电力系统自动化2025，Vol.49Issue(20)：94-102,9.DOI:10.7500/AEPS20240910006
面向电能量与调频联合市场的虚拟电厂集群投标策略
Bidding Strategy of Virtual Power Plant Clusters for Joint Market of Electric Power Energy and Frequency Regulation
摘要
Abstract
Multiple virtual power plants(VPPs)participating in the joint market as a cluster can enhance their market competitiveness.To solve the resource allocation of the cluster in the multi-market coupling decision-making and balance the electric power energy and the frequency regulation revenues,this paper proposes a bidding strategy of VPP clusters participating in the electric power energy market and frequency regulation market.Firstly,an internal transaction mode between the VPP and the cluster operator is proposed,and a pricing strategy for the cluster operator and a method for refining the response behavior of the VPP when participating in the external joint market are designed.Secondly,an operation cost model of VPP considering the electric power energy and frequency regulation capacity transaction is established to realize multi-product transactions in the cluster,providing a foundation for the operator to formulate bidding strategies for the joint market.Then,the price quota curve is adopted to characterize the relationship between clearing results of the frequency regulation market and the bidding strategy,and a risk-averse optimal bidding strategy is proposed with the objective of maximizing cluster revenues.Finally,a simulation analysis is conducted on a cluster consisting of three VPPs.The case study results demonstrate that the proposed joint bidding strategy stimulates the response willingness of each VPP and improves the economic revenue of the cluster.关键词
虚拟电厂/投标策略/电能量市场/风险管理/调频/辅助服务Key words
virtual power plant(VPP)/bidding strategy/electric power energy market/risk management/frequency regulation/ancillary service引用本文复制引用
李振坤,张兆柯,李景岳,张智泉,田书欣..面向电能量与调频联合市场的虚拟电厂集群投标策略[J].电力系统自动化,2025,49(20):94-102,9.基金项目
国家自然科学基金资助项目(52177098) （52177098）
上海市科技计划资助项目(21010501200). This work is supported by National Natural Science Foundation of China(No.52177098)and Shanghai Science and Technology Planning Project(No.21010501200). （21010501200）
