Value Quantification of Electric Vehicle Response on Network Investment in the UK

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Value Quantification of Electric Vehicle Response on Network Investment in the UK

Xiaohe Yan, ChenghongGu, Furong Li

Abstract— Electric vehicles (EVs) are a significant element in the electricity system to meet the environmental target and can help the network operator to shift the peak demand in the future. Therefore, it is important to evaluate the future economic impacts resulting from the increasing EV penetration to the national network. However, the contribution of EVs to network investment is difficult to quantify. Although we can measure the reduced capacity during peak demand for a specified low voltagenetwork, it is difficult to quantify the impact on a national level. This paper uses a typical LV network to analyze the investment savings at this voltage level and then extrapolates the results to the entire UK network by considering the coincidence factor, peak shaving percentage etc. From the demonstration, it can be concluded that semi-urban areasand low voltage networks enjoy the mostly investment savingscompared with other areas and voltage levels.

Keywords— Electric vehicle, Investment savings, Present value, Savings allocation.

I.INTRODUCTION

A

ccording to the government report in 2014, the CO2 emissions from transportation areincreasing significantly higher than previous years, especially fromcars and vehicles on the road [1]. In order to reduceCO2 emissionsto meet their environmental targets, the UK government has implemented some policies to incentivize the use of electric vehicles (EVs). For example, £400 million will be used to advance EV technologies and £500 million will be used for citizens to shift from traditional cars to EVs [2]. In National Grid’s Gone Green scenario, the amount spends on EVs in 2030 will be £3.19 million [3]. Due to the increase in the penetration of EVs’, it is important to know their impact on the electricity systems.

EVs, typically a transportation tool, can be treated as a storage strategy to shift the peak demand and fill the valley, which can lead to investment deferral and saving. Several studies focus on this impact. Paper [4] discusses the impact of EVs on energy usage and electricity demand profiles by considering their using time period and location. An algorithm has been developed to determine the impact of charging and discharging behavior of EVs based on the statistics of driving habits on daily trips and distance. But this paper has not qualified the economic impact of EV penetration, no matter from customer side or customer side. Paper [5] provides a Vehicle to Home concept which treats EVs as an energy provider for households. The cost reduction in local electricity bills is evaluated due to EVs’ supplement during the peak demand period. However, these results cannot contribute to the network investment guidance. Paper [6] evaluates the impact on electricity demand resulting from unscheduled charging of EVs’. Electricity demand is simulated by Monte Carlo methods to determine the relationship between potential demographic characteristics and charging habits of EV drivers. Although less than 1% of the peak load will be increased by uncontrolled charging, this method focuses more on a specific region rather than on national level. A spatiotemporal model is used in thepaper[7] to assess the potential of peak-shaving from EV. It found that EVs can reduce peak loads 2~5% of the day in specific geographical regions.

In the typical electricity load profile, the peak demand periodoccurs from 6pm to 8pm which is 4~6 times higher than the valley demand. The peak demand period exists only for a short time, but this total required capacity determines the investment needed. However, due to the network losses and the complex of electricity distribution system, it is difficult to know the demand capacity reduction results from EV penetration. In other words, 1 MW EV penetration in the low voltage (LV) system will lead to different peak demand reduction in the high voltage (HV) and extra high voltage (EHV) levels. Therefore, it is much more difficult to quantify the peak demand capacity reduction which can be achieved at a national level.

This paper focuses on quantifying the investment reduction from flexible EV charging/dischargingat different voltage levels and in different areas of the distribution network. The performance assessment is conducted by: 1) quantifying the impact of different EV penetrations on network investment deferral ona real UK LV network; 2) using present value to measure the economic savings from EV penetrations; 3) evaluating the investment savings at thenational level by considering different voltage levels and areas. Sensitivity analysis is also conducted to mitigate the uncertainty of EVs’ unit contribution to network peak demand shift.

This paper is organized as follows: Section II explains the methodology to extrapolate results from the LV network to the national level and Section III gives an introduction of key components. In Section IV, a typical LV network is analyzed to demonstrate how EV penetration affects the investment savings in LV level. The investment savings in 2020 and 2030 in the UKwill be demonstrated in Section V and conclusions are in Section VI.

II.Methodology

This section introduces the methodology to determine the investment saving in LV system from a typical LV network and then extrapolate this to the whole UK networks.

Firstly, a typical LV network is analyzed to demonstrate how EVs reducing the peak demands in local load profiles and also to find out EV’s unit contribution to the peak reduction. By calculating the change of present value, investment savings can be determined for the LV system. The reduced capacity caused by EV penetration can be calculated on anational level by multiplying the unit contribution and the predicted number. Then, this reduced capacity can be allocated to EHV, HV and LV levels by considering peak shaving percentage.Due to the time difference of peak demand in different voltage levels, coincidence factor is involved. Because the capacity of this peak demand determines the network investment, the investment savings on three voltage levels can be obtained by multiplying the unit cost withthe capacity reduction. Capacity share can allocate these savings to three different areas which are urban, semi-urban and rural areas. These elements will be described in Section III.The flowchart in Fig. 1 shows the proposed approach and the main steps in national level:

Fig. 1 Flow chart of proposed approach

1)Convert the predicted number and unit contribution of EV’s to the peak demand reduction;

2)Allocatethis demand reductionat different voltage levels by using the peak shaving percentage andthe coincidence factor;

3)Determine the investment savings by multiplying the unit cost;

4)Allocateinvestment savings in different areas at different voltage levels by involving the capacity share.

III.Key Component

A.Peak Shaving Percentage

Peak shaving percentage is considered to show the allocation of the EV penetration to different voltage level based on distribution characteristics such as network loss. This factor is listed in TABLE I[8].For example, the peak shaving percentage for EHV, 55%, means that 1 MW capacity change in lower voltage level (HV) leads extra 55% change in EHV networks.

TABLE I

Peak Shaving Percentage

B.Coincidence Factor

Since the peak demand time in different voltage levels occursdifferently, coincidence factor is considered to integrate the differences. Thecoincidences factors networksin different voltage levelsare listed in TABLE II[8]. This factor can gather the peak period together in different years. The coincidence factor for HV networks is the largest one.

TABLE II

Coincidence Factor in Different Voltage Levels

C.Unit Cost

Unit cost is the investment for 1 MW reinforcement at different voltage levels. The unit cost for three voltage levels is listed in TABLE III[8]. For LV and EHV networks, the unit cost in 2030 is higher than that in 2020 but theunit cost for HV network is the highest in 2020.

TABLE III

Unit Cost for Network Investment in Different Voltage Levels

D.Capacity Share

The capacity share among urban/semi/rural areas shows the network capacity allocation in different areas in Fig. 2[8]. Urban areas have the largest capacity share which is more than half of the capacity of the whole distribution network and the capacity is doubled in rural areas from 2020 to 2030.

Fig. 2Capacity share among urban/semi/rural areas

E.Pay Back Period and Present Value

The network cost from Long-Run Incremental Cost Pricing (LRIC) can be determined from[9]. Equation (1) shows the relationship betweenrated network capacity (C) and maximum power flows (D). The number of years (n) it takes to grow from D to C for a given load growth rate (r) can be presented in (2).

(1)

(2)

The present value (PV) is used to quantify the current worth with a specific discount rate (d) in the future investment in n years.

(3)

Where: Asset is the modern equivalent asset cost.

IV.Case Study

A.Information for Selected LV Network

The proposed approach is demonstrated on a typical LV system network in Fig. 3which has one substation and two feeders. Feeder 1 contains nodes from 1 to 28, which has 136 customers. Feeder 2 contains nodes from 29 to 48, which has 121 customers. The total customers who have EVs are 11 (3 in Feeder 1 and 8 in Feeder 2). The locations of these EV customers are listed in TABLE IV[10] and each node has one EV unit.

Fig. 3 The topology structure of the case system

TABLE IV

Customer Connection Information

Acceding to the historical data during 2014~2015, the profile of the peak demand day can be drawn inFig. 4. The peak demand in Feeder 1, 2 and substation is 141.7 kW (at 19:15), 171.5 kW (at 17:30) and 303.8 kW (at 17:30) respectively.

Fig. 4 The peak demand profile of the year

B.Investment savings for LV Networks

This part shows how the demand profile be influenced by EV penetration. Fig. 5~7depict that the peak demand is shaved and the valley demand is filled resulting from EV penetration in Feeder 1, Feeder 2 and substation. Normally, the EVs are charging during the evening which is the valley of demand profile and discharging during the peak period which is the time that people back to their home after 5pm. Two different EV penetration levels are considered which are 30% and 50% of the customers have EVs and each EV unit contributes 0.3kW demand reduction during the peak demand period. With 30% EV penetration, the peak demand reduces 11.4 kW, 8.4 kW and 19.8 kW in Feeder 1, Feeder2 and substation respectively. With 50% EV penetration, the peak demand reduces 19.5kW, 15.9kW and 35.4kWin Feeder 1, Feeder2 and substation respectively.EVs are discharging over the peak demand period from 16:00 to 20:00 and charging over the valley period from 2:00 to 6:00.From the results figures below, higher EV penetration levelleads to a more flat profile curves.

Fig. 5 The results for Feeder 1

Fig. 6 The results for Feeder 2

Fig. 7 The results for substation

Typically, the load growth is 2% and the discount rate is 5.6%.The typical unit cost of thefeeder is £67200/km and the unit cost of transformers is £26400 [11, 12]. Based on (3), the investment savingsare listed in TABLE V:

TABLE V

Network Investment savings

From the table above, the savings of thetransformer is just £75.9 because the current utility of transformer is only 42%. The investment for Feeder 2 saves the most and Feeder 1is the least which means Feeder 1 has lower capacity utilization.

C.Sensitivity Analysis on Investment savings

Since the unit contribution of EV is different, it is significant to do the sensitivity analysis to mitigate this uncertainty. Fig. 8~10 show the variation of the investment deferral when the EV unit contribution changes from 0.2 kW to 1.7 kW.

Fig. 8Sensitivity analysis on Feeder 1

Fig. 9 Sensitivity analysis on Feeder 2

Fig. 10 Sensitivity analysis on substation

From these three figures, the trend of each feeder is similar to thetrend of thesubstation. At the beginning of EV contribution, investment deferral increases dramatically. But when the EVs’ contribution exceeds a certain level, the investment savings are saturated because the peak demand is shifted to another time period.

V.Investment savings for the UK Distribution Networks

The demand capacity is 60.2 GW in 2020 and increases to 62.2 GW in 2030[3] and the number of EVs will increase from 0.57 million to 3.19 million overthis 10 year period[13].Assuming 1) the load growth is 2% and the discount rate is 5.6%; 2) The typical unit cost of feeder is £67200/km and the unit cost of transformers is £26400; 3) the 30% of the customer have EVs and the unit contribution of EV is 0.3 kW to the reduction of the peak demand.

A.Investment savings across Voltage Levels and Areas

By considering the factors in section II and following the steps in the flowchart (Fig. 1), the investment savingsat different voltage levels and different areas are drawn below:

TABLE VI

Investment savings in 2020

TABLE VII

Investment savings in 2030

From TABLE VI~VII, it can be found that: first, LV network enjoys the largest investment savings which are £27.3m in 2020 and £138.3m in 2030. EHV networkenjoys the least investment savings which are £21.8m in 2020 and £118.9m in 2030; second, the investment savings in the semi-urban areasaremuch higher than other areas, especially the rural areas; third, the total savings are increasing dramatically. It climbing from £72.3m in 2020 to £383.5m in 2030 which climbs more than five times.

B.Sensitivity Analysis of Investment savings for 2020 and 2030 Scenarios

Considering the uncertainty of the contribution of EV unit, sensitivity analysis need to be involved to show how the investment savings vary with adifferent unit contribution.

Fig. 11 Sensitivity analysis on investment savings for 2020

Fig. 12 Sensitivity analysis on investment savings for 2030

From Fig. 11~12above, it is seen that with the increasing of EV unit contribution, the investment savingsare climbing in different voltage levels. With the unit contribution increasing from 0.3 kW to 0.9 kW, the investment savings for LV networks climbs from £27.3m to £81.9m in 2020 and from £138.3m to £415.0m in 2030.

VI.Conclusion

This paper quantifies the investment savings in EHV, HV and LV networks in the 2020 and 2030. There are some key findings from the qualification analysis:

•EVs in semi-urban areas contribute more to distribution network investment savings;

•The EV penetration effectsare different at different voltage levels. LV network enjoys the largest savings and EHV has the least savings;

•With the EV penetration increasing in national level, investment savings are climbing notably;

•In local distribution networks, the peak of the networks will shift after a certain level of EV penetration, and thus, new EV response scheme is needed;

•In order to encourage EV response, new business models/tariffs are essential.

EV penetration is a significant factor in the whole electricity system. If EVs can response appropriately, the power companies and consumers can gain the more benefits.

References

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Xiaohe Yan was born in Shaanxi, China in Aug/1991. He received Master’s degree in theelectrical power system from theUniversity of Bath in 2015. He is currently pursuing the Ph.D. degree in the Department of Electronic & Electrical Engineering at the University of Bath. His current research activities are in the area of low voltage network pricing.

Chenghong Gu was born in Anhui province, China. He received his, Bachelor degree and Master degree in electrical engineering from Shanghai University of Electric Power and Shanghai Jiao Tong University, Shanghai, China, in 2003 and 2007 respectively. In 2010, he obtained his PhD from University of Bath, U.K. Now, he is a Lecturer and EPSRC Research Fellow at the University of Bath. His major research is in the area of power system planning, economics and multi-vector energy systems.

Furong Li was born in Shanxi, China. She received her B.Eng. degree in electrical engineering from Hohai University, China, in 1990, and her Ph.D. degree from Liverpool John Moores University, U.K., in 1997. She took up a lectureship in 1997 in the Power and Energy Systems Group at the University of Bath, U.K, where she is now Professor. Her major research interests are in the areas of power system planning, operation, automation and power system economics.

All authors are with the Department of Electronic & Electrical Engineering, University of Bath, Bath, BA2 7AY, UK. Tel: (+44) 01225 383040; E-mail: xy442@ bath.ac.uk; C.Gu@ bath.ac.uk;F.Li@ bath.ac.uk.