纸质出版:2026
移动端阅览
张素芳,杨政益,马琨程,任中睿,王婧.基于Stackelberg博弈的政府-电网-聚合商-用户协同优化调度[J].智慧电力,2026,54(5):21-29.
doi:10.20204/j.sp.2026.05003
张素芳,杨政益,马琨程,任中睿,王婧.基于Stackelberg博弈的政府-电网-聚合商-用户协同优化调度[J].智慧电力,2026,54(5):21-29. DOI: 10.20204/j.sp.2026.05003.
doi:10.20204/j.sp.2026.05003 DOI:
为提升新能源消纳能力、缓解系统峰谷矛盾,深入挖掘电动汽车的柔性调节潜力,构建了政府-电网-聚合商-用户4层全链路Stackelberg博弈协同优化模型,并提出了初始效益预估→迭代交互→精准反馈→收敛验证的模块化求解框架。该框架融合时序生产模拟、模型预测控制(MPC)滚动优化及用户细分聚类技术,通过多场景对比量化了各参与层的边际贡献。仿真验证与分析表明:所提模型经5轮迭代后稳定收敛;与无序充电场景相比,新能源消纳率提升13.8%,火电碳排放降低10.8%,负荷峰谷差下降17.4%;政府动态补贴的单位碳减排效益达227 kgCO₂/元,系统综合效益随博弈层级的完善而显著提升。研究实现了政府、电网、聚合商与用户多方利益的协同增效,显著提升了系统整体运行效益。
To enhance renewable energy integration and alleviate peak-valley contradictions in the power system
this study thoroughly explores the flexible regulation potential of electric vehicles. A four-layer full-chain Stackelberg game coordinated optimization model involving government-grid-aggregator-user is constructed
and a modular solution framework of initial benefit estimation-iterative interaction-precise feedback-convergence verification is proposed. This framework integrates time-series production simulation
model predictive control (MPC) rolling optimization
and user segmentation clustering techniques. The marginal contributions of each participating layer are quantified through multi-scenario comparisons. Simulation verification and analysis show that the proposed model converges stably after five iterations. Compared to the uncoordinated charging scenario
the renewable energy integration rate increases by 13.8%
carbon emissions from thermal power decrease by 10.8%
and the peak-valley load difference drops by 17.4%. The dynamic government subsidy achieves a unit carbon reduction benefit of 227 kgCO₂/CNY
and the overall system benefit improves significantly as the game hierarchy is refined. This study achieves synergistic enhancement of the interests of governments
grid operators
aggregators
and users
substantially improving the overall operational efficiency of the system.
叶小宁,王彩霞,时智勇,等. 国外高比例新能源消纳分析及对中国新能源可持续发展的建议 [J ] .中国电力,2025,58(6): 137-144.
李希德,张艳,曹阳,等. 基于精细化模拟的电力系统电力电量平衡研究 [J ] .智慧电力,2024,52(10):70-78.
杨哲,王娟娟. 基于演化博弈论的电动汽车充放电策略研究 [J ] .自动化与仪表,2025,40(9): 6-10,16.
郭红霞,李琳,陈凌轩,等. 基于双层主从博弈的网-商-车协同优化调度 [J ] .电力自动化设备,2025,45(10): 144-150,185.
李青,刘雨婷,刘 超. 车网互动技术的用户接受度分析与对策建议 [J ] .电力需求侧管理,2025,27(3):87-93.
喻磊,王锐,程杉,等. 基于多主体主从博弈的电动汽车充电站双层定价策略 [J ] .电力科学与技术学报,2025,40(3):243-254.
何云华,程宇航,袁晓冬,等. 面向V2G调度的可信联邦学习方法 [J ] .分布式能源.2024,9(6): 65-74
马乾鑫,加鹤萍,郭宇辰,等. 基于CVaR的虚拟电厂与电动汽车主从博弈策略 [J ] .电力建设,2025,46(7): 53-66.
YU H,NIU SY,SHANG YT,et al. Electric vehicles integration and vehicle-to-grid operation in active distribution grids: a comprehensive review on power architectures,grid connection standards and typical applications [J ] .Renewable & Sustainable Energy Reviews,2022,168: 112812.
SUN J,WANG X,QI GQ,et al. Resilient frequency regulation for DoS attack intensity adaptation via predictive reinforcement V2G control learning [J ] .IEEE Transactions on Smart Grid,2025,16(1): 313-329.
车彬,张泽龙,杨燕. 考虑V2G储能特性与负荷需求响应的主动配电网低碳鲁棒调度 [J ] .电网与清洁能源,2024,40(1):29-39.
QU YQ. Economic operation for V2G technology and multiple influence factors extraction of EV penetration [J ] .Energy Science & Engineering,2025,13(4): 1819-1830.
YUE YT,ZHANG QH,ZHANG JR,et al. Orderly charging and discharging group scheduling strategy for electric vehicles [J ] .Applied Sciences,2023,13(24): 13156.
王明深,李毅强,王伟亮,等. 考虑充放电响应度的电动汽车聚合商与机组协同参与调频市场运营策略 [J ] .电网技术,2025,49(7): 2809-2818.
MAO MQ,WANG YY,YUE Y,et al. Multi-Time scale forecast for schedulable capacity of EVs based on big data and machine learning [C ] //2017 IEEE Energy Conversion Congress and Exposition.Piscataway: IEEE,2017: 1425-1431.
肖怡康,牟玉亭,潘博,等. 面向电动汽车聚合商参与电能量-备用市场的分色电价设计 [J ] .电力工程技术,2025,44(6):13-24.
DONG JW,YASSINE A,ARMITAGE A,et al. Multi-agent reinforcement learning for intelligent V2G integration in future transportation systems [J ] .IEEE Transactions on Intelligent Transportation Systems,2023,24(12): 15974-15983.
郭征,欧媛,费婕,等. 考虑电动汽车充电的主动配电网阻塞调度方法 [J ] .电子设计工程,2024,32(15):81-85.
WANG JX,WANG ZJ,YANG B,et al. V2G for frequency regulation service: a stackelberg game approach considering endogenous uncertainties [J ] .IEEE Transactions on Transportation Electrification,2025,11(1): 463-475.
陈伟,陈龙康,郝鹏辉,等. 基于自建储能共享与辅助服务的配电网和光伏聚合商主从博弈优化调度策略 [J ] .电力自动化设备,2025,45(8): 87-95.
王云龙,何山,艾纯玉,等. 考虑云储能的综合能源运营商-负荷聚合商联盟混合博弈定价策略 [J ] .电网与清洁能源,2024,40(6): 11-19,29.
李雄飞,李康平,黄淳驿,等. 考虑用户响应差异的聚合商需求响应投标定价联合优化方法 [J ] .电力系统自动化,2025,49(11): 70-79.
朱虹,孟祥娟,孙健,等. 用户响应机制下基于长短期记忆网络的负荷聚合商用电模型 [J ] .现代电力,2025,42(3): 550-561.
庞维震,马艳红,金春华,等. 基于Stackelberg博弈的V2G交易激励策略研究 [J ] .智慧电力,2025,53(1): 17-23.
姜越. 车网互动(V2G)发展的机遇、挑战及建议 [J ] .上海节能,2024(12): 1901-1905.
WANG D,SECHILARIU M,LOCMENT F. PV-Powered charging station for electric vehicles: Power management with integrated V2G [J ] .Applied Sciences,2020,10(18): 6500.
余子淳,范宏,夏世威,等. 计及电动公交车V2G响应的区域综合能源系统两阶段优化调度 [J ] .中国电力,2022,55(7): 179-192.
杨捷,郭凡,曹子健. 电动汽车储能V2G模式的成本与收益分析 [J ] .储能科学与技术,2020,9(S1): 45-51.
徐蕙,陈平,李香龙,等. 基于Stackelberg博弈的电动汽车充电管理策略 [J ] .计算机应用与软件,2024,41(8): 382-388.
倪晋兵,张云飞,施浩波,等. 基于时序生产模拟的抽水蓄能促进新能源消纳作用量化研究 [J ] .电网技术,2023,47(7): 2799-2809.
刘贵宇,周霞,戴剑丰,等. 基于IDBO算法的电动汽车有序充放电控制策略 [J ] .浙江电力,2025,44(08):34-43.
张强,曹铂苒,刘洪,等. 适应光伏消纳的电动汽车-聚合商-配电网日前时空互动方法 [J ] .电力系统自动化,2025,49(14): 141-151.
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621