Bi-level Stochastic Optimization for A Virtual Power Plant Participating in Energy and Reserve Market Based on Conditional Value at Risk

被引：0

作者：

Wang J. ^{[1
]}

Xu J. ^{[1
]}

Wang J. ^{[1
]}

Ke D. ^{[1
]}

Liao S. ^{[1
]}

Sun Y. ^{[1
]}

Wu Y. ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Wuhan University, Hubei Province, Wuhan

来源：

Dianwang Jishu/Power System Technology | 2024年 / 48卷 / 06期

基金：

中国国家自然科学基金;

关键词：

energy market; reserve market; risk aversion; stochastic optimization; virtual power plant;

D O I：

10.13335/j.1000-3673.pst.2023.2042

中图分类号：

学科分类号：

摘要：

To give full play to the flexibility value of virtual power plants, a bi-level stochastic optimization model is proposed for virtual power plants to participate in the wholesale energy and reserve market. The upper level establishes a two-stage risk-averse decision-making model for a virtual power plant based on conditional value at risk to participate in the electricity market. In the first stage, a bidding model for virtual power plants to participate in the energy and reserve market is established, considering the potential risks of new energy uncertainty. In the second stage, a distributed resource optimization scheduling model is established to minimize the expected operating cost of virtual power plants according to the output of new energy under different scenarios. The lower level is the joint market clearing model of the energy and reserve market based on the bidding information of all participants. Simulation results show that the proposed method can effectively guide virtual power plants to avoid the potential risks of new energy uncertainty and increase their profits by increasing the reserve bidding price to the next marginal generation unit. © 2024 Power System Technology Press. All rights reserved.

引用

页码：2502 / 2510

页数：8

共 30 条

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