Regulation Ability Estimation of Private EVs at City Level Considering Users’ Trip Chain and Regulation Willingness

doi:10.12204/j.issn.1000-7229.2021.05.011

Abstract

Abstract:

This paper presents a method to evaluate the ability of private electric vehicle (EV) to participate in power grid regulation based on EV trip chain. According to the characteristics of users’ trip chain, the trip chain of private EVs is simulated during working days and non-working days, and the spatial distribution variation characteristics of EVs in various functional areas of the city are obtained. Considering the key factors affecting users’ participation in power grid regulation, the capacity model of single private EV is established according to the SOC constraint and power constraint of EV, and then the regulation capacity model of cluster EVs in different urban functional areas is established. Finally through the simulation analysis, the regulation ability of private EVs during working days and non-working days is evaluated and compared. Furthermore, the regulatory capacity changes of private EVs under the willingness to participate in regulation are considered. The results show that the change of the regulatory capability of private EVs is related to the change of EV number in a functional area of a city, and the decrease of the proportion of users’ willingness to participate makes the overall regulatory capability decrease, but the change feature is still dominated by the change feature of EV spatial distribution.

Key words: electrical vehicle, trip chain, urban functional area, regulation ability, participation willingness

CLC Number:

TM73

LI Yaohong, CHEN Liangliang, LIU Weidong, FU Zhixin. Regulation Ability Estimation of Private EVs at City Level Considering Users’ Trip Chain and Regulation Willingness[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 100-112.

Figures/Tables 40

Fig.1 Diagram of trip chain

Fig.2 Change in the number of private EVs during the working day

Fig.3 Change in the number of private EVs during the non-working day

Fig.4 Schematic diagram of regulatory capacity in scenario 1

Fig.5 Schematic diagram of regulatory capacity in scenario 2

Fig.6 A schematic of fuzzy reasoning

Fig.7 Membership function of SOH

Fig.8 Membership function of charging willingness

Table 1 Fuzzy rules for charging willingness (part)

Fig.9 Charging capacity of each region during the working day

Fig.10 Charging power of each region during the working day

Fig.11 Charging capacity of each region during the non-working day

Fig.12 Charging power of each region during the non-working day

Fig.13 EV cluster charging capacity of each region during the working day

Fig.14 EV cluster charging power of each region during the working day

Fig.15 EV cluster charging capacity of each region during the non-working day

Fig.16 EV cluster charging power of each region during the non-working day

Fig.17 Comparison diagram of charging capacity of various regions during the working day

Fig.18 Comparison diagram of charging power of various regions during the working day

Fig.19 Comparison diagram of charging capacity of various regions during the non-working day

Fig.20 Comparison diagram of charging power of various regions during the non-working day

Table A1 Results of fitting parameters at the time of the first trip during the working day

Table A2 Results of fitting parameters at the time of the first trip during the non-working day

Table A3 The fitting parameters of driving time during the working day

Table A4 The fitting parameters of driving time during the non-working day

Table A5 The logarithmic normal distribution fitting parameters of the parking time during the working day

Table A6 The GMM fitting parameters of stopping time during the working day

Table A7 The GMM fitting parameters of stopping time during the non-working day

Table A8 The logarithmic normal distribution fitting parameters of the parking time during the non-working day

Fig.B1 The stroke transfer probability of different time periods during the working day

Fig.B2 The stroke transfer probability of different time periods during the non-working day

Fig.B3 Trip destination transfer probability at 08:00-09:00 during the working day

Fig.B4 Trip destination transfer probability at 08:00-09:00 during the non-working day

Fig.C1 Membership function of SOC

Fig.C2 Membership function of charging price

Fig.C3 Membership function of discharging price

Fig.C4 Membership function of parking time

Fig.C5 Membership function of discharging willingness

Table C1 Fuzzy rules for charging willingness

Table C2 Fuzzy rules for discharging willingness

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