Bidding Strategy of Charging Operator Participating in Peak Regulation Market Based on Distributionally Robust Chance Constraint

Yilin WEN; Zechun HU; Jian NING; Likai LIU

doi:10.7500/AEPS20210716004

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Bidding Strategy of Charging Operator Participating in Peak Regulation Market Based on Distributionally Robust Chance Constraint

Basic Research | 更新时间：2025-06-16

- Bidding Strategy of Charging Operator Participating in Peak Regulation Market Based on Distributionally Robust Chance Constraint
- Automation of Electric Power Systems Vol. 46, Issue 7, Pages: 23-32(2022)
- 作者机构：
  
  1.清华大学电机工程与应用电子技术系，北京市 100084
  2.国家电网有限公司华北分部，北京市 100053
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U1766205);State Grid Corporation of China(2600/2020-02001B)
- DOI：10.7500/AEPS20210716004
  CLC：
- Received：16 July 2021，
  
  Revised：2021-11-27，
  
  Published Online：10 January 2022，
  
  Published：10 April 2022
- 稿件说明：
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文艺林,胡泽春,宁剑等.基于分布鲁棒机会约束的充电运营商参与调峰市场投标策略[J].电力系统自动化,2022,46(7):23-32.

WEN Yilin,HU Zechun,NING Jian,et al.Bidding Strategy of Charging Operator Participating in Peak Regulation Market Based on Distributionally Robust Chance Constraint[J].Automation of Electric Power Systems,2022,46(7):23-32.
文艺林,胡泽春,宁剑等.基于分布鲁棒机会约束的充电运营商参与调峰市场投标策略[J].电力系统自动化,2022,46(7):23-32. DOI： 10.7500/AEPS20210716004.

WEN Yilin,HU Zechun,NING Jian,et al.Bidding Strategy of Charging Operator Participating in Peak Regulation Market Based on Distributionally Robust Chance Constraint[J].Automation of Electric Power Systems,2022,46(7):23-32. DOI： 10.7500/AEPS20210716004.

摘要

电动汽车作为一类重要的新型可控负荷资源，具有为电网提供调控服务的潜力。然而电动汽车的调节能力受其出行安排的影响，既呈现出一定的规律性，也具有不确定性。首先

提出一种电动汽车调节能力模型，该模型可以统一考虑数据结构不同的公共充电站与专用充电站，进而采用数据驱动的分布鲁棒机会约束来描述调节能力的不确定性。考虑聚合了多个不同类型充电站的充电运营商参与调峰辅助服务市场的日前投标阶段，提出一种双层投标-分配模型，使得运营商对外可以控制风险，对内可以灵活调度资源。基于实际数据的仿真结果表明，分布鲁棒机会约束描述的调节能力可以适应电动汽车出行规律的随机变化，所提出的投标-分配策略能够灵活地权衡运营商的经济效益与违约风险。

Abstract

As important new flexible load resources

electric vehicles (EVs) have the potential to provide regulatory services for the power grid. However

the controllability of EVs is affected by their traveling arrangements

which shows the regularity and uncertainty. Firstly

this paper proposes a flexibility model of EVs

which can consider the difference in the data structure of public charging piles and specific charging piles. Besides

the uncertainty of the flexibility is described using the data-driven distributionally robust chance constraint (DRCC). Furthermore

considering a charging operator aggregating different types of charging stations participating in the peak regulation ancillary service market

the paper proposes a bi-layer bidding and allocating model

which enables the charging operator to manage the risk outside and to flexibly dispatch resources inside. Simulation results based on the actual data indicate that the flexibility described by the DRCC can adapt to the random changes of traveling law of EVs

and the proposed bidding and allocating strategy can effectively manage the revenue of the charging operator and the default risk.

关键词

Keywords

references

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