start times at slow/fast public charge points


Charge Use Study findings: Slow/fast public (≤22kW)

• Slow/fast public charge points display bi-modal usage behaviour on weekdays

• Long charge events are due to commuters and overnight charging

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Charge Use Study findings: On-street residential charging

• Use of on-street residential charge points in Netherland’s EVnetNL network

is very similar to home charging

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Charge Use Study findings: Rapid public (≥50kW)

• On weekdays, plug-in events peak in morning, early afternoon and evening

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• Average plug-in duration is 30-40 minutes

• Charging frequency is ~once every 3 weeks but is higher for larger batteries

Average plug-in start times at rapid public charge points


Implementing Charger Use Study findings in the Element

Energy Load Growth Model

• EV’s divided into 16 different types, considering:

–PHEV or BEV

–Commuters or non-commuters

–Access to home charging or not

–Urban or rural

• Charging behaviour of each group is modelled, accounting for:

–Mileage and kWh demand

–Share of charging demand met by each charging location type (home,

work, on-street residential, slow/fast public and rapid public)

–Charging start times

–Charging frequency and duration16


EV Uptake Scenarios

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• The model is pre-loaded with uptake scenarios for BEVs and PHEVs. These are provided by ECCo, Element Energy’s car consumer choice model


Load Growth Scenarios

The model is pre-loaded with a number of scenario packages, which represent different self-consistent

views of future EV charging demand.

1. Current Policies

– Medium EV growth and EV charging behaviour remains as it is today. Managed charging at

home becomes increasingly popular as EV stock grows.

2. Unmanaged Growth

– Rapid growth in EVs but with little consideration of impact of charging demand on the grid. This is

a worst case scenario in terms of impact on the network.

3. Managed Growth

– Similar to unmanaged growth but with maximum effort to alleviate grid impacts.

4. Charging Stations

– Rapid growth in EVs and public charging infrastructure. EV drivers show preference for charging

at slow and rapid public charging points, similar to how conventional vehicles are refuelled.

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Next Steps

• Charger Use Study Report – April

• Network Impact Analysis

• Final Report – End of October

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Preview of Final Report: EV Uptake in London

• Recharge the Future gives us a high resolution understanding of where EV

uptake will occur.

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Preview: EV demand density in London

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• Recharge the Future’s state of the art charger use study enables us to understand

where consumers will require electricity in the future

• Study report will be released in August


Preview: Peak Load Growth

• By comparing these two secondary substation load profiles, we can

clearly see how starkly the two EV loads differ

VALE CRESCENT STATION RD 42


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Network Impact

by 2025

Base scenario

Red = No headroom

Green = headroom


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Network Impact by

2025 – Managed

vs Unmanaged

Available capacity

Hot spots - managed

Additional hot spots -

unmanaged

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