中国电机工程学报2026，Vol.46Issue(6)：2446-2458,中插22,14.DOI:10.13334/j.0258-8013.pcsee.242341
电热联合双向拍卖市场下多能产消者的最优竞价策略
Research on the Optimal Bidding Strategy of Multi-energy Prosumers in the Electricity-heat Combination Double Auction Market
摘要
Abstract
With the proliferation of multi-energy prosumers(MEPs)in the community energy system,the efficient energy management model becomes particularly important.Therefore,this paper designs an electricity-heat combination double auction market,and proposes an optimal bidding strategy based on bidding space and conditional value at risk for MEPs.First,according to the community energy system framework,it introduces the bidding rules,clearing mechanism and information disclosure method of the electricity-heat combination market.Then,the MEP's energy supply cost model and the market clearing price prediction model are established,which constitute the bidding space of MEP.By analyzing the bidding space,it determines the optimal bidding quantity and the reasonable bidding price range.Considering the new bidding risk of the electricity-heat combination double auction market,an optimal bidding price model is proposed based on the conditional value at risk,which helps decide the most optimal price within the given bidding price range.The optimal quantity and the optimal price together constitute the optimal bidding strategy of MEP.Finally,taking a community energy system as an example,the simulation results show that the proposed electricity-heat combination double auction market and the proposed optimal bidding strategy can increase the economic benefit of the community energy system by 15.4%.Compared with"cost-plus method"and"MCP prediction method",the proposed bidding strategy has stronger market competitiveness.关键词
社区能源系统/多能产消者/双向拍卖/竞价策略/条件风险价值Key words
community energy system/multi-energy prosumer/double auction/bidding strategy/conditional value at risk分类
信息技术与安全科学引用本文复制引用
符杨,单杰,李振坤..电热联合双向拍卖市场下多能产消者的最优竞价策略[J].中国电机工程学报,2026,46(6):2446-2458,中插22,14.基金项目
国家自然科学基金项目(U2066214). Project Supported by National Natural Science Foundation of China(U2066214). （U2066214）
