Design of Optimization Strategy and Response Incentive Mechanisms of Energy and Frequency Ancillary Services Market With Participation of Electric Vehicle Aggregators

ZHU Lan; YU Jiale; DONG Kaixuan

doi:10.19725/j.cnki.1007-2322.2023.0259

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Design of Optimization Strategy and Response Incentive Mechanisms of Energy and Frequency Ancillary Services Market With Participation of Electric Vehicle Aggregators

更新时间：2025-10-22

- Design of Optimization Strategy and Response Incentive Mechanisms of Energy and Frequency Ancillary Services Market With Participation of Electric Vehicle Aggregators
- Vol. 42, Issue 5, Pages: 1037-1051(2025)
- 作者机构：
  
  上海电力大学电气工程学院, 上海市杨浦区,200090
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0259
  CLC： 5
- Published：2025
- 稿件说明：
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ZHU Lan, YU Jiale, DONG Kaixuan. Design of Optimization Strategy and Response Incentive Mechanisms of Energy and Frequency Ancillary Services Market With Participation of Electric Vehicle Aggregators[J]. 2025, 42(5): 1037-1051.
DOI：

ZHU Lan, YU Jiale, DONG Kaixuan. Design of Optimization Strategy and Response Incentive Mechanisms of Energy and Frequency Ancillary Services Market With Participation of Electric Vehicle Aggregators[J]. 2025, 42(5): 1037-1051. DOI： 10.19725/j.cnki.1007-2322.2023.0259.

摘要

为充分挖掘电动汽车的响应潜力以及提高聚合商的盈利能力，以电动汽车聚合商为主体，构建聚合商协调电动汽车用户同时参与调频调峰辅助服务的双层优化调度模型。上层模型中，电动汽车聚合商根据用户最大可调度潜力，结合电力公司发布的需求响应信息，以电动汽车聚合商收益最大化为目标，分配调频、调峰不同辅助服务投标容量。下层模型中，电动汽车聚合商考虑用户参与意愿以及参与的辅助服务类型，设计用户基本响应激励和响应补偿方法，模拟调度电动汽车用户参与调峰、调频辅助服务，设置聚合商调度策略。算例分析表明，电动汽车聚合商参与多种辅助服务可以增加整体效益，合理的激励价格可以使聚合商收益最大化，同时用户通过参与不同类别的辅助服务也能获取一定的收益。

Abstract

To fully exploit the response potential of electric vehicles and improve the profitability of aggregators

a two-layer optimal scheduling model is developed with electric vehicle aggregators as the main entity. In this model

aggregators coordinate electric vehicle users to participate in the auxiliary service of frequency modulation and peak regulation. The electric vehicle aggregator in the upper level model aims to maximize their revenue and allocates bidding capacity for different auxiliary services by considering the maximum schedulable potential of users and incorporating demand response information provided by electric power companies. While in the lower model

the electric vehicle aggregator takes into account the user’s willingness to participate and the type of auxiliary service

designs the basic response incentive and response compensation methods for users

simulates the scheduling of electric vehicle users participating in the auxiliary service of peak regulation and frequency modulation

and establishes the aggregator scheduling strategy. The example analysis demonstrates that the participation of electric vehicle (EV) aggregators in a variety of auxiliary services can enhance the overall benefits

while reasonable incentive prices can maximize the benefits of aggregators

and users can also gain certain benefits by participating in various types of auxiliary services.

关键词

Keywords

references

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