Photovoltaic-Hydrogen Energy Storage Multi-Agent Decentralized Cooperative Scheduling Based on Stochastic-Nash-Harsanyi Bargaining Game
Wang Xuejie1, Zhao Huiru2, Cao Yiqiong2, Zhao Mingrui3
1. School of Vehicles and Transportation Tsinghua University Beijing 100084 China; 2. School of Economics and Management North China Electric Power University Beijing 102206 China; 3. Electric Power Construction Technology and Economic Consulting Center China Electricity Council Beijing 100032 China
Abstract：Integrating photovoltaic (PV) and hydrogen energy storage (HES) into a PV-HES joint system proves to be an effective strategy in boosting local consumption of renewable energy and facilitating the low-carbon transformation of energy. The system's internal photovoltaic output and load exhibit considerable randomness, influencing the system's stable operation. Additionally, PV and HES, being typically associated with different stakeholders, each prioritize maximizing their own interests, which limits the effective operation of the PV-HES joint system. Current research often overlooks the bargaining power of diverse entities within the system, failing to fully portray each entity's actual contributions in cooperative efforts amidst uncertainties. In response to these problems, the relationships between entities in the PV-HES combined system should be coordinated and multiple uncertainties should be considered to ensure the stable operation of the PV-HES combined system. Initially, utilizing Nash-Harsanyi bargaining game theory, the study formulates a multi-agent cooperative operational model for PV-HES. This model equivalently addresses the determination of power transaction volume and power transaction cost as distinct subproblems. The Nash-Harsanyi bargaining game extends the Nash bargaining game into an asymmetric game model. This model accounts for participants' bargaining power and their contributions in cooperative efforts, ensuring a fair distribution of benefits. Secondly, stochastic optimal operating models for PV and HES systems were created using probabilistic scenarios, considering uncertainties in both energy supply and load aspects. These models aim to minimize cooperative operational costs. Thirdly, to safeguard the privacy of each participant, the improvement of alternating direction multiplier method (ADMM) is employed to address the aforementioned two subproblems. This method utilizes a two-stage dynamic step size correction approach. To validate the efficacy of the proposed decentralized collaborative scheduling model and distributed solving algorithm, simulations were performed on a standard PV-HES joint system. "The simulation results indicate a reduction in operating costs for HES entities by 11.67% and 10.48% respectively, alongside a 15.72% increase in profit for PV aggregators. Furthermore, the improvement of ADMM method significantly boosts system efficiency, reducing solving time by 70% compared to the traditional ADMM algorithm. The calculation outcomes validate the model's advantages in reducing cooperative operating costs, enhancing solving efficiency, and fostering the utilization of renewable energy. The simulation analysis yields the following conclusions: (1) The proposed multi-agent cooperative operation model of PV-HES strikes a balance between individual rationality and alliance rationality. Unlike traditional cooperative approaches, this model comprehensively accounts for various uncertainties, including energy trading, entities' bargaining power, information privacy, and source load. (2) Employing the enhanced alternating direction multiplier method (ADMM) to resolve the electricity trading quantum problem and its associated cost exhibits favorable convergence characteristics. While safeguarding the confidentiality of individual data, this improved ADMM algorithm effectively addresses the multi-agent collaborative operation problem within PV-HES.
[1] Zhao Xin, Ma Xiaowei, Chen Boyang, et al.Challenges toward carbon neutrality in China: strategies and countermeasures[J]. Resources, Conservation and Recycling, 2022, 176: 105959. [2] 孟沛彧, 向往, 潘尔生, 等. 分址建设直流输电系统拓扑方案与运行特性研究[J]. 电工技术学报, 2022, 37(19): 4808-4822. Meng Peiyu, Xiang Wang, Pan Ersheng, et al.Research on topology and operation characteristics of HVDC transmission system based on site-division construction[J]. Transactions of China Electrotechnical Society, 2022, 37(19): 4808-4822. [3] 孟广雨, 于洁潇, 杨挺. 基于随机网络演算的分布式能源调控系统时延上界计算[J]. 电工技术学报, 2020, 35(11): 2360-2371. Meng Guangyu, Yu Jiexiao, Yang Ting.Upper bound calculation of delay of distributed energy resource coordinated-control system based on stochastic network calculus[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2360-2371. [4] 刘语忱, 闫群民, 郭阳, 等. 基于完备局部均值分解和相关分析的光伏发电侧电-氢混合储能优化配置[J]. 电气技术, 2022, 23(11): 21-29. Liu Yuchen, Yan Qunmin, Guo Yang, et al.Optimal configuration of electricity-hydrogen hybrid energy storage on photovoltaic generation side based on ensemble local mean decomposition and correlation analysis[J]. Electrical Engineering, 2022, 23(11): 21-29. [5] Zhang Hongjie, Yu Wenfei, Hua Wei.Design and key technology of oil-free centrifugal air compressor for hydrogen fuel cell[J]. CES Transactions on Electrical Machines and Systems, 2022, 6(1): 11-19. [6] 粟世玮, 郝翊彤, 宋玉娇, 等. 含风电-氢能-电转气的园区综合能源系统优化调度[J]. 广西师范大学学报(自然科学版), 2023, 41(1): 48-57. Su Shiwei, Hao Yitong, Song Yujiao, et al.Optimal dispatch of park integrated energy system including wind power-hydrogen energy-P2G[J]. Journal of Guangxi Normal University (Natural Science Edition), 2023, 41(1): 48-57. [7] 任洲洋, 罗潇, 覃惠玲, 等. 考虑储氢物理特性的含氢区域综合能源系统中长期优化运行[J]. 电网技术, 2022, 46(9): 3324-3333. Ren Zhouyang, Luo Xiao, Qin Huiling, et al.Mid/long-term optimal operation of regional integrated energy systems considering hydrogen physical characteristics[J]. Power System Technology, 2022, 46(9): 3324-3333. [8] 顾玖, 王晨磊, 解大. 电力市场环境下的电-氢一体化站优化运行[J]. 电力科学与技术学报, 2022, 37(1): 130-139. Gu Jiu, Wang Chenlei, Xie Da.Research on optimal operation of electricity-hydrogen integrated station in electricity market environment[J]. Journal of Electric Power Science and Technology, 2022, 37(1): 130-139. [9] 滕云, 闫佳佳, 回茜, 等. “无废”电-氢充能服务区多源微网优化运行模型[J]. 中国电机工程学报, 2021, 41(6): 2074-2088. Teng Yun, Yan Jiajia, Hui Qian, et al.Optimization operation model of “zero-waste” electricity-hydrogen charging service area multi-energy microgrid[J]. Proceedings of the CSEE, 2021, 41(6): 2074-2088. [10] Ibrahim A, Ali A, Ali E.Stochastic day-ahead unit commitment scheduling of integrated electricity and gas networks with hydrogen energy storage (HES), plug-in electric vehicles (PEVs) and renewable energies[J]. Sustainable Cities and Society, 2021, 67: 102736. [11] Shao Chengcheng, Feng Chenjia, Shahidehpour M, et al.Optimal stochastic operation of integrated electric power and renewable energy with vehicle-based hydrogen energy system[J]. IEEE Transactions on Power Systems, 2021, 36(5): 4310-4321. [12] 王雨晴, 王文诗, 徐心竹, 等. 面向低碳交通的含新能源汽车共享站电-氢微能源网区间-随机混合规划方法[J]. 电工技术学报, 2023, 38(23): 6373-6390. Wang Yuqing, Wang Wenshi, Xu Xinzhu, et al.Hybrid interval/stochastic planning method for new energy vehicle sharing station-based electro-hydrogen micro-energy system for low-carbon transportation[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6373-6390. [13] Lei Gang, Wang Tianshi, Zhu Jianguo, et al.Robust multiobjective and multidisciplinary design optimization of electrical drive systems[J]. CES Transactions on Electrical Machines and Systems, 2018, 2(4): 409-416. [14] Kafaei M, Sedighizadeh D, Sedighizadeh M, et al.An IGDT/Scenario based stochastic model for an energy hub considering hydrogen energy and electric vehicles: a case study of Qeshm Island, Iran[J]. International Journal of Electrical Power & Energy Systems, 2022, 135: 107477. [15] 王再闯, 陈来军, 李笑竹, 等. 基于合作博弈的产销者社区分布式光伏与共享储能容量优化[J]. 电工技术学报, 2022, 37(23): 5922-5932. Wang Zaichuang, Chen Laijun, Li Xiaozhu, et al.Capacity optimization of distributed PV and shared energy storage of prosumer community based on cooperative game[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5922-5932. [16] 任洲洋, 程欢, 周桂平, 等. 考虑外部博弈和内部协同的耦合系统时序随机生产模拟[J]. 电工技术学报, 2023, 38(22): 6204-6217. Ren Zhouyang, Cheng Huan, Zhou Guiping, et al.A sequential probabilistic production simulation method for coupled systems considering external game and internal coordination[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 6204-6217. [17] 希望·阿不都瓦依提, 吕海鹏, 晁勤. 基于非合作博弈的风-光-氢微电网容量优化配置[J]. 电力工程技术, 2022, 41(2): 110-118. Xiwang Abuduwayiti, Lü Haipeng, Chao Qin.Optimal capacity configuration of wind-photovoltaic-hydrogen microgrid based on non-cooperative game theory[J]. Electric Power Engineering Technology, 2022, 41(2): 110-118. [18] 郭旭升, 文汀, 林湘宁, 等. 面向多利益主体的清洁能源化大型离网海岛源储一体化优化定容[J]. 中国电机工程学报, 2019, 39(增刊1): 1-14. Guo Xusheng, Wen Ting, Lin Xiangning, et al.Integration capacity optimization of multi-stakeholder generation-storage on large-scale off-grid island[J]. Proceedings of the CSEE, 2019, 39(S1): 1-14. [19] Wang Haiyang, Zhang Chenghui, Li Ke, et al.Distributed coordinative transaction of a community integrated energy system based on a tri-level game model[J]. Applied Energy, 2021, 295: 116972. [20] Guo Ningxuan, Wang Yinan, Yan Gangfeng.A double-sided non-cooperative game in electricity market with demand response and parameterization of supply functions[J]. International Journal of Electrical Power & Energy Systems, 2021, 126: 106565. [21] 刘晓峰, 高丙团, 李扬. 博弈论在电力需求侧的应用研究综述[J]. 电网技术, 2018, 42(8): 2704-2711. Liu Xiaofeng, Gao Bingtuan, Li Yang.Review on application of game theory in power demand side[J]. Power System Technology, 2018, 42(8): 2704-2711. [22] 晏鸣宇, 王玲玲, 滕飞, 等. 面向分布式能源的可交易能源市场研究综述与展望[J]. 电力系统自动化, 2023, 47(18): 33-48. Yan Mingyu, Wang Lingling, Teng Fei, et al.Review and prospect on transactive energy market for accommodating distributed energy resources[J]. Automation of Electric Power System, 2023, 47(18): 33-48. [23] 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 Huiru, Wang Xuejie, Li Bingkang, 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. [24] Ma Tengfei, Pei Wei, Deng Wei, et al.A Nash bargaining-based cooperative planning and operation method for wind-hydrogen-heat multi-agent energy system[J]. Energy, 2022, 239: 122435. [25] Nazari A, Keypour R, Amjady N.Joint investment of community energy storage systems in distribution networks using modified Nash bargaining theory[J]. Applied Energy, 2021, 301: 117475. [26] 黄海涛, 陈曦, 查俊吉. 多园区综合能源系统分区自治式能量合作社区及联合优化调度[J]. 电网技术, 2022, 46(8): 2955-2965. Huang Haitao, Chen Xi, Zha Junji.Partition autonomous energy cooperation community and its joint optimal scheduling for multi-park integrated energy system[J]. Power System Technology, 2022, 46(8): 2955-2965. [27] Du Yan, Wang Zhiwei, Liu Guangyi, et al.A cooperative game approach for coordinating multi-microgrid operation within distribution systems[J]. Applied Energy, 2018, 222: 383-395. [28] Kim H, Lee J, Bahrami S, et al.Direct energy trading of microgrids in distribution energy market[J]. IEEE Transactions on Power Systems, 2020, 35(1): 639-651. [29] Ghadimi E, Teixeira A, Shames I, et al.Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems[J]. IEEE Transactions on Automatic Control, 2015, 60(3): 644-658. [30] Nord Pool. Day-ahead prices[DB/OL]. [2023-12-12]. http://data.nordpoolgroup.com/auction/day-ahead/prices?deliveryDate=latest¤cy=EUR&aggregation=Hourly&deliveryAreas=AT. [31] Yan Rujing, Lu Zherui, Wang Jiangjiang, et al.Stochastic multi-scenario optimization for a hybrid combined cooling, heating and power system considering multi-criteria[J]. Energy Conversion and Management, 2021, 233: 113911. [32] 林敏洪, 侯祖锋, 王超, 等. 基于分布式内点法的输配协同优化调度[J]. 可再生能源, 2020, 38(9): 1226-1232. Lin Minhong, Hou Zufeng, Wang Chao, et al.Optimal dispatch for transmission distribution coordination system based on distributed interior point method[J]. Renewable Energy Resources, 2020, 38(9): 1226-1232. [33] 吴辰晔, 孙健. 凸包定价模式下的电力市场潜在市场力分析方法[J]. 电力系统自动化, 2021, 45(6): 101-108. Wu Chenye, Sun Jian.Analysis method for potential market power of electricity market in convex hull pricing mode[J]. Automation of Electric Power Systems, 2021, 45(6): 101-108. [34] Li Hongze, Wang Xuejie, Li Fengyun, et al.A robust day-ahead electricity market clearing model considering wind power penetration[J]. Energies, 2018, 11(7): 1772.
2024, Vol. 39 
