Service Strategy of EV Aggregator under the “Reservation/On-demand Charging” and Discharging Service Mode

doi:10.12204/j.issn.1000-7229.2021.12.012

Abstract

Abstract:

Since the current research on charging and discharging service mode only considers EV load guidance from the perspective of time-shifting, a “reservation/on-demand” mode is established incorporating the consideration of the regularity of users’ grid-access behaviors. In this mode, electric vehicle aggregator (EVA) can predict the charging needs of users with regular grid-access behaviors through service reservations, thereby improving the guidance effect of EVAs. Firstly, the business process of “reservation/on-demand” service is elaborated. Secondly, considering the comprehensive economic effect and convenience effect, with the goal of maximizing user’s utility, the EVAs can construct optimal service purchase strategies for various users and analyze purchase intentions. Finally, from the perspective of EVA, this paper constructs a utility-based user service selection behavior analysis model to help EVA grasp the influence of different user choices, and uses the Stackelberg game method to process EVA price guidance strategy. Analysis shows that, compared with the existing simplistic service model, the “reservation/on-demand” service mode can not only improve service quality, but also further reduce the power purchase cost of EVA.

Key words: electric vehicle aggregator (EVA), charging and discharging service mode, “reservation/on-demand” service mode, service selection behavior analysis

CLC Number:

TM73

CHEN Yingqi, LI Huaqiang, CHEN Ying, LIN Zhaohang. Service Strategy of EV Aggregator under the “Reservation/On-demand Charging” and Discharging Service Mode[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(12): 104-115.

Figures/Tables 15

Table 1 Comparison of on-demand service and reservation service

Fig.1 Schematic diagram of “reservation/on-demand” service mode

Fig.2 The relationship between EVA and users

Fig.3 The cost of users and the benefit of EVA in different scenarios

Fig.4 Pricing results in various scenarios

Fig.5 Quantitative results of the utility of the users of the reservation service

Fig.6 Charging and discharging of users’ EVs in different scenarios

Fig.7 Load guidance result of reservation service

Table 2

Table 3

Table C1 Parameter setting of electric vehicles

Fig.Fig.D1 Scenario simulation results of users’ random access behavior

Fig.D2 EVA purchase cost in day-ahead market in Scenario 1

Fig.D3 EVA purchase cost in day-ahead market in Scenario 2

Fig.D4 EVA purchase cost in day-ahead market in Scenario 3

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