2024 05 72-77
基于主从博弈的工业负荷需求响应策略
基金项目(Foundation):
邮箱(Email):
DOI:
10.19929/j.cnki.nmgdljs.2024.0069
中文作者单位:
国网宁夏电力有限公司营销服务中心, 银川 750004;中国电力科学研究院有限公司需求侧多能互补优化与供需互动技术北京市重点实验室, 北京 100192;国网宁夏电力有限公司宁东供电公司, 银川 750411
摘要(Abstract):
为引导工业负荷规模化地参与需求响应（Demand Response，DR），提升调控灵活性，保证电力供需平衡，提出一种基于主从博弈的工业负荷需求响应策略。首先，构建基于主从博弈的工业园区互动优化模型，证明主从博弈均衡解的唯一性，并利用遗传算法联合二次规划求解负荷聚合商（Load Aggregator，LA）针对每个工业用户的最优单位激励及工业用户在该激励下的最优功率削减量。其次，以LA收益最大化为目标，遴选在DR调控任务下最适合参与响应的工业用户。仿真结果表明，本文所提策略在取得良好调控效果的同时能够保障LA的收益。
关键词(KeyWords):
主从博弈;需求响应;工业负荷;负荷聚合商;优化运行
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参考文献
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[15] 姚娟, 张晓文, 王磊, 等.计及碳流的工业企业能源系统运行策略分析[J].电力大数据, 2024, 27(1):54-63. YAO Juan, ZHANG Xiaowen, WANG Lei, et al. Analysis of Energy System Operation Strategy of Industrial Enterprises Considering Carbon Flow[J]. Power Systems and Big Data, 2024, 27(1):54-63.
[16] 刘喜生.基于进化神经网络的短期电网负荷预测算法[J].电力大数据, 2023, 26(4):11-18. LIU Xisheng. Algorithm for Predicting Short-Term Load Based on Evolutionary Neural Network[J]. Power Systems and Big Data, 2023, 26(4):11-18.
[2] 代心芸, 陈皓勇, 肖东亮, 等.电力市场环境下工业需求响应技术的应用与研究综述[J].电网技术, 2022, 46(11):4169-4186. DAI Xinyun, CHEN Haoyong, XIAO Dongliang, et al. Review of applications and research of industrial demand response technology under electricity market environment[J]. Power System Technology, 2022, 46(11):4169-4186.
[3] 李明轩, 齐步洋, 贺大玮.工业园区需求响应资源聚合优化配置方法[J].电网技术, 2022, 46(9):3543-3549. LI Mingxuan, QI Buyang, HE Dawei. Optimal allocation method of resource aggregation in industrial park demand response[J]. Power Grid Technology, 2022, 46(9):3543-3549.
[4] 苏湘波, 吕睿可, 郭鸿业, 等.基于负荷台阶的工业需求响应用户优选方法[J].中国电力, 2024, 57(1):18-29. SU Xiangbo, LYU Ruike, GUO Hongye, et al. User optimization method for industrial demand response based on load step[J]. Electric Power, 2024, 57(1):18-29.
[5] 孙诗洁, 叶承晋, 郭超, 等.考虑工业用户生产特性的精细化用电管控模型及实证研究[J].电力科学与技术学报, 2023, 38(6):44-54. SUN Shijie, YE Chengjin, GUO Chao, et al. Refined power consumption control model and empirical research considering the production characteristics of industrial users[J]. Journal of Electric Power Science and Technology, 2023, 38(6):44-54.
[6] 陈光宇, 杨锡勇, 江海洋, 等.高比例新能源接入下计及工业负荷特性的电网需求响应调控策略[J].电力自动化设备, 2023, 43(4):177-184. CHEN Guangyu, YANG Xiyong, JIANG Haiyang, et al. Power grid demand response control strategy considering industrial load characteristics under high proportion of new energy access[J]. Electric Power Automation Equipment, 2023, 43(4):177-184.
[7] 杨雪莹, 祁琪, 李启明, 等.奖励机制与用户意愿结合的高峰期负荷博弈调度策略[J].电工技术学报, 2024, 39(16):5060-5074. YANG Xueying, QI Qi, LI Qiming, et al. Peak Load Game Scheduling Strategy Combining Reward Mechanism and User Willingness[J]. Transactions of China Electrotechnical Society, 2024, 39(16):5060-5074.
[8] Tavakkoli M, Fattaheian-Dehkordi S, Pourakbari-Kasmaei M, et al. Bonus-based demand response using Stackelberg game approach for residential end-users equipped with HVAC system[J]. IEEE Transactions on Sustainable Energy, 2020, 12(1):234-249.
[9] 徐博强, 张沛超, 何光宇, 等.基于主从博弈的热水器集群的负荷准线控制方法[J].中国电机工程学报, 2022, 42(21):7785-7797. XU Boqiang, ZHANG Peichao, HE Guangyu, et al. Stackelberg Game Based Control Method for Water Heater Cluster Using Customer Directrix Line[J]. Proceedings of the CSEE, 2022, 42(21):7785-7797.
[10] 张虹, 王明晨, 尹世诚, 等.考虑用户消费偏好的分散式电采暖系统主从博弈优化方法[J].电网技术, 2023, 47(6):2262-2273. ZHANG Hong, WANG Mingchen, YIN Shicheng, et al. Master-slave game optimization method for distributed electric heating system considering user consumption preference[J]. Power System Technology, 2023, 47(6):2262-2273.
[11] 刘文霞, 姚齐, 王月汉, 等.基于阶梯型需求响应机制的供需主从博弈电源规划模型[J].电力系统自动化, 2022, 46(20):54-63. LIU Wenxia, YAO Qi, WANG Yuehan, et al. Generation planning model of Stackelberg game between supply and demand based on stepped demand response mechanism[J]. Automation of Electric Power Systems, 2022, 46(20):54-63.
[12] 张衡, 张沈习, 程浩忠, 等.Stackelberg博弈在电力市场中的应用研究综述[J].电工技术学报, 2022, 37(13):3250-3262. ZHANG Heng, ZHANG Shenxi, CHENG Haozhong, et, al. A State-of-the-Art Review on Stackelberg Game and Its Applications in Power Market[J]. Transactions of China Electrotechnical Society, 2022, 37(13):3250-3262.
[13] 王海洋, 李珂, 张承慧, 等.基于主从博弈的社区综合能源系统分布式协同优化运行策略[J].中国电机工程学报, 2020, 40(17):5435-5445. WANG Haiyang, LI Ke, ZHANG Chenghui, et al. Distributed coordinative optimal operation of community integrated energy system based on Stackelberg game[J]. Proceedings of the CSEE, 2020, 40(17):5435-5445.
[14] 王辉, 王宝全, 李旭阳, 等.基于主从博弈的楼宇微网优化运行[J].电力系统及其自动化学报, 2023, 35(2):10-18, 26. WANG Hui, WANG Baoquan, LI Xuyang, et al. Optimal Operation of Building Microgrid Based on Stackelberg Game[J]. Proceedings of the CSU-EPSA, 2023, 35(2):10-18, 26.
[15] 姚娟, 张晓文, 王磊, 等.计及碳流的工业企业能源系统运行策略分析[J].电力大数据, 2024, 27(1):54-63. YAO Juan, ZHANG Xiaowen, WANG Lei, et al. Analysis of Energy System Operation Strategy of Industrial Enterprises Considering Carbon Flow[J]. Power Systems and Big Data, 2024, 27(1):54-63.
[16] 刘喜生.基于进化神经网络的短期电网负荷预测算法[J].电力大数据, 2023, 26(4):11-18. LIU Xisheng. Algorithm for Predicting Short-Term Load Based on Evolutionary Neural Network[J]. Power Systems and Big Data, 2023, 26(4):11-18.
基本信息:
DOI：10.19929/j.cnki.nmgdljs.2024.0069
中图分类号:
引用信息:
[1]顾晓晔1,陈珂2,金旭荣1等.基于主从博弈的工业负荷需求响应策略[J],2024,42(05):72-77.DOI:10.19929/j.cnki.nmgdljs.2024.0069.
基金信息:
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