Time of Use Pricing Strategy of Charging Aggregator considering Peak Load and Frequency Regulation

2021 IEEE Sustainable Power and Energy Conference (iSPEC) Pub Date : 2021-12-23 DOI:10.1109/iSPEC53008.2021.9735678

Yang Zhenyu, Yang Shaohong, Huang Xiaoqing

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Abstract

Orderly charging of electric vehicles (EVs) can alleviate the impact of large-scale EVs on distribution network. Time of use (TOU) sharing pricing is an orderly scheduling strategy. However, the previous TOU pricing strategies overlook EVs participating in peak shaving and frequency regulation and the comprehensive profits of power grid, charging aggregator (CAs) and EVs. Thus, this paper studies the TOU pricing strategy. Firstly, the driving characteristics and load change characteristics of EVs are analyzed, and the cost-benefit model of EVs participating in peak load regulation and frequency regulation is constructed. Then, a TOU pricing model taking the minimum variance of EV load and the maximum cost-benefit of EV users and CAs as the objective function is modeling. The non-dominated sorting genetic algorithm (NSGA-II) algorithm is used to solve the peak-normal-valley period electricity price. The result shows the effectiveness of the TOU pricing strategy, the peak valley difference and volatility of load can be reduced, the expenditure of EV users will be reduced, and the income of CAs will be increased.

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考虑峰值负荷和频率调节的充电聚合器使用时间定价策略

电动汽车有序充电可以缓解大规模电动汽车对配电网的影响。分时电价共享定价是一种有序调度策略。然而，以往的分时电价定价策略忽视了电动汽车参与调峰调频以及电网、充电集热器和电动汽车的综合收益。因此，本文对分时电价定价策略进行了研究。首先，分析电动汽车的行驶特性和负荷变化特性，构建电动汽车参与调峰和调频的成本效益模型;然后，以电动汽车负荷方差最小、电动汽车用户和ca的成本效益最大为目标函数，建立了分时电价定价模型。采用非支配排序遗传算法(NSGA-II)求解峰谷电价。结果表明，分时电价策略的有效性，可以减小峰谷差和负荷波动，降低电动汽车用户的支出，增加电网运营商的收入。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 IEEE Sustainable Power and Energy Conference (iSPEC)

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