Recharge the Future Interim Findings

Transcription

1 Recharge the Future Interim Findings Jack Lewis Wilkinson, Smart Grid Development Engineer, UK Power Networks Celine Cluzel, Director, Element Energy Tristan Dodson, Senior Consultant, Element Energy 1

2 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

3 About UK Power Networks Distribution Network Operator - South East & East of England - North, East, South & Central London Will power almost all EVs in licence area 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 * Average per annum 2010/ /15 3

4 EV Readiness Pillars Promoting partnerships between transport, energy & City stakeholders, and encouraging cross learning Creating a toolbox for network planners to provide fast and affordable EV connections Developing accurate & granular EV load forecasts

5 Recharge the Future: Context Government Targets: All new cars to be electric by 2040 EV Parc Share - DfT 2018 High Scenario %< 32% EVs to have larger impact on load growth More accurate peak load forecast needed Impact & strategy over next regulatory period ( ) needs to be reassessed

6 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

7 Recharge the Future: Key Modelling Requirements Must be able to model changing load growth on individual assets. Share of cars parked on-street by MSOA. Source: Element Energy for TfL It must therefore take into consideration geospatial variations in: EV uptake Utilisation of different charging infrastructures Charging profiles

8 Recharge the Future: Project Overview 1. Charger Use Study 2. Modelling Tool Enhancement 3. 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) 8

9 Charger Use Study: Overview 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 Datasets analysed Plugged-in Places ( ) Low Carbon London ( ) My Electric Avenue ( ) ESB ecars (Nov 2016-Sep 2017) ZapMap (Mar 2017 Mar 2018) Electric Nation (interim, Apr 2017 Feb 2018) 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 9

10 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 Average plug-in start times, from the Electric Nation interim data 10

11 Charge Use Study findings: Home charging PHEVs charge at home more often than BEVs Average home charges per day Average kwh per charge is strongly correlated with battery capacity 11

12 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 Average plug-in start times at slow/fast public charge points 12

13 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 13

14 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 14

15 Charge Use Study findings: Rapid public ( 50kW) On weekdays, plug-in events peak in morning, early afternoon and evening Average plug-in start times at rapid public charge points Average plug-in duration is minutes Charging frequency is ~once every 3 weeks but is higher for larger batteries 15

16 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 duration 16

17 EV Uptake Scenarios The model is pre-loaded with uptake scenarios for BEVs and PHEVs. These are provided by ECCo, Element Energy s car consumer choice model 17

18 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. 18

19 Next Steps Charger Use Study Report April Network Impact Analysis Final Report End of October 19

20 Preview of Final Report: EV Uptake in London Recharge the Future gives us a high resolution understanding of where EV uptake will occur. 20

21 Preview: EV demand density in London 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 21

22 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

23 Network Impact by 2025 Base scenario Green = headroom Red = No headroom 23

24 Network Impact by 2025 Managed vs Unmanaged Available capacity Hot spots - managed Additional hot spots - unmanaged 24