混合FCC-IGDT的负荷聚合商市场投标决策模型

程瑜; 闫宇露

doi:10.19912/j.0254-0096.tynxb.2025-1045

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 812-820. DOI: 10.19912/j.0254-0096.tynxb.2025-1045

混合FCC-IGDT的负荷聚合商市场投标决策模型

程瑜, 闫宇露

作者信息 +

MARKET BIDDING DECISION MODEL OF LOAD AGGREGATOR BASED ON HYBRID FCC-IGDT

Cheng Yu, Yan Yulu

Author information +

文章历史 +

摘要

面向负荷聚合商参与电能量-备用联合市场面临的外部市场价格和内部聚合资源响应行为的不确定性风险,结合价格接受型负荷聚合商的风险态度,优化其在日前电能量和备用联合市场中的投标策略。为支撑负荷聚合商细粒度管理响应行为以及市场价格双重风险因素导致的购售电收益和备用服务收益风险,将模糊机会约束（FCC）和信息间隙决策理论（IGDT）相结合,建立考虑风险约束的聚合商混合FCC-IGDT市场投标决策模型。算例仿真表明,模型可兼顾负荷聚合商收益稳健性和风险敏感性需求,基于风险管理驱动协同优化聚合商日前电能量及备用容量投标决策,制定风险预警和风险对冲机会捕捉策略,提升聚合商市场竞争力。

Abstract

Load aggregators in the day-ahead joint energy-reserve market face uncertainties from external market price volatility and the internal responsiveness of aggregated resources. Accounting for the risk preferences of price-taking aggregators, we optimize bidding strategies for both energy and reserve markets. To support fine-grained management of response behavior and the associated risks to energy-trading and reserve revenues undes dual sources of uncertainty （prices and resource responses）, we integrate fuzzy chance constraint （FCC） with information gap decision theory （IGDT） to develop a risk-constrained hybrid FCC-IGDT bidding model. Simulation results show that the model meets requirements for both revenue robustness and risk sensitivity, enabling risk-management-driven co-optimization of day-ahead energy offers and reserve capacity bids. It also provides mechanisms for early warning and identification of hedging opportunities, thereby enhancing aggregators’ market competitiveness.

导出引用

程瑜, 闫宇露. 混合FCC-IGDT的负荷聚合商市场投标决策模型[J]. 太阳能学报. 2026, 47(7): 812-820 https://doi.org/10.19912/j.0254-0096.tynxb.2025-1045

Cheng Yu, Yan Yulu. MARKET BIDDING DECISION MODEL OF LOAD AGGREGATOR BASED ON HYBRID FCC-IGDT[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 812-820 https://doi.org/10.19912/j.0254-0096.tynxb.2025-1045

中图分类号： TM73

参考文献

[1] 国家能源局. 关于支持电力领域新型经营主体创新发展的指导意见[EB/OL]. (2024-11-28)[2025-06-12]. https://zfxxgk.nea.gov.cn/2024-11/28/c_1212408354.htm.
National Energy Administration. Guidance on supporting the innovative development of new business entities in the electric power field[EB/OL]. (2024-11-28)[2025-06-12]. https://zfxxgk.nea.gov.cn/2024-11/28/c_1212408354.htm.
[2] Khojasteh M, Faria P, Lezama F, et al.Optimal strategy of electricity and natural gas aggregators in the energy and balance markets[J]. Energy, 2022, 257: 124753.
[3] 孙勇, 仪忠凯, 李宝聚, 等. 多元零售市场环境下电力聚合商有功-无功协同优化竞标策略[J]. 电力建设, 2024, 45(10): 146-157.
Sun Y, Yi Z K, Li B J, et al.Active and reactive power collaborative bidding strategy for the power aggregator in multiple categories of retail markets[J]. Electric Power Construction, 2024, 45(10): 146-157.
[4] Iria J, Soares F, Matos M.Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets[J]. Applied Energy, 2019, 238: 1361-1372.
[5] 王鹏, 贺焕然, 伏凌霄, 等. 多品种电力市场交易下负荷聚合商投标策略及市场均衡分析[J]. 电力系统自动化, 2024, 48(4): 111-122.
Wang P, He H R, Fu L X, et al.Bidding strategy of load aggregators and market equilibrium analysis for multi-variety electricity market trading[J]. Automation of Electric Power Systems, 2024, 48(4): 111-122.
[6] 程松, 周鑫, 任景, 等. 面向多级市场出清的负荷聚合商联合交易策略[J]. 电力系统保护与控制, 2022, 50(20): 158-167.
Cheng S, Zhou X, Ren J, et al.Bidding strategy for load aggregators in a multi-stage electricity market[J]. Power System Protection and Control, 2022, 50(20): 158-167.
[7] Zheng Y C, Wang Y B, Yang Q.Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: a risk-averse approach[J]. Energy, 2023, 283: 129138.
[8] Vatandoust B, Ahmadian A, Golkar M A, et al.Risk-averse optimal bidding of electric vehicles and energy storage aggregator in day-ahead frequency regulation market[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2036-2047.
[9] 许思颖, 王旭, 蒋传文, 等. 基于WCVaR的区域综合能源运营商交易策略研究[J]. 电网技术, 2021, 45(8): 3207-3218.
Xu S Y, Wang X, Jiang C W, et al.WCVaR-based transaction strategy of regional integrated energy system operators[J]. Power System Technology, 2021, 45(8): 3207-3218.
[10] 张虹, 侯宁, 葛得初, 等. 供需互动分布式发电系统收益-风险组合优化建模及其可靠性分析[J]. 电工技术学报, 2020, 35(3): 623-635.
Zhang H, Hou N, Ge D C, et al.Modeling and reliability analysis of benefit-risk portfolio optimization for supply and demand interactive distributed generation system[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 623-635.
[11] Zhao J, Wan C, Xu Z, et al.Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 1609-1618.
[12] Kim J H, Hwang J S, Kim Y S.An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties[J]. Energy, 2024, 313: 133712.
[13] 谭颖, 管霖. 基于数据驱动信息间隙理论的风光水储聚合商多元市场竞标决策模型[J]. 电网技术, 2024, 48(3): 979-989.
Tan Y, Guan L.A data-driven information gap theory based joint decision model for aggregator with wind-solar-hydro power and storage resources in multiple power markets[J]. Power System Technology, 2024, 48(3): 979-989.
[14] 周亦洲, 李想, 沈思辰, 等. 基于信息间隙决策理论的产消者多市场产品分布式交易[J]. 电力系统自动化, 2023, 47(23): 122-130.
Zhou Y Z, Li X, Shen S C, et al.Distributed multi-market product transactions of prosumers based on information gap decision theory[J]. Automation of Electric Power Systems, 2023, 47(23): 122-130.
[15] 王永利, 董焕然, 延子昕, 等. 基于融合IGDT的综合能源系统市场联合交易优化[J]. 系统工程理论与实践, 2025, 45(3): 867-885.
Wang Y L, Dong H R, Yan Z X, et al.Optimisation of joint trading in integrated energy system markets based on converged IGDT[J]. Systems Engineering: Theory & Practice, 2025, 45(3): 867-885.
[16] 詹博淳, 冯昌森, 卢治霖, 等. 基于信息间隙决策的分布式产消者电-备用市场投标策略[J]. 电力自动化设备, 2024, 44(9): 162-169.
Zhan B C, Feng C S, Lu Z L, et al.Bidding strategy of energy-reserve market for distributed prosumers based on information gap decision[J]. Electric Power Automation Equipment, 2024, 44(9): 162-169.
[17] 李东东, 张凯, 姚寅, 等. 基于信息间隙决策理论的电动汽车聚合商日前需求响应调度策略[J]. 电力系统保护与控制, 2022, 50(24): 101-111.
Li D D, Zhang K, Yao Y, et al.Day-ahead demand response scheduling strategy of an electric vehicle aggregator based on information gap decision theory[J]. Power System Protection and Control, 2022, 50(24): 101-111.
[18] Najafi A, Pourakbari-Kasmaei M, Jasinski M, et al.A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems[J]. Energy, 2022, 238: 121661.
[19] Khaloie H, ValléE F, Lai C S, et al. Day-ahead and intraday dispatch of an integrated biomass-concentrated solar system: a multi-objective risk-controlling approach[J]. IEEE Transactions on Power Systems, 2022, 37(1): 701-714.
[20] 贾海清, 林延平, 刘茹, 等. 考虑需求侧无功不确定性的负荷聚合商运营优化模型[J]. 太阳能学报, 2022, 43(6): 17-23.
Jia H Q, Lin Y P, Liu R, et al.Load aggregator operation optimization model considering demand response uncertainty[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 17-23.
[21] 张粒子, 杨萌, 梁伟, 等. 辅助服务市场中集成商的资源集成投标策略[J]. 电网技术, 2019, 43(8): 2808-2814.
Zhang L Z, Yang M, Liang W, et al.Bidding strategy of resource integration for aggregator in ancillary service market[J]. Power System Technology, 2019, 43(8): 2808-2814.
[22] 罗纯坚, 李姚旺, 许汉平, 等. 需求响应不确定性对日前优化调度的影响分析[J]. 电力系统自动化, 2017, 41(5): 22-29.
Luo C J, Li Y W, Xu H P, et al.Influence of demand response uncertainty on day-ahead optimization dispatching[J]. Automation of Electric Power Systems, 2017, 41(5): 22-29.
[23] 孙宇军, 王岩, 王蓓蓓, 等. 考虑需求响应不确定性的多时间尺度源荷互动决策方法[J]. 电力系统自动化, 2018, 42(2): 106-113, 159.
Sun Y J, Wang Y, Wang B B, et al.Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power Systems, 2018, 42(2): 106-113, 159.
[24] 张晶晶, 张鹏, 吴红斌, 等. 负荷聚合商参与需求响应的可靠性及风险分析[J]. 太阳能学报, 2019, 40(12): 3526-3533.
Zhang J J, Zhang P, Wu H B, et al.Reliability and risk analysis of load aggregators in demand response[J]. Acta Energiae Solaris Sinica, 2019, 40(12): 3526-3533.
[25] 刘宝碇. 不确定规划及应用[M]. 北京: 清华大学出版社, 2003: 178-187.
Liu B D.Uncertain programming with applications[M]. Beijing: Tsinghua University Press, 2003: 178-187.
[26] 金旭, 张远实, 李明, 等. 考虑热舒适度的居民空调负荷调控潜力差异化评估[J]. 电力系统自动化, 2024, 48(1): 50-58.
Jin X, Zhang Y S, Li M, et al.Differentiation evaluation of regulation potential for residential air conditioning load considering thermal comfort[J]. Automation of Electric Power Systems, 2024, 48(1): 50-58.