Black Cab Green - innovation.ukpowernetworks.co.uk · The Black Cab Green project makes the first step of modelling the impact on power networks of black cab and private hire vehicle

Black Cab Green London’s Electric Black Cab and Private Hire Future Version: 2.0 Date: April 2018

Black Cab Green London’s Electric Black Cab and Private Hire Future

UK Power Networks (Operations) Limited. Registered in England and Wales. Registered No. 3870728. Registered Office: Newington House, 237 Southwark Bridge Road, London, SE1 6NP Page 2 of 72

Contents

Executive Summary ...................................................................................................................................... 6 Acknowledgements ..................................................................................................................................... 12 1 Background ........................................................................................................................................ 13 2 Approach ............................................................................................................................................ 15

2.1 Project Outputs ....................................................................................................................... 16 2.2 Project Structure ..................................................................................................................... 16 2.3 Key Assumptions .................................................................................................................... 17

3 Licence Mapping to UKPN substations .............................................................................................. 19 3.1 Clustering ................................................................................................................................ 21

4 Vehicle Details.................................................................................................................................... 23 4.1 Black Cabs .............................................................................................................................. 23 4.2 Private Hire Vehicles ............................................................................................................... 25

5 Modelling Scenarios ........................................................................................................................... 26 6 Charging Profiles ................................................................................................................................ 28

6.1 Unconstrained Charging Scenario Profiles ............................................................................. 28 6.2 Optimised Time-Shifting Scenario Profiles ............................................................................. 33 6.3 Managed Charge Rate Scenario Profiles ............................................................................... 35

7 Forecasting ......................................................................................................................................... 36 7.1 Black cabs ............................................................................................................................... 38 7.2 Private hire vehicles ................................................................................................................ 39

8 Simulations ........................................................................................................................................ 43 8.1 Additional Network Demand ................................................................................................... 44 8.2 Network Investment ................................................................................................................ 45

9 Stakeholder Engagement ................................................................................................................... 47 9.1 Online Survey Responses ...................................................................................................... 48 9.2 Private Hire Driver Face-to-Face sessions ............................................................................. 50

10 Conclusions and Recommendations ................................................................................................. 53 11 References ......................................................................................................................................... 56 Appendix A – Supplementary Tables: Profile Proportions and Forecasts .................................................. 57 Appendix B – Half-Hourly Charging Profiles ............................................................................................... 60 Appendix C – Online Black Cab and Private Hire Driver Survey Results .................................................. 72 Appendix D – Detailed LRE Modelling Results .......................................................................................... 72 Appendix E – Dissemination Event Outcomes ........................................................................................... 72



Figures Figure 1: Forecast uptake of ZEC black cabs and private hire vehicles ............................................................................ 6 Figure 2: The load contribution of ZEC black cab and private hire vehicles by 2033 ........................................................ 8 Figure 3: NPV of the additional reinforcement costs from the ZEC black cab and private hire vehicle uptake to 2033 for

three scenarios. .................................................................................................................................................................. 8 Figure 4: High level project approach ............................................................................................................................... 15 Figure 5: Map of UKPN licence areas. ............................................................................................................................. 19 Figure 6: Map showing areas supplied at the secondary substation level by IDNO GTC (in grey). Some of these areas

contain the locations of black cab and private hire licenced drivers. (ENA, 2017) ........................................................... 20 Figure 7: Distribution of ZEC black cab and private hire vehicles per LPN secondary substation in 2032 ...................... 22 Figure 8: Distribution of ZEC black cab and private hire vehicles per LPN primary substation in 2032 .......................... 23 Figure 9: Unconstrained charging scenario profiles for different black cab driver shifts. ................................................. 29 Figure 10: Unconstrained charging scenario for private hire drivers with different shifts and battery electric vehicles ... 29 Figure 11: Unconstrained charging scenario for private hire drivers with different shifts and hybrid electric plug-in

vehicles ............................................................................................................................................................................. 30 Figure 12: LEVC black cab survey responses to the question - 'When do you work?' .................................................... 31 Figure 13: Optimised time-shifting scenario profiles for black cab drivers ....................................................................... 33 Figure 14: Set of considered daily shift patterns (non-aggregated profiles) ..................................................................... 34 Figure 15: Daily demand profile (original profile on the left and time-shifted profile on the right) where some charging

demand is shifted away from the peak (18.00) and introduced at low demand times (around 04.00) ............................. 35 Figure 16: Charging profiles for black cabs in the unconstrained charging and managed charging scenarios ............... 36 Figure 17: Forecast uptake of ZEC black cab and private hire vehicle licences (left) and driver licences (right) ............ 38 Figure 18: Line of best fit on black cab vehicle/driver ratio graph. ................................................................................... 39 Figure 19: Year on year change in the number of private hire licences. .......................................................................... 40 Figure 20: Graph to show line of best fit on private hire vehicles/drivers ratio by year. ................................................... 42 Figure 21: The load contribution from black cab and private hire vehicles in 2033.......................................................... 44 Figure 22: NPV costs for each of the scenarios modelled for different voltage levels. .................................................... 46 Figure 23: Online survey responses to – “How much does your shift pattern vary on a daily basis?” ............................. 50



Tables Table 1: Summary of key project assumptions. ................................................................................................................ 18 Table 2: Quantities of black cab and private hire drivers mapped to secondary substations in UKPN licence areas. .... 20 Table 3: Summary of the Black Cab Profile Parameters. ................................................................................................. 30 Table 4: Summary of Private Hire Profile Parameters. ..................................................................................................... 32 Table 5: TfL figures for total number of driver and vehicles licences of each type by year (TfL, 2017) ........................... 37 Table 6: Forecast uptake of ZEC vehicles among private hire drivers ............................................................................. 41 Table 7: Project assumptions tested against black cab and private hire driver survey results. ....................................... 49 Table 8: Interviewed driver profiles (*not real names). ..................................................................................................... 51 Table 9: Black cab proportions across the two shift pattern profiles ................................................................................ 57 Table 10: Annual uptake by profile as a proportion of total private hire licences. ............................................................ 58 Table 11: Variation between the forecast uptake percentages and allocation of full profiles under Distribution 2. ......... 59



Abbreviations and Definitions

Abbreviation Meaning

BEV Battery Electric Vehicle, a vehicle that runs solely on electric batteries.

Black Cab Also known as a hackney carriage or ‘taxi’ by TfL (TfL, 2018).

DNO Distribution Network Operator

EHV Extra High Voltage

EPN Eastern Power Networks plc

EV Electric Vehicle

LEVC London Electric Vehicle Company

LRE Load Related Expenditure (the model used in this project)

LPN London Power Networks plc

LV Low Voltage

HV High Voltage

Managed Charging Charging with a reduced charge rate. In this project this is set at 50% of the

maximum charge rate for the duration of charge as this will still deliver the charge

required before the next shift, but any further reductions in charge rate would need

to be tested to confirm customer acceptability

NEDC New European Drive Cycle

NPV Net Present Value

Optimised Time-Shifted

Charging

Charging which is shifted in time, within the specified off-shift period of the driver, to

the time when the local network base load is least.

PHEV Plug-in Hybrid Electric Vehicle

PHV Private Hire Vehicle includes minicabs, chauffeur and executive cars and limousines

(TfL, 2018)

Shift The period of time for which a driver is on duty and working

Smart Charging When the charge rate is managed and/or shifted in time to keep the total load within

the limits of the electricity network capabilities. Optimised Time-Shifted Charging

and Managed Charging are both examples of Smart Charging.

SMMT Society of Motor Manufacturers and Traders

SPN South Eastern Power Networks plc

TfL Transport for London

Unconstrained charging Charging at times that follow existing driving shift behaviours and preferences

(before zero emission targets)

UKPN UK Power Networks

WLTP Worldwide Harmonised Light Vehicle Test Procedure

ZEC Zero Emission Capable - emit ≤75g/km CO2 exhaust emissions (at tailpipe) and be

capable of zero emission operation for a minimum range of 20 miles (TfL, 2018).



Executive Summary

Changes to TfL’s licensing requirements for black cab and private hire vehicles will mean that all vehicles will

need to be Zero Emission Capable (ZEC) (plug-in (hybrid or electric)) by the end of 2032.

The Black Cab Green project makes the first step of modelling the impact on power networks of black cab and

private hire vehicle charging at the owners’ homes. This project has brought together a combination of

modelling and stakeholder engagement activities to assess the impacts, understand the issues and consider

possible solutions.

Why the project is necessary

To date, several innovation projects have examined the impacts of electric vehicle charging on distribution networks. At

the time of writing, the impact of charging at home has dealt with users of electric vehicles who would have relatively low

mileage and work more standard hours in comparison to black cab and private hire drivers. The variability in shift patterns

of black cab and private hire drivers, coupled with their higher mileage, means that they are likely to pose different

challenges to the distribution network in comparison to other electric vehicle users.

Previous UK Power Networks (UKPN), Transport for

London (TfL) and London Electric Vehicle Company

(LEVC) studies have looked at the requirements for rapid

and public charging infrastructure incorporating evidence

from drivers of black cabs and private hire vehicles.

No project has yet investigated the network impacts of

black cab and private hire vehicles charging at home as

they transition to zero emission over the next fifteen

years.

The key benefits to UKPN delivered by this project are in the detailed understanding of the level of network impact caused

by the transition to zero emission black cabs and private hire vehicles, as distinct from other, private vehicles. This is

captured in terms of a well-defined view of the network locations and asset types that are likely to experience thermal or

voltage issues arising from the ‘off-shift’ charging of these vehicles.

Figure 1: Forecast uptake of ZEC black cabs and private hire vehicles



A further benefit is an insight into the likely applicability of some of the smart interventions trialled through other projects

(such as My Electric Avenue and Electric Nation) in mitigating this issue.

Finally, the project also acts as a first stage towards greater levels of cooperation between UKPN and this key stakeholder

group and forms a key stepping stone towards the development of UKPN’s longer-term engagement strategy in this area.

Approach

The approach taken by this project consists of five distinct stages:

1. Data collection – gathering data from various industry sources and official surveys regarding driver shift patterns

and behaviours and on smart charging techniques being trialled elsewhere.

2. Load profile creation – using the data gathered to determine the likely charging demands that would be placed

on the network from black cab and private hire drivers charging their vehicles at home. This resulted in the

creation of 8 different profiles: two for black cab drivers, three for battery electric vehicle (BEV) private hire

drivers, and three for plug-in hybrid (PHEV) private hire drivers.

3. Network impacts assessment – performing network modelling to understand how these charging profiles will

overlay onto existing network demands. This work indicates the level of network investment this is likely to be

triggered, where across UKPN’s network that is likely to occur and when over the next fifteen year ZEC transition.

4. Conclusions and recommendations – summarise the key learning both from modelling and engaging with key

stakeholders to inform UKPN’s ongoing activity in this space.

5. Stakeholder engagement – liaise with stakeholders within the black cab and private hire sectors both to

understand their likely charging behaviours and to begin the process of engaging more closely with this

stakeholder group throughout the ZEC transition.

Network Modelling Findings

1. Analysis shows that 59% of the distribution substations in the LPN area (equivalent to 7,724) supply the postcode

of at least one black cab or private hire vehicle. Clustering is potentially a significant issue as 513 of these

substations have more than 20 black cab and private hire vehicles connected.

2. The potential implication of the electrification of the black cab and private hire fleet is to add 222MW to UKPN

demand levels across the three licence areas



3. The network investment that would be required over the transition period (2018 – 2033) would be of the order of

almost £14m in discounted net present value (NPV) terms if vehicles charge in an unconstrained manner.

0

2

4

6

8

10

12

14

Unconstrained Charging Optimised Time-ShiftingCharging

Managed Charge RateNP

V d

iffe

ren

ce b

etw

een

sce

nar

io a

nd

bas

e co

sts

(£m

)

LV HV EHV

Figure 3: NPV of the additional reinforcement costs from the ZEC black cab and private hire vehicle uptake to 2033 for three scenarios.

Figure 2: The load contribution of ZEC black cab and private hire vehicles by 2033



4. Smarter charging approaches can yield significant benefits. Optimised time-shifted charging yields up to a 71%

reduction in this investment, and managed charging shows a benefit of the order of 15% (although the latter is

based on assumed 50% reduced charging rates, which could in reality be calculated dynamically based on local

network conditions. This means that more expenditure could potentially be avoided or deferred). The optimised

time-shifted charging described here requires a highly sophisticated level of local control and coordination

meaning that the likely benefit of smart charging will fall somewhere between these figures of 15% and 71%.

5. The modelling has shown that black cabs and private hire vehicles cause later evening peaks than might be

expected of typical domestic drivers (particularly black cab drivers). This leads to a peak addition of load in the

middle of the night. Although the volume of private hire vehicles is considerably greater, in the main they have

smaller battery sizes than the black cabs, meaning that their contribution to peak load is somewhat diluted. The

modelling is based on the survey results which also showed greater levels of diversity in the private hire drivers’

shift patterns, further reducing the peak contribution.

Stakeholder Engagement Findings

This project has successfully initiated the process of engaging with the black cab and private hire driver communities in

London, who will be among the first demographics in the country to transition to ZEC vehicles.

New online surveys engaging around 400 drivers1, plus face-to-face sessions and workshops have formed key activities

in this Black Cab Green project, laying the foundations for UKPN’s longer-term stakeholder engagement strategy. Of the

survey participants:

1. Over 93% of respondents keep their vehicle at home, use it to get to and from work and do not share it

with any other driver.

2. 20% of black cab drivers’ shifts vary more than 90 minutes each day, while 39% of private hire drivers’

shifts vary by more than 90 minutes each day. This level of variability poses a challenge to modelling methods

as it affects the charging profile and hence the modelling outputs. To better understand the variability and its’

impacts more data needs to be gathered.

3. 88% of private hire drivers keep their vehicle for less than 5 years (licence could be valid for up to 10

years) and 74% of black cab drivers keep their vehicles for less than 10 years (licence could be valid for

up to 15 years). This suggests that uptake levels for ZEC vehicles may be accelerated such that the transition

occurs sooner than dictated by the vehicle licence age limits used in the forecasts for this project. In some cases,

the move to electric is market led. As more key account holders at booking companies request ‘eco’ preference

for their journeys the fleet operators upgrade the vehicles they lease to drivers to reflect the customer demand.

It is suggested that the forecast be revisited at least annually to reflect the latest real uptake figures.

4. Only approximately 30% of those with plug-in private hire vehicles reported having specialised domestic

charging points. It should be noted that this is based on a small sample size of the early adopters. Information

on whether they had plans for installing charging points was not collected. During the face-to-face interviews it

1 A small minority of whom already operate EVs



was found that of those without domestic charging points some used three pin plugs to charge while others used

public infrastructure exclusively. When asked about the lack of charging points at home drivers responded with

one or more of the following reasons associated with their living arrangement:

a. rented accommodation (requiring permission for installations, as yet not sought from the landlord),

b. houses with no access to off-street parking,

c. living in a non-ground floor flat.

Recommendations and Next Steps

1. This project represents the first step on the journey for UKPN in its engagement with the black cab and private

hire stakeholder group. This project will inform the development of a longer-term engagement strategy that will

seek to address some of the points highlighted here via various channels, including through organisations such

as TfL.

2. The charging profiles used in this work have been derived and inferred from various data sources, such as official

industry surveys and interviews. These profiles could be refined, and their accuracy improved, based on real

plug-in vehicle charging data. It is recommended that follow-up activity be pursued with the black cab and private

hire community to record actual charging behaviour over a sustained period of time, such as a year, to reflect

seasonal variation (perhaps via vehicle telematics or monitoring at charge points). The profiles can then be

updated, and the models re-executed to understand the implications these ‘real-world’ profiles have on network

demand and expenditure levels. This needs to be performed once there is a significant volume of ZEC black

cabs and private hire vehicles on the road, providing a richer data set than was available at the time of this study.

3. The Black Cab Green project fully acknowledges the target set out in the Mayor’s Transport Strategy seeking to

achieve a minimum of 9,000 ZEC taxis in the fleet by 20202. The Mayor, together with TfL, is already offering

financial incentives and installing charging infrastructure to achieve this target. The uptake of ZEC taxis set out

in this project is based on the normal rate of churn of licensed taxis and the imperative for taxi vehicle owners to

transition to ZEC in order to licence new vehicles. While this leads to fewer than 9,000 taxis being ZEC by 2020,

this represents the lowest number of ZEC taxis that will be on the streets in this timescale and as such sets

something of a minimum level of additional electric taxi volumes on UKPN’s network. It is acknowledged that it

is very likely that the uptake rate of ZEC taxis will be faster than that set out in this study but there is some

uncertainty around how this might manifest (for example, will there be a linear uptake of ZEC vehicles, or will

there be a tipping point after which uptake will rapidly increase?). It is also acknowledged that the potential

introduction of new ZEC taxis (e.g. the Dynamo (Nissan) and Fraser Nash Metrocab) will affect the uptake and

progress towards the 9,000 target. Furthermore it is important to acknowledge the uncertainty in the uptake rate

of ZEC private hire vehicles (PHVs). Although the ZEC requirement for new vehicles licensed for the first time

as PHVs comes into effect on 1 January 2020, there may be an increase in the number of licensed ZEC PHVs

in advance of this date. Given these uncertainties, it was felt that using the licence expiry date and the churn of

2 https://www.london.gov.uk/sites/default/files/mayors-transport-strategy-2018.pdf

https://www.london.gov.uk/sites/default/files/mayors-transport-strategy-2018.pdf



vehicles as the driver for the uptake was appropriate for this project. In running the LRE model adjusted to reach

9,000 electric taxis by 2020 the NPV cost difference for UKPN was negligible.

4. It is prudent that this modelling be revisited regularly, with continual updates regarding the actual uptake levels

of both ZEC taxis and PHVs, perhaps on an annual basis. Charging profiles based on battery technology

projections should also be revisited in the future. Once the number of electric vehicles used by black cab and

private hire drivers become more widespread, the profiles should be revisited to ensure they align with the

technology available. Again, this could result in re-executions of the models created in this work to understand

the implications of the evolving technology. Moreover, research for this project has given some indication that

drivers keep their vehicles for less than the maximum validity of the vehicle licence. UKPN will seek to continue

their liaisons with TfL to monitor the number of licensed ZEC taxis and PHVs in operation in the Capital.

5. The potential benefits of using smarter charging approaches rather than unconstrained charging highlights the

importance of continuing to liaise cross-industry and with government and regulatory bodies. This will ensure the

potential benefits to customers that smarter charging could bring (in terms of reduced energy bills) are not missed

through inaction. If charging technology is widely rolled out without the capability to enact these charging options

(such as the functionality to time-shift or charge at a reduced rate), it could represent a significant missed

opportunity to drive out value for customers and other stakeholders.



Acknowledgements

This report has been prepared for UK Power Networks by EA Technology.

EA Technology gratefully acknowledges the support of the project team at UK Power Networks in the production of this

report and is also happy to acknowledge the contribution of the following Black Cab Green partners in the provision of

data and feedback on the document:

• Imperial College London

• Transport for London

• London Electric Vehicle Company



1 Background

Over the past few years, Distribution Network Operators (DNOs) in Great Britain have carried out a number of innovation

projects to improve understanding in the area of Electric Vehicles (EVs), including studies focusing on potential uptake

scenarios; possible driver behaviours and charging profiles; and the impacts this will have on distribution networks.

Early projects in this area funded through the Low Carbon Networks Fund included UKPN’s Low Carbon London (LCL)3

and SSEN’s My Electric Avenue (MEA)4, with the latter showing that at a 3.5kW charge rate, EVs could double peak

demand on Low Voltage (LV) networks. MEA went on to show that a sizeable proportion of LV networks will require

intervention as clusters of plug-in vehicles occur due to the lower diversity of charging demand in comparison to some

other electricity demand types. Indeed, MEA showed that across GB by 2050 the additional cost of traditional

reinforcement (compared to the use of smart solutions), supposing an EV penetration of between 30-70%, would be £2.2

billion. It is important to note that these results were based on 24kWh Nissan Leaf vehicles charging at only 3.5kW. Since

this project was completed, it has become standard to charge at higher rates and for vehicles to be equipped with larger

batteries.

While this work was useful in initiating the analysis of EV impacts, more is clearly required to understand how impacts

will change both as vehicle and battery technology develop, and as customer uptake and acceptance of EVs increases.

Work in this area has therefore continued, and one example of a live project in this space is Electric Nation5; a Network

Innovation Allowance project being carried out in Western Power Distribution’s licence area. This project is collecting

domestic charging data from up to 700 plug-in vehicles, composed of 36 different models across 16 different

manufacturers, thereby capturing a broader cross-section than was possible in MEA. This project will give great insight

into the viability of smart charging and its impact on consumers.

While Electric Nation represents an extensive trial for the UK, its focus is very much on domestic charging by private

users. Indeed, this has been the main area of investigation within innovation projects in the sector to date, with some

work also being undertaken on public charging infrastructure and commercial charging. In mid-2017 TfL released its

‘Electric vehicle charging infrastructure: Location guidance for London’ report which, based on various evidence, aims to

inform understanding of where different types of publicly accessible charging infrastructure may be best placed. Similarly,

UKPN has previously conducted its ‘Electrification of buses and taxis in London’ study to understand the level of

reinforcement on the HV and EHV network that could be triggered by electrification of these fleets and charging at depots

3 http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-projects/Low-Carbon-London-(LCL)/ 4 http://myelectricavenue.info/ 5 http://www.electricnation.org.uk/

Electric vehicles have been the focus of several innovation projects within the electricity distribution

sector. This chapter briefly outlines some of the previous work carried out in this space, indicating where

Black Cab Green fits into this wider portfolio and demonstrating the unique challenge that it is seeking to

address.

http://innovation.ukpowernetworks.co.uk/innovation/en/Projects/tier-2-projects/Low-Carbon-London-(LCL)/

http://myelectricavenue.info/

http://www.electricnation.org.uk/



or hubs. To date, there has not been any extensive analysis of the unique use case of black cab and private hire vehicles

charging at home and their effects on the electricity networks.

Owing to the differences between this group of EV users and the wider population (in terms of daily mileage, shift patterns

etc) it is difficult to apply the same thinking regarding charging profiles and driver behaviour. This group therefore merits

individual analysis.

Market intelligence from Transport for London (TfL) shows that the current 22,000 licensed black cab drivers and 116,000

licensed private hire vehicle drivers tend to live in certain hotspots around Greater London and the South East.

Furthermore, London has become the first city in the UK to impose emission restrictions on black cab and private hire

vehicle licences. From 1 January 2018, all new black cabs in central London must be zero-emission capable (ZEC) (i.e.

plug-in vehicles) and the same will apply to private hire vehicles from 1st January 2023. (These requirements came in

only after the end of the LCL project in 2014 which looked at real data from EV users in South London.)

As such, it is likely that UKPN’s distribution networks will be amongst the first in Britain to accommodate residential

clusters with high concentrations of plug-in vehicles, making this project particularly timely. Continually revisiting and

refining the analysis begun in 2009 when UKPN first performed its ‘Electrification of buses and taxis in London’ study will

be key to ensure that the impacts are fully understood as technologies mature and become mass market in their adoption.

While London is the focus of this study, the electrification of black cabs and private hire fleets is likely to spread to other

UK towns and cities. Indeed, there is already an initiative in Dundee (launched in 2015) for an electric fleet of taxis6, while

Oxford too announced a zero emissions zone in 20177. The findings of Black Cab Green will therefore form a key part of

understanding the challenges network operators across the country face as this electrification accelerates.

6 https://newsroom.nissan-europe.com/uk/en-gb/media/pressreleases/131075/transport-minister-launches-dundees-electric-taxi-fleet 7

https://www.oxford.gov.uk/news/article/553/city_and_county_councils_propose_historic_reduction_in_oxford_s_air_pollution_with_world_s_first_zero_emission_zone

https://newsroom.nissan-europe.com/uk/en-gb/media/pressreleases/131075/transport-minister-launches-dundees-electric-taxi-fleet





2 Approach

The objective of the Black Cab Green project was to quantify the impact of the upcoming emissions requirements for

black cabs and private hire vehicles on UKPN’s residential networks. Quantifying the network requirements, supports UK

Power Networks to:

1. Proactively manage the requirements of plug-in black cabs and private hire vehicles during the RIIO-ED1 price

review (April 2015 - March 2023).

2. Effectively plan for the uptake as it accelerates through RIIO-ED2 (the price review period April 2023 – March

2031).

The high-level approach to the project is described by the five stages in Figure 4 below.

Figure 4: High level project approach

•Gather data

•Test assumptions

•Confirm expected outcomes and compare with previous UKPN studies

Task 1: Data Collection

•Group customers by behaviour

•Generate statistically valid load profiles for specific locations and years to 2022 (or beyond)

Task 2: Load Profiles

•Allocate load profiles to networks

•Determine 'hot spots' for network reinforcement

Task 3: Network Impacts

•Identify level of flexibility in customer demand

•Identify proven solutions which are applicable

•Compare alignement ot other projects and feed into smart charging dialogue

Task 4: Next Steps

•A range of activities to engage with black cab and private hire drivers and their representatives

•Survey, charging log, 1-1 interaction and workshop

•Will lead to well-rounded output and inform longer-term engagement strategy

Task 5: Stakeholder Engagement

In order to quantify and evaluate the challenges posed by home charging of black cabs and private hire

vehicles, it is necessary to agree the methods by which this will be done and, furthermore, understand

any assumptions that must be taken. This chapter briefly describes the key aims of the work; the stages

undertaken to achieve those aims; and the necessary assumptions to facilitate the analysis.



2.1 Project Outputs

This project complements UKPN’s previous work on EV uptake by examining the specific case of home charging for

black cabs and private hire vehicles licenced in Greater London; which is anticipated to be a very different use case

compared to other domestic customers.

This project provides:

• Tailored load profiles for new electric black cabs and private hire vehicles and various driver behaviours.

• Quantified forecast of ZEC black cab and private hire vehicle uptake and network load at specific locations to the

end of 2032 (the time by which all black cab and private hire vehicles in London are expected to be ZEC due to

the new rules and licence age restrictions).

• Clearer understanding of the level of clustering that exists among black cab and private vehicles.

• An understanding in the cost differentials between conventional and smart solutions to manage the network.

• Engagement with stakeholders in the black cab and private hire community to understand the factors influencing

their behaviour and to contribute to a long-term engagement strategy.

• Recommendations for next steps including suitable solutions and methods of implementation.

2.2 Project Structure

To meet these objectives the project began by collecting data to help with the understanding of:

a) the location of the additional load associated with ZEC rules on drivers and,

b) the size and timing or ‘profile’ of the additional load.

The methodology for part a) above is described in more detail in section 3. For forecasting and planning purposes the

required information was the aggregated load at (secondary) substation level. This was determined by using current TfL

licensed driver addresses as opposed to the vehicle licence addresses. This is because for those who rent or lease their

vehicle the vehicle licence address will be the fleet owner’s business address and not where the vehicle is kept overnight

(at the driver’s address).

While it is inevitable that not all licensed drivers will remain at the same address over the course of the modelled period,

any variation in this is expected to be small in comparison to the overall numbers of licensees. Therefore, it is assumed

that the locations of licensees are ‘static’ over the period to 2032.

A second key assumption is that the majority of drivers keep their vehicles at home (and not at a depot or garage). These

and other assumptions are tested in various stakeholder engagement activities associated with the project.

For part b) existing survey data from TfL and London Electric Vehicle Company (LEVC) regarding timing and duration of

shifts was used to determine the likely time that drivers would return home and ‘plug-in’ as well as the time by which they

would need to unplug to commence their next shift. This was used in conjunction with market information on battery

capacities and ranges to determine the likely size and duration of the demand profiles associated with charging the

vehicles. Further detail on this approach can be found in section 6.1.



Having quantified the likely impact of ‘unconstrained’ charging on UKPN’s network (see Section 6), it was then necessary

to understand the potential savings that could be realised through having ‘smarter’ charging regimes. For this analysis,

two scenarios were developed; one to shift the load in time and the other to reduce the charge rate. The creation of the

smart scenarios profiles is explained in sections 6.2 and 6.3.

Section 7 sets out the methodology used in determining a forecast for the uptake of ZEC vehicles among the population

of drivers based on trends seen in published historic volumes of licences.

The outputs of this process form the configuration parameters used by Imperial College London in their simulations run

on their Load Related Expenditure (LRE) model. Section 8 provides an analysis of these results.

The range of stakeholder engagement activities planned to provide further evidence for the assumptions made in the

analytical work carried out are introduced in section 9. Online surveys, face-to-face interviews, charging logs and

workshops, some of which provide a basis for UKPN’s longer-term engagement strategy, are detailed in section 9 and

the appendices.

2.3 Key Assumptions

With such early stage desktop studies, it is necessary to make some assumptions (listed in Table 1). The basis of

these assumptions is described in more detail in the sections of the report referenced in Table 1, but they are listed

here for ease of reference.



Table 1: Summary of key project assumptions.

Key project

area

Assumption Further

information

Licence

Mapping

and

Forecast

The net change in the number of drivers at each substation to 2032 will be insignificant Section 7

Drivers will change their vehicle when its (TfL issued) licence expires (15 years for black cabs

and 10 years for private hire vehicles)

Section 7

Most drivers keep their vehicles at home (at the driver licence address). Section 3

Vehicles

and

Charging

The private hire transition will be towards plug-in hybrid vehicles first. Assumed to be

charged at 3.5kW (as evidenced by the Electric Nation trials) and Battery Electric Vehicles

(BEVs) will be charged at 7kW (as evidenced in Electric Nation trials).

Section 4.2

The black cab profile will be based on there being 23kWh of useable battery capacity and

7kW charging. This specification applies to the LEVC TX (LEVC currently hold 85% of the pre-

electrification black cab market) and is also applicable to the Nissan-Dynamo electric vehicle

when considering likely battery depletion at the end of a shift.

Section 4.1

The drivers with access to off-street parking will have the ability to charge at home and

those without off-street parking will have access to on-street charging points near their

home.

Section 3

The majority will charge their vehicles daily during off-shift hours (even if operating a plug-

in hybrid). Non-residential charging impacts are not modelled. Section 4

Modelling

and

Scenarios

The mapping of black cab and private hire driver locations was conducted at the distribution

and primary substation level only. This was due to the nature of the available dataset

(postcodes only) meaning it did not provide the granularity to map to individual addresses

or to LV circuits supplied from the distribution substation. The modelling tool used (Imperial

College’s LRE tool) only extends to the distribution substation level and then makes a series

of assumptions regarding the LV circuit level. Therefore, the dataset could be provided to

the model and the same assumptions retained as they would be for other UKPN investment

planning activity, ensuring a consistent approach.

Section 3

Without knowing the age of the licence at each location, the forecast was carried out on

the population and applied in whole number profiles, at random, on each substation. Section 8

Charge events can be shifted to any time before the start of the next shift (this assumes the

vehicle is kept at home throughout the downtime and not used for other activities). Section 6.2

Charge rates are reduced to half their maximum capacity. This is assumed as the tolerance

the customers would be willing to accept while still providing full charge before the next

shift.

Section 6.3



3 Licence Mapping to UKPN substations

The first task of the project was to map the locations of black cab and private hire drivers to locations within UKPN’s

network. This is important as the likely network impacts could be highly localised and there is a need to understand which

local distribution substations, for example, will be most significantly affected. The analysis also proves useful in

understanding the level of clustering that occurs with black cab and private hire drivers’ locations.

The anonymised postcodes of 21,966 licensed black cab drivers and 116,712 licensed private hire drivers were

processed to obtain the total numbers of black cab and private hire licence holders supplied by each UKPN secondary

substation.

UK Power Networks operates three distribution Networks: EPN, LPN &

SPN. These are located in the south east of England, as shown in Figure

5). Table 2 shows that not all driver postcodes were mapped to UKPN

secondary substations. Upon investigation it was found that there were

the following two reasons for this.

• Firstly, some drivers were registered outside the UKPN area (i.e.

their address did not fall within one of the three licence areas

shown in Figure 5)

• Secondly, a proportion were supplied by IDNOs within the UKPN

boundary (Figure 7). For this latter group, the partial postcodes

were used to determine the closest and hence most likely

primary substation supplying those drivers. In this way it was still

possible to consider their impact on the distribution network.

Figure 5: Map of UKPN licence areas.

Before modelling the effects of their charging demand at home, it is necessary to understand where the

black cab and private hire vehicles are located on UKPN’s network. This mapping was performed using

anonymised postcode data from TfL and resulted in a total number of each licence type (black cab or

private hire) at each distribution substation. This was essential in understanding the level of clustering of

black cab and private hire drivers which exists, and therefore driving the potential investment needs of

the network, which will vary significantly depending on both these clustering levels, along with the

underlying network demand levels.



Table 2: Quantities of black cab and private hire drivers mapped to secondary substations in UKPN licence areas.

UKPN Licence Area Number of Black Cab Driver

Postcodes

Number of PHV Driver Postcodes

LPN 9,031 55,216

SPN 3,811 9,463

EPN 9,695 28,049

Total mapped to secondary substation 22,597 92,728

Total licences 21,996 116,712

Driver postcodes matched to UKPN (%) 91% 79%

Figure 6: Map showing areas supplied at the secondary substation level by IDNO GTC (in grey). Some of these areas contain

the locations of black cab and private hire licenced drivers. (ENA, 2017)



The driver postcode information was used to carry out the mapping because this project is interested in measuring the

impacts of charging at home (not of opportunistic charging throughout the course of the day).

This project assumed that the majority of drivers keep their vehicle at home. It is true that some drivers may leave there

vehicle in a garage or with the fleet operator and/or share their vehicle with someone else. However, data shows that

over 94% of black cab and private hire drivers leave their vehicle at home between shifts, meaning that this mapping

exercise is appropriate as a means to identify the charging locations.

Although most current thinking assumes that off-street parking is required for home charging, it is envisaged that in the

near future domestic charging will be available more widely. Encouraged by grants such as OLEV’s ‘On-Street

Residential Chargepoint Scheme‘ and new technologies such as charging points in lampposts (as seen in trials in

Hounslow (Taylor, 2017)) there will be opportunities for domestic charging for drivers without off-street parking. Drivers

who rent rather than own their home will not be hindered from installing charging points (if they do not already have one)

because of the government grant available. This project therefore assumes that all drivers will have the ability to charge

at their home.

3.1 Clustering

A key concern with the uptake of Low Carbon Technologies (LCT) such as plug-in electric vehicles is the phenomenon

of clustering. The nature of LCT clustering on networks is such that there are some areas of the network with very low

numbers of a technology and others that quickly become hotspots.

Analysis shows that of the 13,141 distribution substations in the LPN area, 59% (equivalent to 7,724) supply the

postcode of at least one black cab or private hire vehicle. 513 have more than 20 black cabs and private hire vehicles

connected (representing 4.4% of all substations within LPN), while 176 substations supply more than 30 black cab or

private hire vehicles as illustrated in Figure 7.



Figure 7: Distribution of ZEC black cab and private hire vehicles per LPN secondary substation in 2032

This analysis gives a clear indication that there are high levels of clustering occurring in certain areas, and this suggests that some LV networks could be significantly impacted by ZEC black cab and private hire vehicles. It is important to note that these figures do not account for any non-black cab and non-private hire EVs that could also be present on these networks. This means that the figures presented here are a minimum level and there is likely to be further EVs connected which would exacerbate network demand at these locations. The analysis also shows that 52% (4,032) of the substations supplying black cab and private hire vehicles, supply five or fewer drivers. This means that, of all the distribution substations in the LPN areas, 72% (9,449) of them supply no more than five black cab and private hire vehicles. These network areas are far less likely to suffer issues requiring investment as a result of the electrification of the black cab and private hire vehicle fleet. However, even at low levels, a cumulative effect in a wider network area could cause upstream effects on the higher voltages and result in investment at these levels, even though there is sufficient capacity at the LV level.

Indeed, this potential upstream effect is illustrated when one considers the number of black cab and private hire vehicles

connected to each primary substation in the LPN area. There are 95 primary substations that will supply ZEC black cab

and private hire vehicles. Of these, more than 20% will supply over 1000 such vehicles, while 50% of the primary

substations will supply fewer than 500 vehicles, as illustrated in Figure 8, below.

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Figure 8: Distribution of ZEC black cab and private hire vehicles per LPN primary substation in 2032

4 Vehicle Details

4.1 Black Cabs

4.1.1 ZEC Vehicles and Charge Duration

The public domain technical specification information from vehicle manufacturers was used to define the maximum

battery capacity and charge rate. For black cab drivers, the choice of vehicles is more limited than for their private hire

counterparts. The LEVC TX is the only approved black cab vehicle on the roads at time of writing and the Nissan Dynamo

(based on the e-NV200) is the only vehicle that is expected to follow shortly. Metrocab trialled a vehicle in November

2014 (Metrocab, 2017) but technical information on the battery capacity is not available on their website and the launch

date is not yet announced.

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Having understood where the black cab and private hire vehicles are in relation to UKPN’s asset base, it

is necessary to investigate the characteristic of the drivers’ behaviour and the technical specifications of

the vehicles. With this, the impacts of vehicle charging on the distribution network can begin to be

quantified. This section sets out the various vehicles that could be used as black cabs or private hire

vehicles and explains the construction of charging profiles based on battery sizes, charging rates and

daily mileage.



For the purposes of this project it is assumed that a specialist domestic charging point is installed and used (rather than

a 3-pin charging cable). It is further assumed that the domestic cut-out fuse can accommodate the specialist charger or

has been upgraded to do so.

With Range Extending Vehicles (plug-in hybrids), there is not the same range anxiety as with BEVs. Therefore, if the

mileage allows, it is possible (and indeed likely) the driver would fully deplete the battery. Responses to a survey

conducted by LEVC show that 83% of all drivers travel more than 70 miles (electric range of the LEVC PHEV model)

during their working day and 48% travel more than 100 miles. It is evident that the majority of users will deplete most of

the useable battery capacity (23kWh for the plug-in hybrid LEVC model) during the shift. It is therefore assumed that the

battery is fully depleted and requires a full charge at the end of the shift. Hence, for each profile the total power drawn

(7kW) multiplied by the duration equates to the chargeable battery capacity.

It is necessary to consider other potential electric black cabs alongside the LEVC model. Nissan-Dynamo’s BEV model

with a 40kWh battery has a stated range of over 170 miles (Dynamo Taxi, 2017). Taking the survey responses referenced

above, and therefore assuming that the average driver travels 100 miles per day (48% travelled this much or further), we

can calculate how much of the 40kWh battery in the Nissan Dynamo would be depleted. Assuming the 170-mile range

figure to be correct, a driver travelling 100 miles per day would deplete the battery by 58% (100 miles divided by the 170-

mile range). 58% of the 40kWh battery approximates to 23kWh of battery used per day.

This is the same as the full battery depletion associated with the LEVC TX model. As the charge rates are also the same

(7kW) the charge profile derived for the Nissan-Dynamo would be the same as for the LEVC TX.

4.1.2 Proportions Per Profile

The split of the population across the LEVC TX and the Nissan-Dynamo is therefore irrelevant to the modelling because

the profile is the same. According to a TfL survey conducted at the end of 2016 with 673 responses, the current black

cab vehicle market share of LEVC models is 85%. 14% of the survey respondents were drivers of Mercedes vehicles.

(At time of writing, Mercedes are not publicising a ZEC black cab model.) Therefore, it seems likely that Nissan-Dynamo

will take a proportion of the remaining market.

The ‘Conditions of Fitness’ set out the requirements that vehicles must comply with in order to be used as black cabs in London. These include accessibility and emissions standards plus the turning circle requirement which makes it more challenging for vehicles to be licensed as black cabs in the Capital. The market size for new black cabs is limited and in most recent years only around 1,200 new black cabs have been licensed in London. Therefore, it is unlikely there will be many new models emerging quickly. It is for these reasons that there is one set of vehicle specifications considered in the black cab profiles.

In terms of the different shift patterns, LEVC survey results show black cab driver shift hours to be relatively well defined

and more fixed (than private hire driver shifts). The split is 49% during the day shift 08:00-18:00 and 51% during the night

shift 18:00 – 00:00.



4.2 Private Hire Vehicles

4.2.1 ZEC Vehicles and Charge Duration

The range of vehicles available to private hire drivers is much wider than the black cabs described above. The two main

types of ZEC vehicles are explored.

Average plug-in hybrid vehicle (PHEV) capacity for the marques of vehicles a private hire driver might own is 8kWh.

This is based on analysis of vehicles manufactured by Toyota, Mercedes, Volkswagen, BMW, Citroen, a group of

manufacturers which make up 61% of the private hire vehicle population according to TfL survey results. (Note: other

private hire driver vehicle manufacturers (e.g. Vauxhall and Honda, 12% of private hire vehicles) do not currently have

plug-in vehicle models.)

The newest models of PHEVs, according to the Electric Nation project, tend to use a 3.5kW charge rate. The reason for

this lower charge rate as compared to BEVs is that these vehicles have a smaller battery size, meaning it takes less time

to fully charge and the lower charge rate does not adversely affect the customer. It is assumed that this will remain the

same over the modelled period.

Clearly, the amount of mileage that will be achieved using these smaller batteries is considerably less than the daily

mileage covered in a shift and could be fully utilised several times over. The project has therefore assumed that by the

time the driver returns home, the battery will be fully depleted, whether they use (rapid) charging points in the city or not.

Accounting for the trend in increasing battery capacities for BEVs and discounting the 100kWh Teslas which are unlikely

to form any significant portion of the private hire population, it is assumed that from 2020, the most popular battery

capacity is 40kWh. The quoted mileage for 40kWh batteries (for the latest releases of Renault Zoe 4.0 and Nissan Leaf)

are 250 NEDC miles (Renault, 2018) and 177 (WLTP Combined Cycle) miles (Nissan, 2018) respectively. The New

European Drive Cycle (NEDC) tests have been used since the 1980s as the standard to determine vehicle mileage. It is

being replaced with the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) which accounts for evolutions in

technology and driving conditions that are expected to give results closer to the reality. EV users (of various models) who

have shared their experiences online state that the actual mileage can be 60% of the NEDC value stated by the

manufacturer. This is corroborated by Renault who state on their webpage about the Zoe 4.0 that real-life estimates are,

‘up to 186 miles in the summer and 124 miles in the winter’ (Renault, 2018).

TfL survey responses show that the total daily mean mileage is 74 miles. However, the same survey showed that 36%

of drivers have a daily mileage exceeding 110 miles, showing that the mean figure seems a little low to take as the basis

for the profile generation. Furthermore, in winter time it is likely that the range of the electric vehicle will be slightly lower,

particularly if the vehicle is heated throughout the day. In order to avoid the modelling exercise being overly optimistic,

nor overly pessimistic, it was felt that a compromise daily mileage of 110 miles be used.

This figure approximates to 23kWh of battery capacity, meaning that for modelling purposes, it can be assumed that

23kWh of battery is depleted during the course of each day and recharged at home



4.2.2 Proportions per Profile

The future market split between BEVs and plug-in hybrid electric vehicles (PHEV) among private hire drivers is difficult

to predict. According to Society of Motor Manufacturers & Traders (SMMT) figures for UK registered vehicles in 2017 (up

until October) the split of plug-in vehicles is 22% BEVs and 78% PHEV (SMMT, 2017). According to the TfL survey,

private hire drivers are most concerned about the electric range of EVs and the potential impact that needing to charge

an EV would have on their daily work. PHEVs would alleviate this concern as well as concerns with the charging

infrastructure. It is therefore assumed that initially, up to 2025, 80% of drivers will go for PHEVs due to the uncertainty

represented by BEVs and 20% will go for BEVs.

Learning from the market research in UKPN’s project, ‘Recharge the Future8’ suggests that, based on New European

Driving Cycle (NEDC) data and survey responses, a 450km range (in test conditions, real world range closer to 300km)

is the optimum range for the cost to benefit ratio. With improving battery technologies this is represented by a battery

capacity of around 50kWh from medium-sized, mid-range vehicles and is predicted to be available on the market from

2020. Market uptake will lag slightly behind this. This will alleviate most range anxiety issues drivers feel and perhaps

encourage more drivers to buy or lease BEVs. It would not alter the profile which is dependent on the mileage covered

though it may mean some drivers choose to charge every other day instead of on a daily basis. For the purposes of this

study, because there is no data concerning the likely black cab and private hire driver behaviours in future, it is assumed

that they will plug in every day.

According to the same study, PHEV ranges have been increasing at a much slower rate and appear to be plateauing.

Therefore, the assumption for average PHEV capacity is that it will remain at 8kWh throughout the modelled period.

The split between BEVs and plug-in hybrids will alter over time as battery technology improves along with charging

infrastructure and driver acceptance. According to forecasts by Element Energy (Element Energy, 2017) the split between

BEVs and plug in hybrid in 2025 will be 42.5% to 57.5%, representing a significant change from the starting position of

80% to 20% taken by this project.

5 Modelling Scenarios

Having established where the black cab and private hire vehicle charging demand is likely to materialise (see section 3),

and also what the likely technical characteristics of the vehicles are likely to be (see section 4) it is then necessary to

8 http://www.smarternetworks.org/project/nia_ukpn0028

The way in which vehicles are charged will have a profound impact on the need or otherwise to reinforce

the local distribution network. This section sets out the various scenarios for charging to be investigated;

from unconstrained charging where black cab and private hire drivers can charge at home at any time of

day, to the smart charging approaches that could mitigate the need for network investment.

http://www.smarternetworks.org/project/nia_ukpn0028



understand what the implications of this will be for the distribution network. In order to do this, it is necessary to determine

what the charging profiles of the various vehicles will be under various different conditions.

As outlined in section 4, there are a number of potential different vehicles and these require a range of charging profiles

to reflect both their technical characteristics, and also the shift pattern of the drivers using these vehicles.

Furthermore, it is insufficient to simply determine what these profiles could resemble if charging were to happen in an

‘unconstrained’ manner. Rather, there is a need to consider the implications for network loads and expenditure in the

event that various ‘smart charging’ regimes are implemented.

To this end, three potential scenarios have been modelled by Imperial College London within the Black Cab Green

project. In brief the scenarios simulated are as follows:

1) Unconstrained charging – business as usual scenario for electrification of black cabs and private hire

vehicles whereby the drivers plug-in and unplug at the end/start of their shifts as they wish and receive their

charge without any delay. This potential world neglects any smart charging approaches or incentives that could

be introduced through tariffs or manged charging measures. The profiles considered for this future world can be

found in section 6.1.

2) Optimised time-shifting charging – an approach whereby drivers plugging in at peak network demand times

do not receive the charge as soon as they plug-in. Instead, the necessary charge is delayed and delivered at

some time in advance of their unplug time, so as to avoid charging occurring at the time of the local network

demand. When charge is delivered, this is at the same rate as it would be in the unconstrained charging scenario

(i.e. if the vehicle usually charges at 7kW, it would receive charge at a rate of 7kW in this scenario). Sufficient

provision is in place to ensure a full charge is delivered before the driver’s next shift (allowing for commute times),

but this is intended to reduce demand at peak times and hence reduce potential network expenditure. Details of

this potential charging regime can be found in section 6.2.

3) Managed charging rate – an approach whereby no delay is applied, and the driver begins receiving charge

as soon as they plug-in, but the level of charge received at any given point can be less than the maximum power

that the charger can deliver. For example, in the case of a 7kW charger, if a driver returned home and plugged

in at 18.00 (a time of high network demand) they may only receive charge at 50% of the usual rate (i.e. 3.5kW)

for several hours. The level of charge rate reduction and the time over which it is necessary is governed by local

network conditions. Once network conditions are such that the demand is reducing, then the level of charge

received would increase. For the purposes of this project, it was not possible to ascertain local network

conditions, and hence to be able to determine the benefits of such a mechanism, the charge rate was reduced

by a uniform 50% for the duration of the charging event Details of this potential charging regime can be found in

section 6.3.

As part of the analysis, eight charging profiles have been developed for each of these scenarios. There are three variants

for BEV private hire drivers (see section 6.1.2), three for PHEV private hire drivers (also shown in section 6.1.2), and two

for black cab drivers (see section 6.1.1).



6 Charging Profiles

6.1 Unconstrained Charging Scenario Profiles

To develop a suite of profiles that are likely to be representative of the way in which black cab and private hire vehicles

charge at the end of a shift, the project drew on a number of sources, including previous surveys conducted by TfL and

LEVC.

These surveys contain information relating to shift patterns and the number of miles driven per day. These factors are

important when considering the plug-in and unplug times, and also the state-of charge of the vehicle when finishing a

shift. The state-of-charge will influence the length of time for which the vehicle needs to charge before commencing the

next shift, while the shift timings will influence the point at which charging begins.

The creation of the profiles also drew upon publicly available information from vehicle (and black cab) OEMs regarding

the likely battery capacity and charge rates for different battery electric vehicles (BEVs) and plug-in hybrid electric

vehicles (PHEVs), previously discussed in section 4.

For reference, the profiles derived for black cabs, private hire BEVs and private hire PHEVs are shown in Figure 9, Figure

10 and Figure 11 respectively. It can be seen from these figures that the profiles are aggregated, capturing the diversity

that occurs in ‘start of charge’ times.

Building on the scenarios described in the previous section; this chapter illustrates the creation of the

eight ‘unconstrained charging’ profiles for different vehicle types and shift patterns (based on technical

specifications of the vehicles and on driver survey responses). It then goes on to examine the profiles

that are used in the various smart charging scenarios: ‘optimised time-shifted charging’ and ‘managed

charging’.



Figure 10: Unconstrained charging scenario for private hire drivers with different shifts and battery electric vehicles

Figure 9: Unconstrained charging scenario profiles for different black cab driver shifts.

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These profiles were created based on survey results of when drivers commenced and finished their shifts. The results

indicate a slight dip around 04.00 arising from the fact that this profile is an aggregation of numerous survey responses

with relatively few respondents indicating their shift finished at around 04.00. More information on the profile creation

can be found in Appendix B – Half-Hourly Charging Profiles.

6.1.1 Black cabs

A summary of the parameters used in determining the black cab profiles is shown in Table 3 and explained in the following

section.

Table 3: Summary of the Black Cab Profile Parameters.

Black Cab Profile Shift Description

Day Evening

End of Shift 18:00 00:00

Start of Shift (latest unplug time)

08:00 18:00

Vehicle Type LEVC PHEV / Dynamo BEV

LEVC PHEV / Dynamo BEV

Battery Capacity Charged each Day (kWh)

23 23

Charging rate (kW) 7 7

Proportion of Licence Type (%)

49 51

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Figure 11: Unconstrained charging scenario for private hire drivers with different shifts and hybrid electric plug-in vehicles



This data comes from a survey conducted by LEVC in April 2016 which gathered 1,189 responses (5.4% of all TfL

licenced black cab drivers). LEVC make up 85% of the black cab market hence this dataset is considered to be

representative for the population.

Based on the survey results, it was found that the shift patterns are discretely defined for black cabs (93% falling across

08:00-18:00 and 18:00 – 00:00). However, in order to account for the diversity in commute distances and times and in

traffic there is a ramp up to the plug-in/charging times. The start charge time is based on the end of shift time plus the

duration of the commute home. The 7% of the population that work between 00:00 – 08:00 do not have a profile of their

own as this does not constitute a significant enough portion of the population for separate consideration within the

modelling environment.

6.1.2 Private hire

A summary of the parameters used in determining the private hire profiles is shown in Table 4 and explained in the

following section. The raw data used to determine these patterns and profiles comes from 1,803 private hire driver

responses (2.4% of all TfL licences private hire drivers) to a TfL survey conducted at the end of 2016.

Figure 12: LEVC black cab survey responses to the question - 'When do you work?'



Table 4: Summary of Private Hire Profile Parameters.

Private Hire Profile Shift Description Early

Evening Finisher

Late Evening Finisher

Early Morning Finisher

Early Evening Finisher



End of Shift 16:00 - 20:00

20:00 - 00:00

00:00 - 06:00

16:00 - 20:00

20:00 - 00:00

00:00 - 06:00

Start of Shift (latest unplug time) 06:00 06:00 12:00 06:00 06:00 12:00

Vehicle Type BEV BEV BEV PHEV PHEV PHEV

Battery Capacity Charged each day (kWh)

23 23 23 8 8 8

Charging Rate (kW) 7 7 7 3.5 3.5 3.5

Proportion of Licence Type (%) 6.4 6.6 7 25.6 26.4 28

Proportion of Licence Type 2025 (%) 13.6% 14.0% 14.9% 18.4% 19.0% 20.1%

The granularity of the TfL private hire survey responses shows finish time within two-hour timeslots. Because there is no

indication of when in a two-hour timeslot a driver may finish the day’s shift it is assumed that there is an even chance of

them finishing at each half hour interval (e.g. for timeslot 00:00 to 01:59 the finish times are: 00:00, 00:30, 01:00, 01:30).

Similarly, the end of shift time does not necessarily align with the plug-in time as there is some time required for the

commute from the final job back to the driver’s home.

The estimation for the commute time home is based on the following survey results: 56 % travel 10 miles or less and

25% travel more than 20 miles. As the maximum granularity of the profiles is half hourly the commute time home is

assumed to be half an hour except for at peak times when, to account for traffic, a one-hour average commute is

assumed.

If we again consider the time slot from 00:00 – 01:59 in the TfL survey, then irrespective of which half-hour of the time

slot the shift finishes within (whether it is 00:00, 00:30. 01:00 or 01:30), we assume that once we allow for the 30-minute

commute time that the vehicle will be at home by 02:00. Hence the profile assumes that the vehicle will (with 100%

certainty) be connected and charging by 02.00 (end shift (01:30) plus commute time (30 minutes)).

For other, earlier, finish times, there is a reduced likelihood of the vehicle charging. As we assume that the shift finish is

evenly distributed through the two-hour time slot, this means that by 01:30, 75% of the vehicles will be back home at

charging. Therefore, this is expressed in the charging profile as 7kW x 0.75. By the same logic, at 01:00 there is a 50%

chance of any of the vehicles charging (7kW x 0.5), while at 00:30 there is a 25% chance (7kW x 0.25).



This process illustrates the way in which the values in the ramp at the start of the charging profile are obtained.

Additional detail concerning the method by which the individual two-hour time slots were aggregated together to form the

three charging profiles illustrated in Figure 10 and Figure 11 can be found in Appendix B – Half-Hourly Charging Profiles.

6.2 Optimised Time-Shifting Scenario Profiles

Having considered the likely charging profiles in a world of unconstrained charging, it is now necessary to understand

how these profiles might alter under different smart charging regimes.

The first of these to consider is that of time-shifted charging. The difference in this set of profiles compared to

unconstrained charging is that the start of charge is not directly correlated with the time the driver returns home. It is

assumed that the vehicle starts its charge cycle at a time offset from the time the driver returns home, either because the

consumer makes use of preferential off-peak tariffs (through later plug-in or through uses of timers) or because the DNO

or a third party uses the flexibility to delay charging to protect the network. This is reflected in the step change from 0kW

to the maximum charge rate in the examples in Figure 13.

Figure 13: Optimised time-shifting scenario profiles for black cab drivers

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This shape represents a non-aggregated charging profile which can be time-shifted, within certain limits, by different

amounts, for each vehicle.

According to the TfL survey, 69% of drivers work 11 hours or less though this does not mean they and their vehicles are

at home for 13 hours. The ‘latest unplug time’ in Table 4 is conservative to allow for the fact that the majority of drivers

will use their vehicle outside their shift hours. The stated ‘start of shift/latest unplug time’ ensures that charging will be

complete before the time that the driver requires the vehicle. Figure 14 shows some examples of non-aggregated profiles

based on the criteria set out in Table 4.

Figure 14: Set of considered daily shift patterns (non-aggregated profiles)

Figure 14 illustrates where the flexibility is for managed charging:

• During ‘Driving’ periods (red bars in Figure 14) domestic charging will almost definitely not take place.

• The ‘Charging’ period (green bars in Figure 14) represents possible plug-in and hence start of charging indicating also the duration needed to recharge vehicle if using the full capabilities (3.5kW or 7kW).

• ‘Available for charging’ (the yellow bars in Figure 14) represent periods which are available for charging to be shifted to, in order to mitigate network constraints.

Imperial College use an optimisation technique to move each charging profile, within the limits specified, to minimise network investment. An example of what this looks like for a representative distribution substation in LPN is shown in Figure 15. The substation has 20 ZEC private hire vehicles across the six different private hire charging profile types (P1 to P6). (Note that this substation did not have any black cabs present.) Taking into account the local conditions at the substation (namely the base load profile) the start of charging of ten ZEC private hire vehicles are delayed in order to reduce total peak demand. It is important to note that this optimised time-shifting represents the maximum level of benefit that could be achieved in terms of utilising spare capacity on the network. It is not true to say that this level of benefit would be achieved through static time of use tariffs, but rather requires a dynamic element. It requires disaggregated management of individual vehicles, far more sophisticated than static applications. However, it may be possible for this level of management to be achieved via aggregator platforms, but any commercial and technical framework between network operators, retailers,

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aggregators and customers required to realise this would still need to be agreed meaning that this represents very much a ‘best case’ scenario.

Figure 15: Daily demand profile (original profile on the left and time-shifted profile on the right) where some charging demand is shifted away from the peak (18.00) and introduced at low demand times (around 04.00)

6.3 Managed Charge Rate Scenario Profiles

An alternative smart charging approach is to scale back the level of charge received at peak times, rather than shift the

charging away from these peak times.

These reduced charging rate profiles can be determined dynamically depending on the network load and number of

vehicles connected to any given substation/feeder. The charge rate of each plugged-in vehicle is reduced to ensure that

the total demand from charging vehicles remains within the capacity available at the substation feeding that part of the

network.

It is not possible to fully, dynamically carry out the above methodology with the LRE tool that is being used within this

project. Furthermore, from a customer acceptability point of view there will be a cut-off point beyond which the charge

rate for a vehicle’s charge rate cannot be turned down. Learning from other projects will inform this more fully over time.

For the purposes of this project, profiles have been created in which the current drawn is limited to 50% of its maximum

capabilities to illustrate the potential advantages of this sort of smart solution, while ensuring that the vehicle will be fully

charged ahead of the next shift. This 50% reduction is an estimate at this stage, which is currently being tested and will

be further validated by the Electric Nation project. This will determine whether the solution is sufficient in alleviating

network problems. An example of this is shown in Figure 16 next to the unconstrained charging scenario.

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7 Forecasting

Clearly, when considering the likely impact of electric black cab and private hire vehicles on the distribution network, it is

important to understand how the volume of these vehicles will change over time.

The Mayor’s Transport Strategy sets out a target of 9,000 ZEC taxis in the fleet by 2020 (Mayor of London, 2018). The

Mayor, together with TfL, is offering financial incentives and installing charging infrastructure to achieve this target.

Figure 16: Charging profiles for black cabs in the unconstrained charging and managed charging scenarios

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When performing the modelling of the impact of electric black cab and private hire domestic charging on

the distribution network, it is essential to have an understanding of when the electrification of this fleet

will occur. This section shows the projected uptake of electric black cab and private hire vehicles over the

period until all licences are expected to be ZEC (i.e the fleet will be 100% electrified).



At present, TfL do not have a forecast of how the total population size may change over the coming years, though they

do publish total licence numbers for previous years and they also hold data on the number of licences of each age (up

to the respective limits for each licence type). The uptake of ZEC taxis set out in this project is therefore based on the

normal rate of churn of licensed taxis and the imperative for taxi vehicle owners to transition to ZEC in order to licence

new vehicles. While this leads to fewer than 9,000 taxis being ZEC by 2020, this represents the lowest number of ZEC

taxis that will be on the streets in this timescale and as such sets something of a minimum level of additional electrical

taxi volumes on UKPN’s network. It is acknowledged that it is very likely that the uptake rate of ZEC taxis will be faster

than that set out in this study but there is some uncertainty around how this might manifest (for example, will there be a

linear uptake of ZEC vehicles, or will there be a tipping point after which uptake will rapidly increase?). Given these

uncertainties, it was felt that using the licence expiry date and the churn of vehicles as the driver for the uptake was

appropriate for this project. Table 5 shows the total driver and vehicle licence numbers (TfL, 2017). The way in which this

is used to create the forecasts for the black cabs and private hire vehicles is described in greater detail in the sections

following. In summary:

• The change in driver numbers (’∆ Drivers’ in Table 5) is used to predict total licence numbers in the coming years.

• The vehicles/drivers ratio (’Vehicles/ Drivers Ratio’ in Table 5) is used to avoid double counting vehicles that are

shared between drivers. This ratio is applied to the number of drivers mapped to each substation.

• The age of the existing licences (provided by TfL) is used to predict when they will be renewed.

Table 5: TfL figures for total number of driver and vehicles licences of each type by year (TfL, 2017)

Year

Black cab licences Private hire licences

Drivers Vehicles ∆ Drivers Vehicles/ Drivers

Ratio Drivers Vehicles ∆ Driver

Vehicles / Drivers Ratio

09/10 24,914 22,445 - 0.901 59,191 49,355 - 0.834

10/11 25,070 22,558 156 0.900 61,200 50,663 2,009 0.828

11/12 25,336 23,099 266 0.912 64,063 53,960 2,863 0.842

12/13 25,460 22,168 124 0.871 66,975 49,854 2,912 0.744

13/14 25,538 22,810 78 0.893 65,656 52,811 -1,319 0.804

14/15 25,232 22,500 -306 0.892 78,690 62,724 13,034 0.797

15/16 24,870 21,759 -362 0.875 101,434 78,139 22,744 0.770

16/17 24,487 21,300 -383 0.870 117,712 87,409 16,278 0.743

The figures in Table 5 have been used in the manner described in sections 7.1 and 7.2 to determine the forecast uptake

of ZEC vehicles volumes shown in Figure 17. The graph shows uptake rate increasing from about 2024, this is because



of the licensing requirement change for the private hire population in 2023. PHVs make up approximately 85% of the

combined group of black cab and private hire vehicles.

7.1 Black cabs

7.1.1 Total Licences

The average year on year change in the total number of black cab driver licences over the last seven years is -61 (from

Table 5). This is insignificant compared to the total population (less than 1%) and so, in the model, no annual change is

assumed, particularly as it is highly possible that there is a negative skew on this figure as a result of new entrants into

the market. The appearance of new technological platforms can be linked to the increase in the number of private hire

drivers over the same time period and at the time of writing there is a degree of uncertainty as to how the legislation will

develop with respect to these market disruptors.

7.1.2 Vehicles/Drivers Ratio

The ratio of vehicles to drivers for black cabs (Table 5) is falling year on year indicating more vehicle sharing. The latest

data shows that for every 100 drivers there are 86 black cabs. Predictions for the future are based on excluding the

anomaly in year 2012/13 and drawing a line of best fit (Figure 18). The equation for this line of best fit is used to determine

the ratio until 2023 at which point the ratio is kept constant because, for practical and logistical reasons, concerned with

Figure 17: Forecast uptake of ZEC black cab and private hire vehicle licences (left) and driver

licences (right)

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shift patterns and geography, the amount of vehicle sharing is limited. People cannot share a vehicle if their shifts overlap

and it would be impractical to share a vehicle with someone who lives and works far away.

7.1.3 Licence Renewals

Black cab vehicles can be licenced for up to 15 years. Although a small number of exemptions are permitted which allow

a black cab to be licensed for a further five years (currently 0.05% of the population). As part of this project, TfL provided

data concerning the number of licences of each age. All licences that are 15 years old (6% of the population) will need

to be replaced by ZEC vehicles in 2018, all licences that are 14 years old (6% of the population) will be replaced by ZEC

vehicles in 2019 and so on. (It is assumed that all vehicles coming to the end of their licences will be replaced.)

Based on work described in the above sections, 49% of the 6% changing their licence will have the day shift charging

profile and 51% will have the evening shift charging profile (i.e. 2.94% and 3.06% respectively) as illustrated in Table 9

in the Appendix.

7.2 Private hire vehicles

7.2.1 Total Licences

A similar process has been followed for private hire vehicles. The relevant numbers from Table 5 are shown in Figure 19

where it can be seen that the values for 2014 to 2016 are more than four times larger than the largest value seen in

previous years, hence they are excluded in the calculation of the rate of change. To reflect the upward general trend for

the change in vehicle numbers and to avoid an overly pessimistic growth rate, the negative value at year 2013 is also

Figure 18: Line of best fit on black cab vehicle/driver ratio graph.

y = -0.0048x + 10.553

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excluded leaving an average year on year growth rate from the remaining figures of 2,595. This figure is rounded up and

an average year on year growth rate for the total number of private hire licences of 2,600 is applied until 2025.

By 2025 this would mean that the total number of private hire driver licences would be around 135,000 (representing

approximately 95,000 vehicle licences).

It is unclear at present whether this continued growth is sustainable or necessary and so for this forecast, the total number

of private hire licences is shown as remaining constant after 2025. The forecast should be revisited periodically to assess

any changes in this (which may be driven by regulation or market forces).

However, not all of these vehicles are ZEC. Table 6 illustrates the proportion of the population of private hire vehicles

that are ZEC in any given year. By 2025, for example, it can be seen that 17% of all private hire vehicles are ZEC,

representing approximately 16,000 vehicle licences.

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Table 6: Forecast uptake of ZEC vehicles among private hire drivers

In order to understand the relationship between vehicle licences and driver licences, the proportion of private hire

population with ZEC vehicles is then multiplied by the vehicle/driver ratio (calculated based on the line of best fit

equation shown in Figure 20) and the profile ratios (Table 5) to get the overall year on year uptake rates for each

charging profile results (shown in Table 10 in the Appendix).

Year Proportion due

for renewal Proportion of renewals that

change to ZEC in any given year

Cumulative proportion of private hire population with ZEC vehicles

(accounting for population growth)

2017 - 2.0% -

2018 0.9% 3.8% 2%

2019 2.8% 7.4% 4%

2020 4.2% 14.1% 5%

2021 8.3% 27.1% 7%

2022 11.1% 52% 8%

2023 16.2% 100% 10%

2024 14.7% 100% 13%

2025 13.3% 100% 17%

2026 11.4% 100% 25%

2027 9.3% 100% 35%

2028 8.0% 100% 49%

2029 0.8% 100% 62%

2030 2.6% 100% 74%

2031 3.6% 100% 85%

2032 6.0% 100% 93%

2033 5.3% 100% 100%



7.2.2 Renewals

PHV licences have a duration of 10 years but the ZEC requirements will only be enforced from 2023 (TfL, 2018). For

those licences that are renewed before 2023 only a proportion will choose ZEC vehicles, this is reflected in the second

column, ‘proportion due for renewal’, in Table 6. (It should also be noted that from 2020, any newly licenced private hire

vehicles under 18 months old must be ZEC.) These figures are based on the number of licences of each age and derived

in the same way as for black cabs. From 2029 the figures come from the licences that are renewed in 2018 but without

a ZEC because that licence will have expired and they will be required to obtain a ZEC vehicle.

Currently 2% of private hire drivers have made the switch to ZEC vehicles (according to the TfL survey carried out in

November 2016). This is similar to the uptake in the general population and so a similar growth curve can be expected

between this level and 2023 when 100% of new licences will be required to be ZEC (see column two, ‘Proportion of

renewals that change to ZEC in any given year’ in Table 6).

Figure 20: Graph to show line of best fit on private hire vehicles/drivers ratio by year.

y = 8E+13e-0.016x

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8 Simulations

The project selected to use Imperial College London’s Load Related Expenditure (LRE) model for the analysis of

expenditure requirements driven by the residential charging of black cabs and private hire vehicles. This model was

selected as it is used by UKPN for investment decisions, as well as in previous innovation projects, thereby offering a

degree of consistency with other analysis that has been carried out.

The LRE Model provides a system maximum study and therefore models the simultaneous load across the entire system.

The LRE Model provides high level information on a number of assets including EHV circuits, HV circuits, secondary

distribution sites and the LV circuits in a whole network study. The complete model comprises three smaller models, an

EHV model, HV model and LV model and considers thermal, voltage and fault level investment drivers. The LRE model,

because it looks at the whole network, will predict overloaded circuits as well as substations and it uses this output to

estimate required reinforcement expenditure. Each of UKPN’s three licences areas (LPN, SPN and EPN) are simulated

and the total values for UKPN are shown in section 8.1.

The underpinning load is as defined by Element Energy Forecasts updated in January 2018 for the uptake levels and

trends seen for heat pumps, domestic EVs and other base loads, including both domestic and industrial and commercial

customers.

In order to allocate vehicles to substations, they could have been smeared across the population, resulting in ‘portions’

of vehicle demand appearing on different substations. This was decided against. Instead, the vehicle allocation was

randomised, thereby ensuring that a full charging demand was seen at a substation. This represents a more ‘real world’

view and is more likely to draw out the network challenges posed as it is more likely to avoid understating the contribution

of individual vehicle transitioning to electric at local substations.

To check that the random allocation was a fair representation three different random allocation or ‘distribution’ runs were

carried out and the variance between each distribution and the original forecast assessed. For each assessment, the

distributions tended to be within 3% of the aforementioned forecasts, giving a high degree of confidence that the results

were not sensitive to the distribution selected. The distribution which gave the closest to the original forecast was selected

as the default distribution upon which the following results are based.

More detail on the variations between the distributions can be found in Appendix A – Supplementary Tables: Profile

Proportions and Forecasts.

This section of the report shows some of the results obtained by Imperial College London through their

LRE model that was used to evaluate the changes to demand caused by charging black cab and private

hire vehicles at home, and also the network investment requirements arising from this additional demand.



8.1 Additional Network Demand

In the unconstrained charging scenario, by 2033 (the year by which all black cabs and private hires are expected to be

ZEC) LPN’s black cab and private hire demand contribution is 105MW.

EPN is the second most populated area and the black cab and private hire drivers’ maximum contribution amounts to

76MW by 2033.

For SPN the contribution in the same period is 41MW.

In total that represents an extra 222MW of load at its peak on UKPN’s primary substations and slightly less on secondary

substations due to IDNO presence.

Figure 21: The load contribution from black cab and private hire vehicles in 2033.

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For the period between 19:00 and 04:30, it can be seen that the contribution is always upwards of 140MW across the

three licence areas in total (‘UKPN Total’ line in Figure 21).

The flat period on the curve in Figure 21 between 09:15 and 15:30 is not because there are no black cabs or private hire

vehicles charging between those times but because their contribution in this period is relatively less significant (both in

terms of absolute size (20% for Private hires and 7% for Black Cabs) and in terms of the total network load between

these times). The charging profiles used in the project featured this flat period of no charging demand during this time,

although clearly this is a simplification and in reality, this will not be the case.

However, the focus is on those areas where charging demand is coincident with higher network demand as this is where

the investment needs arise.

The day-to-day variability in shift times, particularly for private hire drivers, tells us that in reality it is also possible that

drivers normally finishing work during the day will be plugging in during the evening too. Section 6.1.2 and Appendix B –

Half-Hourly Charging Profiles show how the various shift patterns were grouped to include all drivers but within three

shift groups. The same was done with the black cabs (see section 6.1.1) but in two shift patterns.

Hence, whereas the ‘UKPN Total’ line shows the modelled profile contributions the ‘estimated real load’ line shows

another view of what the actual load could look like to account for this variability While this is higher during the daytime

‘trough’, it does result in slightly lower peaks, but the contribution from the black cab and private hire load remains

significant at traditional network peak times. It must be recalled that these demands account for the diversity in the plug-

in and plug-out times of drivers with the same shift patterns (i.e. not all drivers charge at exactly the same time).

From this graph it can be observed that black cabs and private hire vehicles cause later evening peaks than might be

expected of typical domestic drivers (particularly black cab drivers), which leads to a peak addition of load in the middle

of the night. Although the volume of private hire vehicles is considerably greater, in the main they have smaller battery

sizes than the black cabs, meaning that their contribution to peak load is somewhat diluted. The modelling is based on

the survey results which also showed greater levels of diversity in the private hire drivers’ shift patterns, further

reducing the peak contribution.

8.2 Network Investment

The LRE results are split into three levels:

• LV;

• HV (which includes distribution transformers); and

• EHV which includes switchgear from the 11kV side of primary substation and higher voltages; primary and main/bulk substations and 132kV networks.



In an unconstrained charging regime, UKPN’s total costs arising from the charging of black cabs and private hire vehicles

at home to 2032 are approximately £14m on a Net Present Value (NPV) basis. From the simulations it can be seen that

EHV reinforcement costs represent just over half of the total reinforcement costs.

It should be noted that the EHV reinforcement values come from the costs associated with installing additional

transformers. However, the model does not account for the possibility of transferring loads between primary substations

and this load shifting is a common practice readily available for the volumes required from the additional loads from black

cabs and private hire vehicles. In practical terms the location of the excess load and its comparison to the available

capacities on adjacent substations needs to be taken into account to check where and when this solution is viable to

delay the need for costly reinforcement. This has not been considered within the LRE modelling approach, which means

that the EHV values provided in may be overestimates.

The reinforcement costs on the LV networks are driven by thermal problems which, for underground parts of the network

can only be fixed by reducing the peak load or by replacing the cables with higher rated assets. Where there are

undervoltage problems (arising from a fall in voltage below the statutory limits) it is possible that some voltage regulation

solutions could be attempted. The model already accounts for the solar PV generation on the network which can also

alleviate daytime undervoltage problems in summer time. Voltage stability also becomes a problem towards the end of

the modelled time period, albeit to a lesser extent.

For the reasons stated around the options available for resolving problems at each of the voltage levels, the estimated

reinforcement costs at LV are subject to the least volatility while the costs at EHV are subject to the most volatility.

Figure 22: NPV costs for each of the scenarios modelled for different voltage levels.

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When using smart charging approaches (as set out by the profiles in the optimised time-shifting scenario and the

managed charging scenario (section 6.2 and section 6.3)), it can be seen that smart solutions can have a considerable

impact on the potential cost of network reinforcements associated with the transition to electric black cabs and private

hire vehicles.

On an NPV basis there is a reduction of over 70% when considering the optimised time-shifted solution (from almost

£14m down to under £4m), while the savings for the managed charging approach are more modest, providing a reduction

to just under £12m.

Smart charging can allow for both time-shifting and charge rate management. The vast savings in the optimised time-

shifting scenario show that peak capacity is a problem but that there is headroom available outside peak times. The

modelling however shows the best possible scenario in time-shifting profiles without taking into account whether those

shifts would be acceptable to the consumer.

Therefore, it is likely that in reality not all of this demand could be shifted in this way, resulting in an investment that falls

between the two figures of £4m and £14m. The modelling does, however, clearly demonstrate the benefits of this

approach. If this were to be coupled with potential savings to the end customer (through variable pricing, meaning

reduced energy bills off-peak) to incentivise this behaviour, then that would be likely to have a beneficial effect on uptake

of the solution and hence on savings in network expenditure.

9 Stakeholder Engagement

While this project has thus far focused on understanding the uptake rates of electric vehicles and devising methods by

which their impact in the distribution network can be quantified, it is important to recognise that a significant part of this

work is concerned with engaging the black cab and private hire driver communities to understand their views on the

transition and to initiate wider stakeholder activities that will be performed by UKPN over the coming years.

This section of the report details the methods by which various stakeholders in the black cab and private

hire community have been engaged by the project. It also revisits some of the assumptions that were set

out to determine whether they are valid when tested with stakeholders. At the close of the project, there

will be an event to disseminate findings and the feedback from this event will be further taken into

consideration as part of the stakeholder engagement and added as an Appendix to this report. It is

expected that this will inform a longer-term strategy for UKPN (to be released separately) in engaging with

this sector to ensure that electrification of the black cab and private hire fleet is achieved in partnership

with the distribution network, ensuring value for all parties.



This stakeholder engagement has been performed in a variety of ways, namely through:

1) Online surveys

2) Charging logs

3) Face-to-face sessions

4) Workshops

Through this engagement the project was able to test the assumptions made in creating the shift and charging profiles

and also to seek opinions on the move to electric, including the perceived challenges and benefits.

It is important to note that due to the differences in licensing terms and requirements, where possible, for the black cab

and private hire vehicles and their drivers the engagement activities were carried out separately for the two groups. For

example, 94% of black cab driver survey respondents use on-street hails and 84% use black cab ranks to get work

while these methods are not available to private hire drivers. It is therefore appropriate to seek views separately

regarding the challenges surrounding electrification in the day-to-day work of the various drivers.

The outputs of the black cab driver face-to-face sessions, the charging log and workshop sessions will be detailed in an

annex which will be released after the stakeholder event, scheduled to take place on 7th March 2018.

9.1 Online Survey Responses

Two online surveys (one for black cab drivers and one for private hire drivers) which were open from 21st December to

28th January and advertised by TfL to their licensed drivers on a number of occasions throughout this period resulted in

291 responses from black cab drivers and 118 responses from private hire drivers. The higher response rate from the

smaller driver group (black cabs) is likely reflecting the timings of the rule changes for each group. In this section, where

appropriate, some learning from the face-to-face sessions will be brought in.

The online survey provided evidence in favour of some of the project’s assumptions and also evidence suggesting other

assumptions (regarding the uptake) may have under estimated (Table 7). Through face-to-face interviews it became

apparent that there may be a multitude of reasons for changing the vehicle before the end of the licence, such as:

• passenger comfort,

• driver comfort,

• excess mileage causing wear, and

• growing maintenance costs and downtime.

Although the drivers of plug-in vehicles interviewed in the face-to-face sessions had not had their vehicles long enough

to recognise a difference in maintenance the knowledge of ‘fewer than 20 moving parts’ for battery electric vehicles was

sufficient to give them peace of mind. For the BEV driver the Nissan offer of a free pick-up in the event of a

breakdown/battery power loss also allayed any potential concerns over the novel technology. With the move to electric,

survey respondents raised issues around infrastructure for both domestic and public charging. Those living in (leasehold)

flats are dependent on management company approval, while those in rented accommodation also rely on landlord

approval which in some cases is the local council.



Table 7: Project assumptions tested against black cab and private hire driver survey results.

Assumptions in profile

creation Statistics from Surveys Assumption upheld?

Drivers use their work

vehicle to travel to and

from work

99% of black cab (BC) drivers and

97% of private hire (PH) drivers

said Yes

Yes

(The majority of) drivers

do not share their

vehicles

93% of BC drivers and 94% of PH

drivers said Yes

Yes (though 5% of the population said they work

different shifts in the same day suggesting that in

the future these drivers would use rapid charging

infrastructure only

Drivers keep their vehicle

at home


drivers said Yes Yes

Vehicles replaced every

15 years at the end of a

licence

74% of BC drivers said they keep

their vehicles for less than 10

years

89% of PH drivers said they keep

their vehicles for less than 5 years

Uncertain, it is possible that the people who

change their vehicles sooner than every 10 years

replace their vehicle with a second-hand vehicle

that has had its licence renewed, so the stock of

vehicles may be as predicted. This could be

further investigated as part of the longer-term

engagement strategy.

Breaks – taken in the city

for short periods

c. 30% of BC drivers and 25% of

PH drivers stated no breaks.

74% BC and 60% of PH drivers

said breaks less than 30 minutes

- Any of your breaks at home?


drivers said yes

Mostly. The 31% of private hire drivers taking

some breaks at home may choose to also top up

their vehicles batteries adding to the LV demand.

Further investigation is required to understand

when, for how long and where this would be.

There are not sufficient numbers of plug-in vehicle drivers (15 respondents to the private hire survey) to draw statistically

significant conclusions; however, the answers given offer some interesting insights that should be investigated further.

From the responses, only 30% of those drivers with a plug-in private hire vehicle stated that they have a specialised

domestic charging point installed at home. Based on further engagement with plug-in owning private hire drivers, it was

found that some use three pin plugs. A large proportion reported anticipated difficulty in having a charger installed owing

to the format of their accommodation (i.e. they live in a top floor apartment with no allocated parking) or due to the

ownership of their accommodation (i.e. they rent their home and such installation would be at the discretion of the

landlord).

Almost half of plug-in private hire vehicle drivers reported that they ‘almost never’ charge on public charging infrastructure

and about a quarter charge twice a week or less at home. While regular charging is not essential for plug-in hybrids and

range extender vehicles, it is not clear what the motives of the respondents are and whether it is primarily driven by a



lack of knowledge or will, or a lack of opportunity. It is important to understand how and when attitudes and hence

behaviours may change for these drivers.

Another key output is the shift variation. 20% of black cab drivers said their shifts varied more than 90 minutes each day,

while 39% of private hire drivers said their shifts varied by more than 90 minutes each day (Figure 23). This level of

variability poses a challenge to modelling methods as it will clearly affect the charging profile and hence the

modelling/simulation outputs; but to understand in what manner and how frequently this occurs, more data needs to be

gathered.

Figure 23: Online survey responses to – “How much does your shift pattern vary on a daily basis?”

In order to gain further understanding of the survey findings, the face-to-face session was developed to explore some

of these issues more deeply.

9.2 Private Hire Driver Face-to-Face sessions

From speaking with drivers, it is apparent that there is a range of interests and knowledge on the topic of EVs; from those

without knowledge of battery capacities and charging connectors/rates to those who are enthusiasts and aware of the

latest developments. The latter group of people are likely to use more apps, be signed up to a number of charging

providers, and know the best places to plug-in.

To help the narrative in this section, short profiles of each of the drivers interviewed are shown in Table 8.

10%

23%

28%

13%

26%

Private Hire Driver Responses

less than 30 mins

30-60 mins

60-90 mins

90-180 mins

more than 180mins

20%

38%

22%

9%

11%

Black Cab Driver Survey Responses

less than 30 mins

30-60 mins

60-90 mins

90-180 mins

more than180mins



Table 8: Interviewed driver profiles (*not real names).

Driver Peter* Lena* Warren* Ashley*

Vehicle type

(electric/hybrid)

Electric Hybrid Hybrid Euro-6

Vehicle Make Nissan Mercedes Mercedes Mercedes

Work through Hailing-

App

Booking

company

Booking

company

Booking

company

The face-to-face sessions were structured to gain insights in three key areas:

1. The driver’s day to day - shift variability, breaks, driver and passenger experience

2. The move to electric – the vehicles and charging

3. Their interface with electricity networks – the DNO, smart charging, future engagement (due to lack of previous

exposure to DNOs this involved mostly dissemination)

9.2.1 Driver’s Day to Day

There are significant differences in shift patterns depending on who the major customer is. For those servicing corporate

clients (mainly executive car drivers) there is considerably less (or no) work at weekends and on Friday nights, exactly

the times when there is the most work for those working for the general public (through ride-hailing apps). These drivers

prefer to take Mondays and Tuesdays off rather than giving up the potential income from working the weekends.

Corporate clients hold accounts with booking companies who also own fleets and lease these to the drivers. The drivers

are then incentivised to work a certain number of jobs so although most try to maintain a common start time the end time

will be dependent on demand.

Drivers reported other factors affecting shift patterns, namely commitments outside of work such as family. In order to be

able to pick up the children from school each day, Ashley, for example, does more days to make up for fewer hours done

each day. Peter said the constraint on the number of jobs or the types of jobs was down to the mileage of his BEV. Since

part-exchanging his previous internal combustion engine (ICE) car for a full electric Nissan, Peter has become more

conscious of driving techniques to extend the range. His choice in the vehicle model was significantly influenced by his

consideration of passenger comfort, namely in the form of space in the back. He looks forward to upgrading to a larger

battery capacity which will allow more airport journeys as these longer distance journeys are more lucrative for drivers.

The project is interested in the idea of ‘breaks’ as these could be times for opportunistic charging. Expanding on the

survey answers saying, ‘no breaks’ or ‘what are breaks?’ the hybrid drivers in our face-to-face interviews highlighted the

fact that the closest semblance to a ‘break’ is probably the time spent waiting for passengers who have booked them,

meaning the location is determined by the customer, not the driver. This means that in the vast majority of cases, they

are not near a charging point and if they were it may not be appropriate for them to leave the vehicle and plug in as they

do not necessarily know how soon the passenger will arrive. For the electric driver, opportunistic city centre charging

was a must and was driven by when the dashboard showed remaining capacity for 30 miles.



The lack of ability to park anywhere was highlighted as a significant issue, with drivers reported being fined for leaving

their car for minutes. This may represent an opportunity for charging infrastructure operators to utilise the need for parking

with points and bays for their subscribers or users.

9.2.2 The Move to Electric

For thousands of drivers across the capital, the decision on the ZEC vehicle make and model will be made by the fleet

operators for whom they work for and lease or rent their car from. One booking company consulted some of their drivers

(such as Eleanor and Warren) and their key account-holding clients in that decision for their new range of hybrid executive

cars. These companies would be well placed to inform drivers on the new technologies. Irrespective of the type of client,

for the greatest flexibility in earning power the size of the vehicle is important in enabling the transport of larger groups

and groups with luggage and ensuring good ratings for passenger comfort.

Anecdotally it would seem that there is a high demand from customers for ZEC vehicle journeys with increasing numbers

of customers seemingly asking for electric cars and certain accounts set ‘eco’ as their preference for vehicles. The

problem comes in the lack of choice for large ZEC vehicles and the limitations are more severe for executive drivers. For

those wishing to purchase and own their vehicle, the upfront cost differential is key, particularly when there is not a

quantitative appreciation of the fuel savings.

The increased price tag for a vehicle that is largely the same as its ICE driven counterpart requires a change in thinking

from just considering the upfront price tag to the total cost of ownership. Though drivers were aware of the fuel economy

they were not aware of how much it could actually save per week. In conversation, the cost differential on the reported

weekly spend on fuel of up to £200 would be significant. There are also maintenance benefits associated with the orders

of magnitude fewer moving parts, i.e. fewer parts that can wear and break.

It is also hard to quantify the benefits of the unanimously agreed upon better drive quality, smoother ride and reduced

noise. The driver quality is important to drivers because of the amount of time they spend in the vehicle. Interestingly,

anecdotally the fuel saving functionality of ‘automatic stop starting’ on ICE vehicles was disabled by a large number of

drivers because of the poor experience with it in traffic (a commonality in London) leaving vehicles idling more. This type

of driving, in traffic and at low speeds is ideal for EVs because of regenerative breaking and lack of engine idling.

9.2.3 Interface with the DNO

One of the drivers interviewed showed familiarity with the logo stating that they had seen it on vans around the city.

Besides this there was no knowledge of the DNO function and operations. When this was explained the drivers showed

interest in understanding the network problems with peak demand and EV loads and reported that the best way to be

made aware of future projects was through the TfL newsletter.

It should be noted that the drivers interviewed had no particular ties to driver associations or unions.



10 Conclusions and Recommendations

The key learning and findings from the report are as follows:

Network Modelling Findings

1. Analysis shows that 59% of the distribution substations in the LPN area (equivalent to 7,724) supply the postcode

of at least one black cab or private hire vehicle. Clustering is potentially a significant issue as 513 of these

substations have more than 20 black cab and private hire vehicles connected.

2. The potential implication of the electrification of the black cab and private hire fleet is to add 222MW to UKPN

demand levels across the three licence areas

3. The network investment that would be required over the transition period (2018 – 2033) would be of the order of

almost £14m in discounted NPV terms if vehicles charge in an unconstrained manner.

4. Smarter charging approaches can yield significant benefits, with optimised time-shifted charging yielding up to a

71% reduction in this investment, and managed charging showing a lesser benefit of the order of 15% (although

the latter is based on assumed 50% reduced charging rates, which could in reality be calculated dynamically

based on local network conditions, meaning that more expenditure could potentially be avoided or deferred). The

optimised time-shifted charging described here requires a highly sophisticated level of local control and

coordination meaning that, in actuality, the likely benefit of smart charging will fall somewhere between these

figures of 15% and 71%.

5. The modelling has shown that black cabs and private hire vehicles cause later evening peaks than might be

expected of typical domestic drivers (particularly black cab drivers), which leads to a peak addition of load in

the middle of the night. Although the volume of private hire vehicles is considerably greater, in the main they

have smaller battery sizes than the black cabs, meaning that their contribution to peak load is somewhat

diluted. The modelling is based on the survey results which also showed greater levels of diversity in the

private hire drivers’ shift patterns, further reducing the peak contribution.

This project has successfully initiated the process of engaging with the black cab and private hire

community in London, as they begin their transition to ZEC vehicles. Key learning and findings from the

report are given in this section.



Stakeholder Engagement Findings

This project has successfully initiated the process of engaging with the black cab and private hire driver communities in

London, who will be among the first demographics in the country to transition to ZEC vehicles.

New online surveys engaging 400 drivers9, plus face-to-face sessions and workshops have formed key activities in this

Black Cab Green project, laying the foundations for UKPN’s longer-term stakeholder engagement strategy. Of the survey

participants:

1. Over 93% of respondents keep their vehicle at home, use it to get to and from work and do not share it

with any other driver.

2. 20% of black cab drivers’ shifts vary more than 90 minutes each day, while 39% of private hire drivers’

shifts vary by more than 90 minutes each day. This level of variability poses a challenge to modelling methods

as it affects the charging profile and hence the modelling outputs. To better understand the variability and its’

impacts more data needs to be gathered.

3. 88% of private hire drivers keep their vehicle for less than 5 years (licence could be valid for up to 10

years) and 74% of black cab drivers keep their vehicles for less than 10 years (licence could be valid for

up to 15 years). This suggests that uptake levels for ZEC vehicles may be accelerated such that the transition

occurs sooner than dictated by the vehicle licence age limits used in the forecasts for this project. In some cases,

the move to electric is market led. As more key account holders at booking companies request ‘eco’ preference

for their journeys the fleet operators upgrade the vehicles they lease to drivers to reflect the customer demand.

It is suggested that the forecast be revisited at least annually to reflect the latest real uptake figures.

4. Only approximately 30% of those with plug-in private hire vehicles reported having specialised domestic

charging points. It should be noted that this is based on a small sample size of the early adopters. Information

on whether they had plans for installing charging points was not collected. During the face-to-face interviews it

was found that of those without domestic charging points some used three pin plugs to charge while others used

public infrastructure exclusively. When asked about the lack of charging points at home drivers responded with

one or more of the following reasons associated with their living arrangement:

a. rented accommodation (requiring permission for installations, as yet not sought from the landlord),

b. houses with no access to off-street parking,

c. living in a non-ground floor flat.

Recommendations and Next Steps

1. This project represents the first step on the journey for UKPN in its engagement with the black cab and private

hire stakeholder group. This project will inform the development of a longer-term engagement strategy that will

seek to address some of the points highlighted here via various channels, including through organisations such

as TfL.

9 A small minority of whom already operate EVs



2. The charging profiles used in this work have been derived and inferred from various data sources, such as official

industry surveys and interviews. These profiles could be refined, and their accuracy improved based on real

plug-in vehicle charging data. It is recommended that follow-up activity be pursued with the black cab and private

hire community to record actual charging behaviour over a sustained period of time, such as a year, to reflect

seasonal variation (perhaps via vehicle telematics or monitoring at charge points). The profiles can then be

updated, and the models re-executed to understand the implications these ‘real-world’ profiles have on network

demand and expenditure levels. This needs to be performed once there is a significant volume of ZEC black

cabs and private hire vehicles on the road, providing a richer data set than was available at the time of this study.

3. Forecasts for uptake levels and profiles based on battery technology projections will also need to be revisited in

the future. Once the number of electric vehicles used by black cab and private hire drivers become more

widespread, the profiles should be revisited to ensure they align with the technology available. Again, this could

result in re-executions of the models created in this work to understand the implications of the evolving

technology. Moreover, research for this project has given some indication that drivers keep their vehicles for less

than the maximum validity of the vehicle licence. As new vehicle licences are only permitted for ZEC vehicle this

could mean that the electrification of the fleet of black cab and private hire vehicles could happen sooner than

predicted in this project. This needs to be validated as new data comes to light. In addition, TfL is now collating figures for the number of private hire drivers and vehicles that operators have

available each week, and this is showing a gap between licensed drivers and those actively available to work so

this may need to be referenced and considered in the future as it could have an impact on the amount of charging

required.

TfL bring to the attention of readers that potential changes to the black cab and private hire licensing

requirements are continuously being considered and if these are introduced it is not known what impact these

will have on driver and vehicle numbers.

4. The potential benefits of using smarter charging approaches rather than unconstrained charging highlights the

importance of continuing to liaise cross-industry and with government and regulatory bodies. This will ensure the

potential benefits to customers that smarter charging could bring (in terms of reduced energy bills) are not missed

through inaction. If charging technology is widely rolled out without the capability to enact these charging options

(such as the functionality to time-shift or charge at a reduced rate), it could represent a significant missed

opportunity to drive out value for customers and other stakeholders.



11 References Dynamo Taxi. (2017, December 18). Technical specification. Retrieved from dynamotaxi.com:

http://www.dynamotaxi.com/tech-spec/ Element Energy. (2017, December 11). Plug-in Electric Vehicle Uptake and Infrastructure Impacts Study. Retrieved

from tfl.gov.uk: http://content.tfl.gov.uk/ev-uptake-and-infrastructure-impacts-study-updated-nov-2016.pdf

ENA. (2017, September 29). Who is my network operator. Retrieved from energynetworks.org:

http://www.energynetworks.org/info/faqs/who-is-my-network-operator.html

Mayor of London. (2018, March). Mayor's Transport Strategy. Retrieved from london.gov.uk: )

https://www.london.gov.uk/sites/default/files/mayors-transport-strategy-2018.pdf

Metrocab. (2017, December 11). Retrieved from Metrocab.com: https://metrocab.com/#0

Nissan. (2018, January 26). range charging. Retrieved from www.nissan.co.uk: https://www.nissan.co.uk/vehicles/new-

vehicles/leaf/range-charging.html

Renault. (2018, January 26). Zoe 250. Retrieved from www.renault.co.uk: https://www.renault.co.uk/vehicles/new-

vehicles/zoe-250.html

SMMT. (2017, November 24). EV Registrations. Retrieved from www.smmt.co.uk:

https://www.smmt.co.uk/2017/11/october-2017-ev-registrations/

Taylor, K. (2017, December 18). EV lamppost charging Hounslow. Retrieved from london.gov.uk:

https://www.london.gov.uk/sites/default/files/ev_lamppost_charging_hounslow.pdf

TfL. (2017, September 29). Electric Vehicle Charging Infrastrcture: Location Guidance for London. Retrieved from

tfl.gov.uk: http://content.tfl.gov.uk/electric-vehicle-charging-infrastructure-location-guidance-for-london.pdf

TfL. (2017, December 11). Licensing Information. Retrieved from tfl.gov.uk: https://tfl.gov.uk/info-for/taxis-and-private-

hire/licensing/licensing-information

TfL. (2018, January 15). Cleaner greener PHVs. Retrieved from tfl.gov.uk: https://tfl.gov.uk/modes/driving/ultra-low-

emission-zone/cleaner-greener-phvs

TfL. (2018, January 24). What to expect from your journey. Retrieved from TfL.gov.uk: https://tfl.gov.uk/modes/taxis-

and-minicabs/what-to-expect-from-your-journey



Appendix A – Supplementary Tables: Profile Proportions and Forecasts

Table 9: Black cab proportions across the two shift pattern profiles

Year Proportion of Black

Cabs

Proportions of Black Cabs

Profile

Total Day Shift Evening Shift

2018 6% 2.61% 2.72%

2019 6% 2.60% 2.70%

2020 5% 2.15% 2.24%

2021 7% 3.00% 3.12%

2022 7% 2.98% 3.10%

2023 10% 4.26% 4.43%

2024 6% 2.56% 2.66%

2025 6% 2.56% 2.66%

2026 7% 2.98% 3.10%

2027 6% 2.56% 2.66%

2028 6% 2.56% 2.66%

2029 5% 2.13% 2.22%

2030 7% 2.98% 3.10%

2031 6% 2.56% 2.66%

2032 5% 2.13% 2.22%

2033 5% 2.13% 2.22%



Table 10: Annual uptake by profile as a proportion of total private hire licences.

Private Hire Profiles Black Cab Profiles





Day Shift Evening

Shift

2018 0.10% 0.10% 0.10% 0.38% 0.39% 0.42%

2019 0.01% 0.01% 0.01% 0.04% 0.04% 0.04%

2020 0.04% 0.04% 0.04% 0.11% 0.11% 0.12%

2021 0.16% 0.17% 0.18% 0.39% 0.40% 0.43%

2022 0.47% 0.49% 0.52% 0.97% 1.00% 1.06%

2023 1.48% 1.53% 1.62% 2.63% 2.71% 2.87%

2024 1.50% 1.54% 1.64% 2.31% 2.38% 2.53%

2025 1.49% 1.54% 1.63% 2.01% 2.08% 2.20%

2026 1.28% 1.32% 1.49% 1.74% 1.79% 1.90%

2027 1.05% 1.08% 1.15% 1.42% 1.46% 1.55%

2028 0.90% 0.92% 0.98% 1.21% 1.25% 1.33%

2029 0.09% 0.10% 0.10% 0.13% 0.13% 0.14%

2030 0.29% 0.30% 0.31% 0.39% 0.40% 0.42%

2031 0.40% 0.41% 0.44% 0.54% 0.56% 0.59%

2032 0.68% 0.70% 0.74% 0.91% 0.94% 1.00%

2033 0.60% 0.61% 0.65% 0.81% 0.83% 0.88%



Table 11: Variation between the forecast uptake percentages and allocation of full profiles under Distribution 2.

Private Hire Black Cab

Year Early Evening Finisher






Day Shift

Evening Shift

2018 -1.46% -2.06% -0.90% -0.23% -0.27% -0.34% -0.16% -0.27%

2019 1.87% 2.90% 0.90% 0.10% -0.45% -0.27% -0.16% -0.27%

2020 -3.06% -2.38% -1.14% -0.89% 0.00% 0.41% 0.34% -0.27%

2021 3.05% -0.07% 0.29% 0.84% 0.50% -0.12% -0.21% -0.27%

2022 -1.03% 1.78% -1.29% -0.21% -0.38% 0.63% 0.10% -0.27%

2023 -1.66% -1.99% -0.10% 0.19% 0.14% 0.06% -0.09% -0.27%

2024 0.33% 0.53% 0.14% 0.08% 0.20% -0.09% 0.20% 0.43%

2025 0.28% 0.05% 0.16% -0.18% -0.29% -0.12% -0.16% -0.27%

2026 -0.01% -0.23% -0.11% 0.04% 0.12% 0.08% 0.10% -0.01%

2027 -0.07% 0.05% 0.05% -0.09% 0.04% -0.04% -0.16% 0.15%

2028 0.01% -0.03% 0.05% 0.08% -0.07% 0.07% 0.20% 0.01%

2029 0.03% 0.05% -0.07% 0.01% 0.06% -0.06% -0.09% -0.12%

2030 -0.11% 0.05% 0.16% -0.04% -0.02% 0.01% 0.10% 0.08%

2031 -0.09% -0.05% 0.02% 0.02% 0.01% -0.01% -0.16% -0.08%

2032 0.21% -0.08% -0.13% -0.04% -0.11% 0.10% -0.09% 0.19%

2033 0.03% 0.21% 0.24% 0.13% 0.18% 0.05% 0.34% 0.03%



Appendix B – Half-Hourly Charging Profiles

This document shows each of the charging profiles used in the LRE modelling carried out for the Black Cab Green project

in both graphical and table form. Please refer to Section 6 of the main report for further information about where the

information to create these profiles comes from and what the scenarios represent. There are 24 profiles in total, eight

use cases across three scenarios.

A spreadsheet containing all the data in this appendix can be found in the Black Cab Green Report Figures spreadsheet.

B.1 Unconstrained Scenario Profiles

Charging profiles as they would be without any interventions from technologies or tariffs.

Figure 24: Unconstrained scenario, black cab charging profiles

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Licence Private Hire Private Hire Private Hire Private Hire Private Hire Private Hire Black Cab Black Cab

Shift Description Day Evening

End of Shift 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 00:00 18:00


Vehicle Type Full Electric Full Electric Full Electric PHEV PHEV PHEVLEVC PHEV /

Dynamo BEV

LEVC PHEV /

Dynamo BEV

Battery Capacity Charged/Day (kWh) 23 23 23 8 8 8 23 23

Charging rate (kW) 7 7 7 3.5 3.5 3.5 7 7

Proportion of Licence Type (%) 6.4 6.6 7 25.6 26.4 28 49 51

Proportion of Licence Type 2025 (%) 25.6 26.4 28 6.4 6.6 7 49 51

Table 12: The eight different profile use cases encompassing all: vehicle types (battery sizes and charge rates), licence types (private hire or taxi) and work patterns.



Figure 25: Unconstrained scenario, private hire, battery electric vehicle charging profiles

Figure 26: Unconstrained charging, private hire, plug-in-hybrid electric vehicle charging profiles

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:00

13

:00

14

:00

15

:00

16

:00

17

:00

18

:00

19

:00

20

:00

21

:00

22

:00

23

:00

Ch

arge

Rat

e (k

W)


Late Evening Finishers

Early Morning Finishers



Table 13: Data used to construct each of the eight profiles for the unconstrained scenario.


Shift Description Evening Day

End of Shift 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 00:00 18:00



Dynamo BEV

LEVC PHEV /

Dynamo BEV


Charging rate (kW) 7 7 7 3.5 3.5 3.5 7 7



Time

00:00 0.00 7.00 0.00 0.00 3.50 0.00 0 0

00:30 0.00 5.50 0.40 0.00 3.50 0.10 3.5 0

01:00 0.00 4.60 1.00 0.00 0.80 0.20 7 0

01:30 0.00 4.20 1.50 0.00 0.20 0.50 7 0

02:00 0.00 3.00 2.20 0.00 0.00 1.40 7 0

02:30 0.00 1.20 3.50 0.00 0.00 3.50 7 0

03:00 0.00 0.40 5.90 0.00 0.00 2.00 7 0

03:30 0.00 0.00 7.00 0.00 0.00 1.40 4 0

04:00 0.00 0.00 6.30 0.00 0.00 1.30 2.5 0

04:30 0.00 0.00 5.15 0.00 0.00 2.80 1 0

05:00 0.00 0.00 3.60 0.00 0.00 1.70 0 0

05:30 0.00 0.00 2.80 0.00 0.00 0.90 0 0

06:00 0.00 0.00 2.30 0.00 0.00 0.40 0 0

06:30 0.00 0.00 1.60 0.00 0.00 0.20 0 0

07:00 0.00 0.00 1.20 0.00 0.00 0.10 0 0

07:30 0.00 0.00 0.80 0.00 0.00 0.00 0 0

08:00 0.00 0.00 0.50 0.00 0.00 0.00 0 0

08:30 0.00 0.00 0.20 0.00 0.00 0.00 0 0

09:00 0.00 0.00 0.10 0.00 0.00 0.00 0 0

09:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:30 0.00 0.00 0.00 0.00 0.00 0.00 0 3.5

17:00 0.00 0.00 0.00 0.00 0.00 0.00 0 7

17:30 0.00 0.00 0.00 0.00 0.00 0.00 0 7

18:00 0.81 0.00 0.00 0.35 0.00 0.00 0 7

18:30 1.62 0.00 0.00 0.75 0.00 0.00 0 7

19:00 3.37 0.00 0.00 1.50 0.00 0.00 0 7

19:30 5.12 0.00 0.00 2.50 0.00 0.00 0 4

20:00 6.06 0.00 0.00 3.30 0.00 0.00 0 2.5

20:30 7.00 0.50 0.00 3.50 0.20 0.00 0 1

21:00 7.00 1.20 0.00 2.40 0.40 0.00 0 0

21:30 5.96 1.80 0.00 1.20 0.65 0.00 0 0

22:00 4.69 2.50 0.00 0.50 0.90 0.00 0 0

22:30 2.90 3.70 0.00 0.00 1.35 0.00 0 0

23:00 1.08 4.70 0.00 0.00 2.00 0.00 0 0

23:30 0.40 5.70 0.00 0.00 2.70 0.00 0 0



B.2 Profile Aggregation In order to explain the shape of the ‘early morning finishers’ profile in Figure 26 it’s important to consider the starting

point. The twelve ‘finish shifts’ times from the industry survey. (Note shift finish times were used to calculate the start of

charge in each of the scenarios).

Three shift groups were created by ranking the proportion of drivers finishing within each timeslot and grouping the top

seven (shown in red in Figure 27). Adjacent shift groups were joined to create profile groups as shown by the labels in

Figure 27. Group 2 (‘early evening finishers’) and Group 3 (‘late evening finishers’) represent 26% of mini cab drivers

each and Group 1 (‘early morning finishers’) represents 28%. The remaining 20% of private hire drivers are split

proportionally across the aforementioned three groups. Making the new groups account for 32%, 33% and 35%

respectively for profile Group 1, 2 and 3.

Aggregation of groups of drivers that finish at different times is carried out in the following way:

1. A profile is written for each finishing time allowing for diversity in plug-in and hence start of charge times (Figure 28).

0%

2%

4%

6%

8%

10%

12%

14%

16%

00:00 -01:59

02:00 -03:59

04:00 -05:59

06:00 -07:59

08:00 -09:59

10:00 -11:59

12:00 -13:59

14:00 -15:59

16:00 -17:59

18:00 -19:59

20:00 -21:59

22:00 -23:59

Group 2

Group 3

Group 1

Figure 27: Industry survey results showing the distribution of private hire driver shifting finishing times (used to

deduce start of charge times)



2. The profiles are combined in proportion to the size of the group finishing at a particular time (Figure 28 and Figure 29).

3. The combined profile peak is adjusted to accommodate the maximum charge rate (Figure 29). 4. The profile from number 3 (above) is adjusted to ensure the area under the curve matches the battery

size/capacity (Figure 29). Table 14 and Table 15 show the above process which creates a more pronounced effect on the shape of the profile for the smaller battery size of PHEVs Figure 29as compared to the larger capacities of EVs (comparing ‘early morning finishers’ in Figure 26 with Figure 25Figure 30).

Figure 28: Three early morning finisher profiles and their proportionally combined profile



Figure 29: Combined profile for early morning finishers adjusted for peak charging rate and then normalised for battery size/capacity

Figure 30: Private Hire Electric Vehicle aggregated profile creation.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

00

:00

01

:00

02

:00

03

:00

04

:00

05

:00

06

:00

07

:00

08

:00

09

:00

10

:00

11

:00

12

:00

13

:00

14

:00

15

:00

16

:00

17

:00

18

:00

19

:00

20

:00

21

:00

22

:00

23

:00

Ch

arge

r R

ate

(kW

)

00:00 - 01:59

02:00 - 03:59

04:00-05:59

Aggregated

Adusted for ChargeRate & Battery Size



Table 14: Private Hire Plug-in Hybrid Electric Vehicle aggregated profile creation

Proportion of

profile group 25% 36% 39% 61.5% 38.5% 53.8% 46.2%

7% 10% 11% 28% 16% 10% 26% 14% 12% 26%

04:00 - 05:59 02:00 - 03:59 00:00 - 01:59 22:00 - 23:59 20:00 - 21:59 18:00 - 19:59 16:00 - 17:59

Time Combined Normalised

Adjusted

for Battery

Capacity Combined Normalised

Adjusted

for Battery

Capacity Combined Normalised

Adjusted

for Battery

Capacity

00:00 0 0 0.00 0.00 3.50 0.00 2.15 3.50 3.50 0 0 0.00 0.00 0.00

00:30 0 0 0.88 0.34 0.71 0.10 3.5 0.00 2.15 3.50 3.50 0 0 0.00 0.00 0.00

01:00 0 0 1.75 0.69 1.43 0.20 2.50 0.00 1.54 2.50 0.80 0 0 0.00 0.00 0.00

01:30 0 0 2.63 1.03 2.14 0.50 1.25 0.00 0.77 1.25 0.20 0 0 0.00 0.00 0.00

02:00 0 0 3.50 1.38 2.85 1.40 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

02:30 0 0.88 3.5 1.69 3.50 3.50 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

03:00 0 1.75 2.50 1.61 3.33 2.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

03:30 0 2.63 1.25 1.43 2.96 1.40 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

04:00 0 3.50 0 1.25 2.59 1.30 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

04:30 0.88 3.5 0 1.47 3.05 2.80 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

05:00 1.75 2.50 0 1.33 2.76 1.70 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

05:30 2.63 1.25 0 1.10 2.29 0.90 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

06:00 3.50 0 0 0.88 1.81 0.40 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

06:30 3.5 0 0 0.88 1.81 0.20 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

07:00 2.50 0 0 0.63 1.30 0.10 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

07:30 1.25 0 0 0.31 0.65 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

08:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

08:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

09:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

09:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

10:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

10:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

11:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

11:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

12:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

12:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

13:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

13:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

14:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

14:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

15:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

15:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

16:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

16:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

17:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

17:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0 0.00 0.00 0.00

18:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 0.88 0.40 0.47 0.35

18:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0 1.75 0.81 0.93 0.75

19:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.88 2.63 1.68 1.94 1.50

19:30 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 1.75 3.50 2.56 2.95 2.50

20:00 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 2.63 3.5 3.03 3.49 3.30

20:30 0 0 0 0.00 0.00 0.00 0 0.88 0.34 0.55 0.20 3.50 2.50 3.04 3.50 3.50

21:00 0 0 0 0.00 0.00 0.00 0 1.75 0.67 1.09 0.4 3.5 1.25 2.46 2.84 2.4

21:30 0 0 0 0.00 0.00 0.00 0 2.63 1.01 1.64 0.65 2.50 0.00 1.35 1.55 1.20

22:00 0 0 0 0.00 0.00 0.00 0 3.50 1.35 2.19 0.90 1.25 0.00 0.67 0.78 0.50

22:30 0 0 0 0.00 0.00 0.00 0.88 3.5 1.88 3.06 1.35 0.00 0.00 0.00 0.00 0

23:00 0 0 0 0.00 0.00 0.00 1.75 2.50 2.04 3.31 2.00 0.00 0.00 0.00 0.00 0.00

23:30 0 0 0 0.00 0.00 0.00 2.63 1.25 2.10 3.41 2.7 0.00 0.00 0.00 0.00 0

Proportion of

each licence



Table 15: Private Hire Electric Vehicle aggregated profile creation

Proportion of

profile group 25% 36% 39% 61.5% 38.5% 53.8% 46.2%

7% 10% 11% 28% 16% 10% 26% 14% 12% 26%

04:00-05:59 02:00 - 03:59 00:00 - 01:59 22:00 - 23:59 20:00 - 21:59 18:00 - 19:59 16:00 - 17:59 Aggregated

Time Aggregated

Adusted for

Charge Rate &

Battery Size Aggregated

Adusted for

Charge Rate &

Battery Size

Adusted for

Charge Rate &

Battery Size

00:00 0.00 0.00 0.00 0.00 0.00 7.00 4.75 6.13 7.00 0.00 0.00

00:30 0.00 0.00 1.75 0.69 0.40 7.00 2.00 5.08 5.50 0.00 0.00 0.00

01:00 0.00 0.00 3.50 1.38 1.00 7.00 0.75 4.60 4.60 0.00 0.00 0.00

01:30 0.00 0.00 5.25 2.06 1.50 7.00 4.31 4.20 0.00 0.00 0.00

02:00 0.00 0.00 7.00 2.75 2.20 4.75 2.92 3.00 0.00 0.00 0.00

02:30 0.00 1.75 7.00 3.38 3.50 2.00 1.23 1.20 0.00 0.00 0.00

03:00 0.00 3.50 7.00 4.00 5.90 0.75 0.46 0.40 0.00 0.00 0.00

03:30 0.00 5.25 7.00 4.63 7.00 0.00 0.00 0.00 0.00 0.00

04:00 0.00 7.00 4.75 4.37 6.30 0.00 0.00 0.00 0.00 0.00

04:30 1.75 7.00 2.00 3.72 5.15 0.00 0.00 0.00 0.00 0.00

05:00 3.50 7.00 0.75 3.67 3.60 0.00 0.00 0.00 0.00 0.00

05:30 5.25 7.00 0.00 3.81 2.80 0.00 0.00 0.00 0.00 0.00

06:00 7.00 4.75 0.00 3.45 2.30 0.00 0.00 0.00 0.00 0.00

06:30 7.00 2.00 0.00 2.46 1.60 0.00 0.00 0.00 0.00 0.00

07:00 7.00 0.75 0.00 2.02 1.20 0.00 0.00 0.00 0.00 0.00

07:30 7.00 0.00 0.00 1.75 0.80 0.00 0.00 0.00 0.00 0.00

08:00 4.75 0.00 0.00 1.19 0.50 0.00 0.00 0.00 0.00 0.00

08:30 2.00 0.00 0.00 0.50 0.20 0.00 0.00 0.00 0.00 0.00

09:00 0.75 0.00 0.00 0.19 0.10 0.00 0.00 0.00 0.00 0.00

09:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

11:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

11:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

15:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

15:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

16:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

16:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

17:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

17:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

18:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.75 0.81

18:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 1.62

19:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.75 5.25 3.37

19:30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.50 7.00 5.12

20:00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.25 7.00 6.06

20:30 0.00 0.00 0.00 0.00 0.00 1.75 0.67 0.50 7.00 7.00 7.00

21:00 0.00 0.00 0.00 0.00 0.00 3.50 1.35 1.20 7.00 7.00 7.00

21:30 0.00 0.00 0.00 0.00 0.00 5.25 2.02 1.80 7.00 4.75 5.96

22:00 0.00 0.00 0.00 0.00 0.00 7.00 2.69 2.50 7.00 2.00 4.69

22:30 0.00 0.00 0.00 0.00 0.00 1.75 7.00 3.77 3.70 4.75 0.75 2.90

23:00 0.00 0.00 0.00 0.00 0.00 3.50 7.00 4.85 4.70 2.00 0.00 1.08

23:30 0.00 0.00 0.00 0.00 0.00 5.25 7.00 5.92 5.70 0.75 0.00 0.40

Proportion of

each licence



B.3 Optimised Time-Shifting Scenario Profiles To reflect a delay between plug-in time and start of charge these profiles where changed to show a step change in the

charge demand i.e. no aggregation and no diversity.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

23

:30

01

:00

02

:30

04

:00

05

:30

07

:00

08

:30

10

:00

11

:30

13

:00

14

:30

16

:00

17

:30

19

:00

20

:30

22

:00

Ch

arge

Rat

e (k

W) Early Evening Finishers

Late Evening FinishersEarly Morning Finishers

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

23

:30

01

:00

02

:30

04

:00

05

:30

07

:00

08

:30

10

:00

11

:30

13

:00

14

:30

16

:00

17

:30

19

:00

20

:30

22

:00

Ch

arge

Rat

e (k

W)

Early EveningFinishersLate EveningFinishersEarly MorningFinishers

0

1

2

3

4

5

6

7

23

:30

01

:00

02

:30

04

:00

05

:30

07

:00

08

:30

10

:00

11

:30

13

:00

14

:30

16

:00

17

:30

19

:00

20

:30

22

:00

Ch

arge

Rat

e (k

W) Evening Shift

Day Shift

Figure 31: Optimised time-shifting scenario, private hire plug-in

electric vehicle drives

Figure 32: Optimised Time-Shifting Scenario Private Hire BEVs

Figure 33: Optimised time-shifting scenario, black cab charging profile





End of Shift 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 08:00-18:00 18:00-00:00



Dynamo BEV

LEVC PHEV /

Dynamo BEV


Charging rate (kW) 7 7 7 3.5 3.5 3.5 7 7

Proporition of Licence Type (%) 6.4 6.6 7 25.6 26.4 28 49 51


Time

00:00 7 7 0.00 3.50 3.50 0.00 0 7

00:30 7 7 0.40 3.50 3.50 0.10 3.5 7

01:00 7 7 1.00 3.50 3.50 0.20 7 7

01:30 7 7 1.50 3.50 3.50 0.50 7 7

02:00 7 7 2.20 2.00 2.00 1.40 7 7

02:30 7 7 3.50 0.00 0.00 3.50 7 7

03:00 3 3 5.90 0.00 0.00 2.00 7 4

03:30 1 1 7.00 0.00 0.00 1.40 4.2 0

04:00 0.00 0.00 6.30 0.00 0.00 1.30 2.4 0

04:30 0.00 0.00 5.15 0.00 0.00 2.80 0.9 0

05:00 0.00 0.00 3.60 0.00 0.00 1.70 0 0

05:30 0.00 0.00 2.80 0.00 0.00 0.90 0 0

06:00 0.00 0.00 2.30 0.00 0.00 0.40 0 0

06:30 0.00 0.00 1.60 0.00 0.00 0.20 0 0

07:00 0.00 0.00 1.20 0.00 0.00 0.10 0 0

07:30 0.00 0.00 0.80 0.00 0.00 0.00 0 0

08:00 0.00 0.00 0.50 0.00 0.00 0.00 0 0

08:30 0.00 0.00 0.20 0.00 0.00 0.00 0 0

09:00 0.00 0.00 0.10 0.00 0.00 0.00 0 0

09:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

17:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

17:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

18:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

18:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

19:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

19:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

20:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

20:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

21:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

21:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

22:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

22:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

23:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

23:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

Table 16: Data used for the eight profiles in the optimised time-shifting scenario.



B.4 Managed Charge Rate Scenario Profile Due to the limitation of the modelling technique used charge rate was halved to create the profiles for this scenario.

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Evening Shift withmanaged charging

Day Shift withmanaged charging

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Early Morning Finishers

Figure 34: Managed charge rate scenario, private hire battery electric vehicle charging profiles

Figure 35: Managed charge rate scenario, black cab charging profiles

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Figure 36: Managed charge rate scenario, private hire plug-in hybrid electric vehicle charging profiles





End of Shift 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 16:00 - 20:00 20:00 - 00:00 00:00 - 06:00 00:00 18:00



Dynamo BEV

LEVC PHEV /

Dynamo BEV


Charging rate (kW) 7 7 7 3.5 3.5 3.5 7 7



Time

00:00 3.50 3.50 0.00 0.00 1.75 0.00 0 0

00:30 2.50 3.50 0.40 0.00 1.75 0.20 3.5 0

01:00 1.60 3.50 1.00 0.00 1.75 0.30 3.5 0

01:30 0.80 3.50 1.50 0.00 1.75 0.60 3.5 0

02:00 0.30 3.50 2.20 0.00 1.00 1.60 3.5 0

02:30 0.00 3.50 3.50 0.00 0.80 1.75 3.5 0

03:00 0.00 3.50 3.50 0.00 0.20 1.75 3.5 0

03:30 0.00 2.20 3.50 0.00 0.00 1.70 3.5 0

04:00 0.00 1.50 3.50 0.00 0.00 1.60 3.5 0

04:30 0.00 0.70 3.50 0.00 0.00 1.75 3.5 0

05:00 0.00 0.00 3.50 0.00 0.00 1.75 3.5 0

05:30 0.00 0.00 3.50 0.00 0.00 1.50 3.5 0

06:00 0.00 0.00 3.50 0.00 0.00 0.80 3.5 0

06:30 0.00 0.00 3.50 0.00 0.00 0.50 2.5 0

07:00 0.00 0.00 3.50 0.00 0.00 0.20 1.5 0

07:30 0.00 0.00 2.40 0.00 0.00 0.00 0 0

08:00 0.00 0.00 1.80 0.00 0.00 0.00 0 0

08:30 0.00 0.00 1.20 0.00 0.00 0.00 0 0

09:00 0.00 0.00 0.50 0.00 0.00 0.00 0 0

09:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

10:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

11:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

12:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

13:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

14:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

15:30 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:00 0.00 0.00 0.00 0.00 0.00 0.00 0 0

16:30 0.00 0.00 0.00 0.00 0.00 0.00 0 3.5

17:00 0.00 0.00 0.00 0.00 0.00 0.00 0 3.5

17:30 0.00 0.00 0.00 0.00 0.00 0.00 0 3.5

18:00 0.81 0.00 0.00 0.35 0.00 0.00 0 3.5

18:30 1.62 0.00 0.00 0.75 0.00 0.00 0 3.5

19:00 3.37 0.00 0.00 1.50 0.00 0.00 0 3.5

19:30 3.50 0.00 0.00 1.75 0.00 0.00 0 3.5

20:00 3.50 0.00 0.00 1.75 0.00 0.00 0 3.5

20:30 3.50 0.67 0.00 1.75 0.20 0.00 0 3.5

21:00 3.50 1.35 0.00 1.75 0.40 0.00 0 3.5

21:30 3.50 2.02 0.00 1.75 0.65 0.00 0 3.5

22:00 3.50 2.69 0.00 1.75 0.90 0.00 0 3.5

22:30 3.50 3.50 0.00 1.50 1.35 0.00 0 2.5

23:00 3.50 3.50 0.00 1.00 1.75 0.00 0 1.5

23:30 3.50 3.50 0.00 0.40 1.75 0.00 0 0

Table 17: Data used in the eight profiles for the managed charge rate scenario.



Appendix C – Online Black Cab and Private Hire Driver Survey Results

Please see the separate report for this Appendix.

Appendix D – Detailed LRE Modelling Results

Please see the separate report for this Appendix.10

Appendix E – Dissemination Event Outcomes

Please see the separate report for this Appendix which was populated based on the dissemination event on 7th March

2018.

10 This document is most relevant to UKPN for internal planning purposes but may be made available on request

Documents

Black Cab Green - innovation.ukpowernetworks.co.uk · The Black Cab Green project makes the first step of modelling the impact on power networks of black cab and private hire vehicle