Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities

doi:10.11930/j.issn.1004-9649.202503024

Abstract

Abstract:

Smart communities utilize demand response to achieve integrated management of electric vehicles (EVs) and photovoltaic-storage systems. However, traditional static pricing, which disregards actual load responses, tends to induce new load peaks and increases the volatility of the equivalent load. To address these challenges, this paper proposes a dynamic pricing strategy, wherein the price not only varies over time but is also correlated with the internal net load of the community. Firstly, the operator forecasts the photovoltaic generation and the baseline load within the scheduling horizon, and establishes the initial model of dynamic pricing. Secondly, a stackelberg game framework is constructed: the operator, as the leader in the upper-level model, aims to minimize the volatility of the equivalent load by formulating and publishing the dynamic prices as well as scheduling the energy storage output; EVs, as followers in the lower-level model, respond to the dynamic prices and optimize their charging strategies with the objective of minimizing charging costs. Furthermore, in the lower-level model, the introduction of dynamic pricing makes the charging decision of EVs mutually dependent, thereby forming an aggregative game structure, in which the optimal charging load of each EV is determined through the Nash equilibrium. Finally, the optimal dynamic pricing is determined through a genetic algorithm. Simulation results demonstrate that the proposed model effectively reduces load volatility, avoids new load peaks, and achieves the management objectives of the community operator while safeguarding the economic interests of EV users.

Key words: demand response, electric vehicles charging, dynamic prices, stackelberg game, Nash equilibrium

PAN Tingzhe, JIN Fengyuan, LU Yonghao, CAO Wangzhang, YANG Hao, YU Heyang, ZHAO Boyang. Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities[J]. Electric Power, 2025, 58(11): 14-24, 37.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202503024

https://www.electricpower.com.cn/EN/Y2025/V58/I11/14

Figures/Tables 14

Fig.1 Operational framework of static pricing

Fig.2 Operational framework of dynamic pricing

Fig.3 Architecture for a two-level nested game structure

Fig.4 Solution process for dynamic pricing based on genetic algorithm

Fig.5 Profiles of basic residential power load and the PV power output

Fig.6 Uncoordinated load profiles

Fig.7 Equivalent load profiles under different scenarios

Table 1 Comparison of equivalent load performances under different scenarios 单位：kW

项目	无序充电	静态电价	动态电价
晚间峰值（18:00—22:00）	1592.0	944.2	939.5
凌晨峰值（01:00—06:00）	909.5	1249.0	898.4
谷值	381.2	653.7	675.2
峰谷差	1210.7	595.4	264.4
标准差	305.3	144.8	98.5

Fig.8 Comparison of charging costs under different scenarios

Table 2 Comparison of total electricity costs under different scenarios 单位：元

场景		无序充电		静态电价		动态电价
总用电成本		11592.8		11525.3		11470.6

Fig.9 Impact of schedule duration on charging cost savings under dynamic prices

Fig.10 Impact of charging energy on charging cost savings under dynamic prices

Table 3 Comparison of load profiles across different scenarios 单位：kW

场景	峰值	谷值	峰谷差	标准差
1	1649.0	381.2	1267.8	274.8
2	1249.0	653.7	595.4	144.8
3	1298.5	381.2	917.2	243.0
4	939.5	675.2	264.4	98.5

Fig.11 The charging and discharging power of energy storage and equivalent load profiles under dynamic pricing mechanism

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