基于主从博弈的配电网与多微网两阶段能源管理策略

刘佳佳; 田铭兴; 毛旭升

doi:10.19912/j.0254-0096.tynxb.2024-2000

PDF(1850 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (6) : 347-359. DOI: 10.19912/j.0254-0096.tynxb.2024-2000

基于主从博弈的配电网与多微网两阶段能源管理策略

刘佳佳^1,2, 田铭兴^1,2, 毛旭升^1,2

作者信息 +

TWO-STAGE ENERGY MANAGEMENT STRATEGY FOR DISTRIBUTION NETWORKS AND MULTI-MICROGRIDS BASED ON MASTER-SLAVE GAME

Liu Jiajia^1,2, Tian Mingxing^1,2, Mao Xusheng^1,2

Author information +

文章历史 +

摘要

针对同一配电网下的多微网系统（MMG）的能源管理策略问题,提出一种基于主从博弈的配电网与多微网两阶段能源管理策略：第一阶段计及可再生能源发电出力的不确定性,构建Wasserstein分布鲁棒模型,引入虚拟储能,考虑微网间点对点（P2P）电能交互,建立配电网与MMG系统的主从博弈管理策略,并采用Kriging元模型和改进的SCSO算法进行求解,实现配电网与MMG的能源协同管理;第二阶段在考虑MMG内部P2P电能交易的背景下,为最大化降低微电网运营成本,采用一种基于最大运行成本降低比例（MOCRR）分摊的共享能源价格出清与结算模型,通过ADMM算法对其进行分布式求解,有效保护多微网主体的信息安全和隐私。最后,采用算例验证所提方法的可行性和有效性。

Abstract

Aiming at the problem of energy management strategy for multi-microgrids （MMG） systems under the same distribution network, a two-stage energy management strategy based on master-slave game is proposed for distribution networks and MMGs： In the first stage, taking into account the uncertainty of the renewable energy power generation, a Wasserstein distribution robust model is constructed, virtual energy storage is introduced, and the peer-to-peer （P2P） power interaction between microgrids is taken into account, and a Kriging game is used to establish a master-slave management strategy. Peer-to-peer （P2P） power interactions are considered, and a master-slave game management strategy for the distribution grid and MMG system is established, which is solved by the Kriging meta-model and the improved SCSO algorithm to achieve collaborative energy management between the distribution grid and MMG; In the second stage, in order to minimize the operation cost of the microgrid, a shared energy price clearing and settlement model based on the allocation of the maximum ope ating cost reduction ration（MOCRR） is adopted and it is distributed and solved through the ADMM algorithm, effectively protecting the inforination security and privacy of the main body of multiple microgrids. Finally, an example is used to verify the feasibility and effectiveness of the proposed method.

导出引用

刘佳佳, 田铭兴, 毛旭升. 基于主从博弈的配电网与多微网两阶段能源管理策略[J]. 太阳能学报. 2025, 46(6): 347-359 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2000

Liu Jiajia, Tian Mingxing, Mao Xusheng. TWO-STAGE ENERGY MANAGEMENT STRATEGY FOR DISTRIBUTION NETWORKS AND MULTI-MICROGRIDS BASED ON MASTER-SLAVE GAME[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 347-359 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2000

中图分类号： TM73

参考文献

[1] 王蓓蓓, 吴文强, 张汀荟, 等. 考虑动态过网费的分布式电能交易与配电网运营联合交互[J]. 电力系统自动化, 2023, 47(18): 91-100.
WANG B B, WU W Q, ZHANG T H, et al.Joint interaction between distributed power trading and distribution network operation considering dynamic network tariffs[J]. Automation of electric power systems, 2023, 47(18): 91-100.
[2] 刘艳峰, 刘正学, 罗西, 等. 基于柔性负荷的孤立多能互补建筑能源系统优化设计[J]. 太阳能学报, 2022, 43(6): 24-32.
LIU Y F, LIU Z X, LUO X, et al.Design of isolated multi-energy complementary building energy system based on flexible load[J]. Acta energiae solaris sinica, 2022, 43(6): 24-32.
[3] 乔学博, 杨志祥, 李勇, 等. 计及两级碳交易和需求响应的多微网合作运行优化策略[J]. 高电压技术, 2022,48(7): 2573-2583.
QIAO X B, YANG Z X, LI Y, et al.Optimization strategy for cooperative operation of multi-microgrids considering two-level carbon trading and demand response[J]. High voltage engineering, 2022, 48(7): 2573-2583.
[4] 彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
PENG C H, ZHANG J K, CHEN L, et al.Source-load-storage coordinated optimal scheduling of microgrid considering differential demand response[J]. Electric power automation equipment, 2020, 40(3): 1-7.
[5] 周步祥, 李祖钢, 陈实, 等. 基于区块链的微电网合作博弈电力交易优化[J]. 电测与仪表, 2022, 59(8): 39-46.
ZHOU B X, LI Z G, CHEN S, et al.Power transaction optimization of micro-grid cooperative game based on block chain[J]. Electrical measurement & instrumentation, 2022, 59(8): 39-46.
[6] 张凡, 高红均, 吴子豪, 等. 局域能源市场多产消者P2P交易框架设计[J]. 电力自动化设备, 2022, 42(12): 17-25.
ZHANG F, GAO H J, WU Z H, et al.Design of P2P trading framework for multiple prosumers in local energy market[J]. Electric power automation equipment, 2022, 42(12): 17-25.
[7] 陈修鹏, 李庚银, 周明, 等. 考虑新能源不确定性和点对点交易的配网产消者分布式优化调度[J]. 电网技术, 2020, 44(9): 3331-3340.
CHEN X P, LI G Y, ZHOU M, et al.Distributed optimal scheduling for prosumers in distribution network considering uncertainty of renewable sources and P2P trading[J]. Power system technology, 2020, 44(9): 3331-3340.
[8] 潘瑞媛, 唐忠, 史晨豪, 等. 基于主从博弈的多主体投资多微网系统优化配置[J]. 中国电力, 2022, 55(6): 65-73, 127.
PAN R Y, TANG Z, SHI C H, et al.Optimal configuration of multi-microgrid system with multi-agent joint investment based on Stackelberg game[J]. Electric power, 2022, 55(6): 65-73, 127.
[9] 帅轩越, 马志程, 王秀丽, 等. 基于主从博弈理论的共享储能与综合能源微网优化运行研究[J]. 电网技术, 2023, 47(2): 679-690.
SHUAI X Y, MA Z C, WANG X L, et al.Optimal operation of shared energy storage and integrated energy microgrid based on leader-follower game theory[J]. Power system technology, 2023, 47(2): 679-690.
[10] 佟新, 胡存刚, 芮涛. 基于主从博弈的多微电网系统经济优化方法[J]. 中国电力, 2019, 52(6): 68-76.
TONG X, HU C G, RUI T.Master-slave game based on economic optimization method of multi-microgrid system[J]. Electric power, 2019, 52(6): 68-76.
[11] 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 H Y, LI K, ZHANG C H, 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.
[12] 周长城, 马溪原, 郭晓斌, 等. 基于主从博弈的工业园区综合能源系统互动优化运行方法[J]. 电力系统自动化, 2019, 43(7): 74-80.
ZHOU C C, MA X Y, GUO X B, et al.Leader-follower game based optimized operation method for interaction of integrated energy system in industrial park[J]. Automation of electric power systems, 2019, 43(7): 74-80.
[13] 李鹏, 吴迪凡, 李雨薇, 等. 基于综合需求响应和主从博弈的多微网综合能源系统优化调度策略[J]. 中国电机工程学报, 2021, 41(4):1307-1321.
LI P,WU D F,LI Y W,et al.Optimal dispatch of multi- microgrids integrated energy system based on integrated demand response and Stackelberg game[J] Proceedings of the CSEE, 2021, 41(4):1307-1321.
[14] XU D, ZHOU B, LIU N, et al.Peer-to-peer multienergy and communication resource trading for interconnected microgrids[J]. IEEE transactions on industrial informatics, 2021, 17(4): 2522-2533.
[15] 杨昭, 艾欣. 考虑电能共享的综合能源楼宇群分布式优化调度[J]. 电网技术, 2020, 44(10): 3769-3778.
YANG Z, AI X.Distributed optimal scheduling for integrated energy building clusters considering energy sharing[J]. Power system technology, 2020, 44(10): 3769-3778.
[16] 王怡, 王小君, 孙庆凯, 等. 基于能量共享的综合能源系统群多主体实时协同优化策略[J]. 电力系统自动化, 2022, 46(4): 56-65.
WANG Y, WANG X J, SUN Q K, et al.Multi-agent real-time collaborative optimization strategy for integrated energy system group based on energy sharing[J]. Automation of electric power systems, 2022, 46(4): 56-65.
[17] CHUNG H M, MAHARJAN S, ZHANG Y, et al.Intelligent charging management of electric vehicles considering dynamic user behavior and renewable energy: a stochastic game approach[J]. IEEE transactions on intelligent transportation systems, 2021, 22(12): 7760-7771.
[18] DAI Y M, QI Y, LI L, et al.A dynamic pricing scheme for electric vehicle in photovoltaic charging station based on Stackelberg game considering user satisfaction[J]. Computers & industrial engineering, 2021, 154: 107117.
[19] 王程, 汪松, 毕天姝. 含燃气发电综合能源系统风电消纳能力评估[J]. 中国电机工程学报, 2020, 40(7): 2192-2201, 2398.
WANG C, WANG S, BI T S.Wind power accommodation capability assessment of integrated energy systems with gas-fired units[J]. Proceedings of the CSEE, 2020, 40(7): 2192-2201, 2398.
[20] 石锦凯, 鲍谚, 陈振, 等. 计及充电负荷不确定性的充电站储能鲁棒优化配置方法[J]. 电力系统自动化, 2021, 45(20): 49-58.
SHI J K,BAO Y,CHEN Z,et al.Robust optimization configuration method of energy storage for charging stations considering charging load uncertainty[J]. Automation of electric power systems, 2021, 45(20): 49-58.
[21] 赵会茹, 王学杰, 李兵抗, 等. 考虑能量共享的多社区光储系统分布鲁棒优化调度[J]. 电力系统自动化, 2022, 46(9): 21-31.
ZHAO H R, WANG X J, LI B K, et al.Distributionally robust optimal dispatch for multi-community photovoltaic and energy storage system considering energy sharing[J]. Automation of electric power systems, 2022, 46(9): 21-31.
[22] 陈保瑞, 刘天琪, 何川, 等.考虑需求响应的源网荷协调分布鲁棒长期扩展规划[J]. 中国电机工程学报, 2021, 41(20): 6886-6900.
CHEN B R,LIU T Q,HE C,et al.Distributionally robust coordinated expansion planning for generation and transmission systems with demand response[J]. Proceedings of the CSEE, 2021,41(20): 6886-6900.
[23] 郑诗程,许浩,郎佳红,等.计及光伏不确定性的多区域综合能源系统多场景分布鲁棒优化调度[J]. 太阳能学报, 2024, 45(3):460-469.
ZHENG S C,XU H,LANG J H,et al.Multi-scenario distributed robust optimal scheduling of multi-area integrated energy systems considering photovoltaic uncertainty[J].Acta energiae solaris sinica, 2024, 45(3): 460-469.
[24] ZHU X, YANG J, LIU Y, et al.Optimal scheduling method for a regional integrated energy system considering joint virtual energy storage[J]. IEEE access, 2019, 7: 138260-138272.
[25] WANG D X, MENG K, GAO X D, et al.Coordinated dispatch of virtual energy storage systems in LV grids for voltage regulation[J]. IEEE transactions on industrial informatics, 2018, 14(6): 2452-2462.
[26] 霍高臣, 苏海滨. 基于虚拟储能的楼宇微电网综合能源优化研究[J]. 太阳能学报, 2024, 45(9): 678-689.
HUO G C, SU H B.Comprehensive energy optimization research for building microgrids based on virtual energy storage[J]. Acta energiae solaris sinica, 2024, 45(9): 678-689.
[27] 田海东,何山.基于主从博弈的配电网与多微网双层优化研究[J]. 电网与清洁能源, 2023, 39(9): 55-66.
TIAN H D, HE S.Double-layer optimization of distribution networks and multi-microgrids based on master-slave game[J]. Power system and clean energy, 2023, 39(9): 55-66.