考虑空间分区划分与长周期氢储能调节的分布式电氢耦合能源站优化配置

doi:10.19595/j.cnki.1000-6753.tces.251110

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摘要

针对目前分布式电氢耦合能源站（DEHS）接入配电网规划中存在的空间跨区协同与长周期氢储能调节潜力问题,该文提出分区-选址-定容的DEHS优化策略。首先在分区层上建立基于改进边权重的配电网分区模块度指标,利用基于变异策略的离散粒子群算法（MDPSO）对配电网区域划分。其次,运用基于Ward法的源-荷时间数据聚合方法,对源-荷时间序列缩减。在此基础上,选址层构建嵌入分区约束的动态最优潮流模型（DOPF）,以全网网损最小化为目标,利用MDPSO优化DEHS接入位置;定容层上建立 DEHS全生命周期成本（包括设备投资、运维及置换成本）优化模型,综合考虑电-氢储能长短周期调节能力,通过Gurobi进行容量配置求解。最后以光伏及负荷接入的IEEE 33节点配电网对该优化策略的有效性进行验证。

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关键词 ：分布式电氢耦合能源站, 配电网, 分区模块度指标, 选址定容, 长短周期

Abstract：

At present, distributed electric-hydrogen coupled energy stations (DEHS) mostly focus on short-term dispatching, underestimate the strategic value of hydrogen energy storage in the resilience construction of new power systems, and do not consider the zoning structure of the distribution network, such as multi-regional power flow mutual aid and load temporal and spatial differences, resulting in the DEHS layout and grid collaborative optimization limited to local nodes, and the lack of cross-regional resource coordination capabilities. Therefore, this paper takes the distribution network as the main body and proposes a DEHS optimization strategy of zoning-site selection-capacity setting.
Firstly, at the partition level, the modularity index of distribution network partition based on improved edge weight is established, and the mutation discrete particle swarm optimization (MDPSO) based on mutation strategy is used to solve the problem. In view of the fact that the classical clustering method is difficult to retain the continuity of time series when processing data, the DEHS site selection and capacity configuration requirements in this paper cannot be applied. Therefore, the source-load time data reduction method based on the WARD method is used to reduce the time series of the source-load data. On this basis, the site selection and volume determination model of DEHS was established. The site selection model is based on the dynamic optimal flow (DOPF), with the goal of minimizing the network loss of the distribution network, and using MDPSO to plan the access location of DEHS. At the constant capacity level, based on the site selection and planning, considering the characteristics of the long and short cycles of electricity-hydrogen energy storage, the optimal capacity of each equipment is solved with the lowest target of the whole life cycle cost, including equipment investment, operation and maintenance and replacement costs.
Through the comparative analysis of different partition indicators, it can be seen that the proposed strategy not only improves the strength of the partition structure (0.36% higher than that of Scheme 2), but also improves the electrical coupling strength of the nodes in the partition (the proposed strategy reduces the voltage deviation by 13.14% and 16.29% compared with Scheme 2 and Scheme 3). DEHS not only alleviates the impact of new energy on the distribution network, but also reduces the network loss of the distribution network (the proposed strategy is 55.68% less than that of the unplanned situation, and 1.54% and 3.73% less than that of Scheme 2 and Scheme 3), and the hydrogen load in the zone is supported by the DEHS station under the guarantee of minimum cost (the proposed strategy is reduced by 1.56% and 2.69% compared with Scheme 2 and Scheme 3) and the optimal coordinated operation of each equipment. Through the comparative analysis of different scenarios, the battery energy storage capacity in DEHS is significantly reduced, which is 58% lower than that in the case of single battery energy storage, which avoids the overallocation of batteries in long-term energy storage. In terms of total cost, Scenario 2 is 37.84% lower than Scenario 1, indicating that moderate access to hydrogen energy storage can significantly reduce costs, and verifying the potential of hydrogen energy storage in long-term and large-capacity energy storage costs.

Key words： Distributed electro-hydrogen coupled energy station distribution network partition modularity site selection and capacity long-short term

收稿日期: 2025-06-24

PACS:

TM614

基金资助:

国家电网有限公司总部科技项目资助（52240025000H-007-ZN）

通讯作者: 董晓睿女,1985年生,博士研究生,研究方向为电氢耦合物理与数学计算。E-mail：xiaoruidong@yeah.net

作者简介: 孔令国男,1984年生,教授,博士,研究方向为电氢耦合理论与关键技术。E-mail：klgwin@neepu.edu.cn

引用本文:

孔令国, 田杨进, 董晓睿, 周星, 谢佳兵. 考虑空间分区划分与长周期氢储能调节的分布式电氢耦合能源站优化配置[J]. 电工技术学报, 2026, 41(14): 4988-5002. Kong Lingguo, Tian Yangjin, Dong Xiaorui, Zhou Xing, Xie Jiabing. Consider the Optimization Configuration of Distributed Electro-Hydrogen Coupled Energy Stations For Spatial Partitioning and Long-Term Hydrogen Storage Adjustment. Transactions of China Electrotechnical Society, 2026, 41(14): 4988-5002.

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[1] 国家发展改革委, 国家能源局. 氢能产业发展中长期规划(2021-2035年)[EB/OL]. (2022-03-24)2022. [2024-04-16]. https://www.gov.cn/xinwen/2022-03/24/5680975/files/6b388f7c324a4b1db0b30dc6f52b7e02.pdf.
[2] 潘光胜, 顾伟, 张会岩, 等. 面向高比例可再生能源消纳的电氢能源系统[J]. 电力系统自动化, 2020, 44(23): 1-10.
Pan Guangsheng, Gu Wei, Zhang Huiyan, et al.Electricity and hydrogen energy system towards accomodation of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2020, 44(23): 1-10.
[3] 洪芦诚, 王梓萩, 林今, 等. 电-碳-绿证市场背景下电氢协同典型形态及参与模式研究综述[J]. 电工技术学报, 2025, 40(23): 7498-7514.
Hong Lucheng, Wang Ziqiu, Lin Jin, et al.A review of typical forms and participation models of electricity-hydrogen synergy in the context of electricity-carbon-green certificate markets[J]. Tran-sactions of China Electrotechnical Society, 2025, 40(23): 7498-7514.
[4] 李志伟, 赵雨泽, 吴培, 等. 基于㶲经济分析的电氢能源系统区间鲁棒优化调度[J]. 电工技术学报, 2025, 40(11): 3514-3528.
Li Zhiwei, Zhao Yuze, Wu Pei, et al.Interval robust optimal scheduling of electricity and hydrogen energy system based on exergoeconomic analysis[J]. Transactions of China Electrotechnical Society, 2025, 40(11): 3514-3528.
[5] 王士博, 孔令国, 蔡国伟, 等. 电力系统氢储能关键应用技术现状、挑战及展望[J]. 中国电机工程学报, 2023, 43(17): 6660-6681.
Wang Shibo, Kong Lingguo, Cai Guowei, et al.Current status, challenges and prospects of key application technologies for hydrogen storage in power system[J]. Proceedings of the CSEE, 2023, 43(17): 6660-6681.
[6] 蒙军, 任洲洋, 王皓. 氢能交互下的多区域电氢综合能源系统可靠性提升策略[J]. 电工技术学报, 2024, 39(16): 5011-5027.
Meng Jun, Ren Zhouyang, Wang Hao.Reliability improvement strategies of multi-region electricity-hydrogen integrated energy systems considering hydrogen interaction between different regions[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5011-5027.
[7] Chai Yuanyuan, Guo Li, Wang Chengshan, et al.Network partition and voltage coordination control for distribution networks with high penetration of distributed PV units[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3396-3407.
[8] 毕锐, 刘先放, 丁明, 等. 以提高消纳能力为目标的可再生能源发电集群划分方法[J]. 中国电机工程学报, 2019, 39(22): 6583-6592.
Bi Rui, Liu Xianfang, Ding Ming, et al.Renewable energy generation cluster partition method aiming at improving accommodation capacity[J]. Proceedings of the CSEE, 2019, 39(22): 6583-6592.
[9] Cao Xiaoyu, Sun Xunhang, Xu Zhanbo, et al.Hydrogen-based networked microgrids planning through two-stage stochastic programming with mixed-integer conic recourse[J]. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 3672-3685.
[10] Diaz I U, de Queiróz Lamas W, Lotero R C. Development of an optimization model for the feasibility analysis of hydrogen application as energy storage system in microgrids[J]. International Journal of Hydrogen Energy, 2023, 48(43): 16159-16175.
[11] Sun Jing, Peng Yonggang, Xiong Jia.A photovoltaic-assisted in situ hydrogen refueling station system and its capacity optimization method[C]//2023 5th Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, 2023: 1540-1545.
[12] Barhoumi E M, Okonkwo P C, Zghaibeh M, et al.Optimization of PV-grid connected system based hydrogen refueling station[C]//2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey, 2022: 1603-1607.
[13] Toghyani S, Baniasadi E, Afshari E.Performance assessment of an electrochemical hydrogen production and storage system for solar hydrogen refueling station[J]. International Journal of Hydrogen Energy, 2021, 46(47): 24271-24285.
[14] Tabandeh A, Hossain M J, Khalilpour K.A planning framework for integration of distribution systems with grid-connected hydrogen refuelling stations[C]//2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), Melbourne, Australia, 2023: 1-6.
[15] 董雷, 杨子民, 乔骥, 等. 基于分层约束强化学习的综合能源多微网系统优化调度[J]. 电工技术学报, 2024, 39(5): 1436-1453.
Dong Lei, Yang Zimin, Qiao Ji, et al.Optimal scheduling of integrated energy multi-microgrid system based on hierarchical constraint reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1436-1453.
[16] 李鹏, 钟瀚明, 马红伟, 等. 基于深度强化学习的有源配电网多时间尺度源荷储协同优化调控[J]. 电工技术学报, 2025, 40(5): 1487-1502.
Li Peng, Zhong Hanming, Ma Hongwei, et al.Multi-timescale optimal dispatch of source-load-storage coordination in active distribution network based on deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2025, 40(5): 1487-1502.
[17] 袁铁江, 计力, 田雪沁, 等. 考虑燃料电池汽车加氢负荷的电-氢系统协同优化运行[J]. 电力系统自动化, 2023, 47(5): 16-25.
Yuan Tiejiang, Ji Li, Tian Xueqin, et al.Synergistic optimal operation of electricity-hydrogen systems considering hydrogen refueling loads for fuel cell vehicles[J]. Automation of Electric Power Systems, 2023, 47(5): 16-25.
[18] 陈威, 王永恒, 沈欣炜, 等. 计及碳排放流的光储充一体化电站及加氢站协同规划[J]. 电力系统自动化, 2024, 48(13): 40-49.
Chen Wei, Wang Yongheng, Shen Xinwei, et al.Synergistic planning of photovoltaic-storage-charging stations and hydrogen refueling stations considering carbon emission flows[J]. Automation of Electric Power Systems, 2024, 48(13): 40-49.
[19] 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.
[20] Ge Leijiao, Li Yuanzheng.Coupled multi-network constrained planning of energy supplying facilities for hybrid hydrogen-electric vehicles[M]//Smart Power Distribution Network: Situation Awareness, Planning, and Operation. Singapore: Springer Nature Singapore, 2023: 85-114.
[21] 李奇, 赵淑丹, 蒲雨辰, 等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报, 2021, 36(3): 486-495.
Li Qi, Zhao Shudan, Pu Yuchen, et al.Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 486-495.
[22] 金昱烨, 方家琨, 艾小猛, 等. 含季节性氢储能的电力系统跨尺度全年时序生产模拟方法[J]. 电力系统自动化, 2025,49(14): 120-129.
Jin Yuye, Fang Jiakun, Ai Xiaomeng, et al.Cross-time-scale annual chronological production simulation method for power system with seasonal hydrogen energy storage[J]. Automation of Electric Power Systems, 2025, 49(14): 120-129.
[23] Petkov I, Gabrielli P.Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems[J]. Applied Energy, 2020, 274: 115197.
[24] Pan Guangsheng, Gu Wei, Lu Yuping, et al.Optimal planning for electricity-hydrogen integrated energy system considering power to hydrogen and heat and seasonal storage[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2662-2676.
[25] Li P,Li Y,Malik O.International journal of electrical power with energy systems[C]// IEEE International Symposium on Industrial Electronics, 2014: 749-759.
[26] 丁明, 刘先放, 毕锐, 等. 采用综合性能指标的高渗透率分布式电源集群划分方法[J]. 电力系统自动化, 2018, 42(15): 47-52, 141.
Ding Ming, Liu Xianfang, Bi Rui, et al.Method for cluster partition of high-penetration distributed generators based on comprehensive performance index[J]. Automation of Electric Power Systems, 2018, 42(15): 47-52, 141.
[27] 魏震波. 复杂网络社区结构及其在电网分析中的应用研究综述[J]. 中国电机工程学报, 2015, 35(7): 1567-1577.
Wei Zhenbo.Overview of complex networks community structure and its applications in electric power network analysis[J]. Proceedings of the CSEE, 2015, 35(7): 1567-1577.
[28] 高红均, 刘俊勇, 沈晓东, 等. 主动配电网最优潮流研究及其应用实例[J]. 中国电机工程学报, 2017, 37(6): 1634-1645.
Gao Hongjun, Liu Junyong, Shen Xiaodong, et al.Optimal power flow research in active distribution network and its application examples[J]. Proceedings of the CSEE, 2017, 37(6): 1634-1645.
[29] 李子晨, 夏杨红, 孙勇, 等. 考虑氢能长短周期储能特性的电氢综合能源系统容量配置方法[J]. 电网技术, 2025, 49(1): 12-21.
Li Zichen, Xia Yanghong, Sun Yong, et al.Optimal sizing of electricity-hydrogen integrated energy system considering multi-timescale operation of hydrogen storage system[J]. Power System Technology, 2025, 49(1): 12-21.
[30] 孔令国, 王士博, 蔡国伟, 等. 零能耗建筑电-氢-热双层能量优化调控方法[J]. 中国电机工程学报, 2022, 42(17): 6196-6207.
Kong Lingguo, Wang Shibo, Cai Guowei, et al.Zero energy building electricity-hydrogen-heat double-layer energy optimization control method[J]. Proceedings of the CSEE, 2022, 42(17): 6196-6207.
[31] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8214.
Zuo Fengyuan, Zhang Yuqiong, Zhao Qiang, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8214.