A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation

BAO Hongyan; LIU Jicheng; LIU Ziyi; SUN Jiakang

doi:10.11930/j.issn.1004-9649.202507043

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A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation

更新时间：2025-12-08

- A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation
- Vol. 58, Issue 10, Pages: 27-38(2025)
- 作者机构：
  
  华北电力大学经济与管理学院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202507043
  CLC：
- Published Online：23 October 2025，
  
  Published：2025
- 稿件说明：
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鲍红焉, 刘吉成, 刘子毅, 孙嘉康. 考虑电氢混合储能容量配置的主动配电网双层优化模型[J]. 中国电力, 2025, 58(10): 27-38.

BAO Hongyan, LIU Jicheng, LIU Ziyi, et al. A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation[J]. 2025, 58(10): 27-38.
鲍红焉, 刘吉成, 刘子毅, 孙嘉康. 考虑电氢混合储能容量配置的主动配电网双层优化模型[J]. 中国电力, 2025, 58(10): 27-38. DOI： 10.11930/j.issn.1004-9649.202507043.

BAO Hongyan, LIU Jicheng, LIU Ziyi, et al. A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation[J]. 2025, 58(10): 27-38. DOI： 10.11930/j.issn.1004-9649.202507043.

摘要

在主动配电网中配置电氢混合储能，对于增强用电经济性、促进可再生能源消纳、降低电压波动、保障供需平衡与系统稳定至关重要。首先，设计了含电氢混合储能的主动配电网运行框架，构建配电网域的电氢混合储能模型。然后，以综合用电成本最小为目标构建上层优化模型，以弃风弃光率、电压波动率、电网购电波动以及总负荷波动最小为目标建立下层优化模型。最后，以改进IEEE 33节点配电网为例验证模型的有效性。结果表明，所提模型能够实现电氢混合储能容量配置以及主动配电网运行调度优化。相较于不配置储能或配置单一介质储能，电氢混合储能更具经济可行性，并且增强了主动配电网的自我调节能力。分时电价与季节特性共同驱动电氢混合储能的协同运行，形成“电化学快速响应+氢储能长时调节”的协同调度模式。

Abstract

The configuration of electricity-hydrogen hybrid energy storage in active distribution networks is of vital importance for enhancing the economic efficiency of electricity usage

promoting the consumption of renewable energy

reducing voltage fluctuations

and ensuring the balance between supply and demand as well as system stability. Firstly

an active distribution network operation framework containing electricity-hydrogen hybrid energy storage system is designed

and an electric-hydrogen hybrid energy storage model in the distribution network domain is constructed. Then

an upper-level optimization model is constructed to minimize the comprehensive electricity cost

and a lower-level optimization model is established with the goals of minimizing the wind and solar curtailment rate

voltage fluctuation rate

grid purchase fluctuation

and the total load fluctuation. Finally

a modified IEEE 33-node distribution network is used as a case study to validate the effectiveness of the proposed model. The results demonstrate that the model can achieve optimal capacity configuration for electricity-hydrogen hybrid energy storage system

and optimal dispatch of the active distribution networks. Compared to scenarios without energy storage or with single-medium energy storage

the hybrid electricity-hydrogen system proves to be more economically viable and enhances the self-regulating capability of the active distribution networks. Time-of-use electricity prices and seasonal characteristics collectively drive the coordinated operation of the electricity-hydrogen hybrid energy storage system

forming a synergistic dispatching mode characterized by "electrochemical storage providing rapid response + hydrogen storage enabling long-duration regulation."

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references

1徐文韬, 米阳, 蔡鹏程, 等. 基于机会约束规划的含多微网主动配电网分布式优化管理[J]. 电力建设, 2024, 45 (11): 1- 13.

XU Wentao, MI Yang, CAI Pengcheng, et al. Distributed optimization management for active distribution networks with multiple microgrids based on chance-constrained programming[J]. Electric Power Construction, 2024, 45 (11): 1- 13.

2YANG S X, WANG X F, XU J Y, et al. Distribution network adaptability assessment considering distributed PV "reverse power flow" behavior - a case study in Beijing[J]. Energy, 2023, 275, 127497. DOI

3孙国强, 殷岩岩, 卫志农, 等. 基于深度确定性策略梯度的主动配电网有功-无功协调优化调度[J]. 电力建设, 2023, 44 (11): 33- 42.

SUN Guoqiang, YIN Yanyan, WEI Zhinong, et al. Coordinated optimal dispatch of active and reactive power in active distribution networks using deep deterministic strategy gradient[J]. Electric Power Construction, 2023, 44 (11): 33- 42.

4郭文凯, 王果, 闵永智. 计及阶梯式碳交易的牵引供电系统混合储能容量配置[J]. 西南交通大学学报, 2025, 60 (3): 550- 560.

GUO Wenkai, WANG Guo, MIN Yongzhi. Hybrid energy storage capacity configuration for traction power supply systems considering ladder-type carbon trading mechanism[J]. Journal of Southwest Jiaotong University, 2025, 60 (3): 550- 560.

5刘秋华, 王明康, 杨圣城. 基于全寿命周期内经济收益最大化的退役电池储能容量配置方法研究[J]. 太阳能学报, 2024, 45 (7): 143- 152.

LIU Qiuhua, WANG Mingkang, YANG Shengcheng. Research on configuration method of energy storage capacity for retired batteries based on maximizing economic benefits throughout life cycle[J]. Acta Energiae Solaris Sinica, 2024, 45 (7): 143- 152.

6毛志宇, 李培强, 马德鑫, 等. 基于风功率波动平抑的复合储能两次功率分配容量配置方法研究[J]. 电网技术, 2023, 47 (10): 4111- 4123.

MAO Zhiyu, LI Peiqiang, MA Dexin, et al. Double power allocation of capacity configuration of compound energy storage based on wind power smoothing[J]. Power System Technology, 2023, 47 (10): 4111- 4123.

7米阳, 王沛林, 周杰, 等. 基于电力系统分区惯量估计的储能容量配置策略[J]. 电力自动化设备, 2024, 44 (7): 13- 20.

MI Yang, WANG Peilin, ZHOU Jie, et al. Energy storage capacity configuration strategy based on partitioned inertia estimation of power system[J]. Electric Power Automation Equipment, 2024, 44 (7): 13- 20.

8张震霄, 年珩, 李培, 等. 储能型统一电能质量调节器电压补偿能力分析及提升方法[J]. 电力系统自动化, 2022, 46 (23): 151- 159.

ZHANG Zhenxiao, NIAN Heng, LI Pei, et al. Voltage compensation capability analysis and improvement method for unified power quality conditioner with energy storage[J]. Automation of Electric Power Systems, 2022, 46 (23): 151- 159.

9武晓朦, 孙安磊, 李晨晨, 等. 考虑风电出力波动性的混合储能双层优化配置[J]. 科学技术与工程, 2024, 24 (24): 10313- 10320.

WU Xiaomeng, SUN Anlei, LI Chenchen, et al. Hybrid energy storage double-layer optimal configuration considering wind power output volatility[J]. Science Technology and Engineering, 2024, 24 (24): 10313- 10320.

10李金中, 徐斌, 李喆, 等. 基于COA算法和PSO-GSA算法的储能优化与调控方法[J]. 供用电, 2025, 42 (6): 40- 47.

LI Jinzhong, XU Bin, LI Zhe, et al. Energy storage optimization and regulation and control methods based on COA and PSO-GSA algorithms[J]. Distribution & Utilization, 2025, 42 (6): 40- 47.

11DUAN F D, BU X Z. A new cloud-stochastic framework for optimized deployment of hydrogen storage in distribution network integrated with renewable energy considering hydrogen-based demand response[J]. Energy, 2025, 316, 134483. DOI

12方石磊, 杨志淳, 闵怀东, 等. 高比例光伏下考虑移动储能接入的柔性配电网协调优化策略[J]. 供用电, 2025, 42 (5): 61- 71.

FANG Shilei, YANG Zhichun, MIN Huaidong, et al. Coordinated optimization strategy of flexible distribution network considering mobile energy storage system integration under high proportion photovoltaic[J]. Distribution & Utilization, 2025, 42 (5): 61- 71.

13袁铁江, 郭建华, 杨紫娟, 等. 平抑风电波动的电-氢混合储能容量优化配置[J]. 中国电机工程学报, 2024, 44 (4): 1397- 1406.

YUAN Tiejiang, GUO Jianhua, YANG Zijuan, et al. Optimal allocation of power electric-hydrogen hybrid energy storage of stabilizing wind power fluctuation[J]. Proceedings of the CSEE, 2024, 44 (4): 1397- 1406.

14卢子敬, 李子寿, 郭相国, 等. 基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置[J]. 中国电力, 2023, 56 (7): 33- 42.

LU Zijing, LI Zishou, GUO Xiangguo, et al. Optimal configuration of electricity-hydrogen hybrid energy storage system based on multi-objective artificial hummingbird algorithm[J]. Electric Power, 2023, 56 (7): 33- 42.

15陈铭宏天, 耿江海, 赵雨泽, 等. 基于两阶段随机优化的电氢耦合微电网周运行策略[J]. 中国电力, 2025, 58 (5): 82- 90.

CHEN Minghongtian, GENG Jianghai, ZHAO Yuze, et al. Two-stage stochastic optimization based weekly operation strategy for electric-hydrogen coupled microgrid[J]. Electric Power, 2025, 58 (5): 82- 90.

16胡伟, 杨硕. 考虑配电网脆弱性的储能双层优化配置模型[J]. 智慧电力, 2024, 52 (6): 1- 8.

HU Wei, YANG Shuo. Dual-tiered optimization configuration model for energy storage considering distribution network vulnerability[J]. Smart Power, 2024, 52 (6): 1- 8.

17李湃, 卢慧, 李驰, 等. 多能互补发电系统电/热储能容量双层优化配置方法[J]. 中国电力, 2025, 58 (3): 55- 64.

LI Pai, LU Hui, LI Chi, et al. Bi-level capacity optimization for battery/thermal energy storage system in multi-energy complementary power generation system[J]. Electric Power, 2025, 58 (3): 55- 64.

20刘忠, 黄彦铭, 朱光明, 等. 含风-光-电氢混合储能的多微电网系统容量优化配置方法[J]. 发电技术, 2025, 46 (2): 240- 251.

LIU Zhong, HUANG Yanming, ZHU Guangming, et al. Optimal capacity configuration method for multi-microgrid system utilizing wind-solar-electric-hydrogen hybrid energy storage[J]. Power Generation Technology, 2025, 46 (2): 240- 251.

21闫群民, 李召, 马永翔, 等. 考虑综合经济性的风光配电网储能优化配置[J]. 科学技术与工程, 2024, 24 (12): 5014- 5022.

YAN Qunmin, LI Zhao, MA Yongxiang, et al. Consider the optimal configuration of energy storage in wind-solar distribution networks with comprehensive economy[J]. Science Technology and Engineering, 2024, 24 (12): 5014- 5022.

22彭钢, 寇启龙, 方涛, 等. 考虑新能源消纳率的主动配电网储能优化配置策略[J]. 浙江电力, 2025, 44 (1): 84- 94.

PENG Gang, KOU Qilong, FANG Tao, et al. An optimal strategy for energy storage allocation in active distribution networks considering new energy consumption rates[J]. Zhejiang Electric Power, 2025, 44 (1): 84- 94.

23ZHANG T M, WANG X, PARISIO A. A corrective control framework for mitigating voltage fluctuations and congestion in distribution networks with high renewable energy penetration[J]. International Journal of Electrical Power & Energy Systems, 2025, 165, 110508.

24杜帅, 贾黎明, 李真娣, 等. “光伏-储能”耦合参与调峰的配电网运行优化配置[J]. 储能科学与技术, 2024, 13 (5): 1741- 1743.

DU Shuai, JIA Liming, LI Zhendi, et al. Optimization configuration of distribution network operation with "photovoltaic energy storage" coupling participation in peak shaving[J]. Energy Storage Science and Technology, 2024, 13 (5): 1741- 1743.

25李响, 武海潮, 王文雪, 等. 基于电网承载能力的新能源并网适应性评估[J]. 中国电机工程学报, 2023, 43 (S1): 107- 113.

LI Xiang, WU Haichao, WANG Wenxue, et al. Adaptability assessment of new energy connected to the power grid based on the carrying capacity[J]. Proceedings of the CSEE, 2023, 43 (S1): 107- 113.

26刘志文, 李岩, 邵冲, 等. 考虑柔性资源协同互动的配电网灵活性评估方法[J]. 中国电力, 2024, 57 (10): 158- 165.

LIU Zhiwen, LI Yan, SHAO Chong, et al. Distribution network flexibility evaluation method considering collaborative interaction of flexible resources[J]. Electric Power, 2024, 57 (10): 158- 165.

27郭久亿, 刘洋, 郭焱林, 等. 不同典型用户侧储能配置评估与运行优化模型[J]. 电网技术, 2020, 44 (11): 4245- 4254.

GUO Jiuyi, LIU Yang, GUO Yanlin, et al. Configuration evaluation and operation optimization model of energy storage in different typical user-side[J]. Power System Technology, 2020, 44 (11): 4245- 4254.

28李明, 尹文良, 李勇康, 等. 多源耦合不确定性下含电氢储能的微电网低碳容量优化配置研究[J]. 储能科学与技术, 2025, 14 (5): 1969- 1981.

LI Ming, YIN Wenliang, LI Yongkang, et al. Low-carbon capacity optimal configuration of microgrid with hydrogen energy storage under multi-source coupling uncertainties[J]. Energy Storage Science and Technology, 2025, 14 (5): 1969- 1981.

31夏佳伟, 张一帆, 雷浩, 等. 考虑高效氢能利用和碳捕集的综合能源系统低碳优化调度[J]. 电力建设, 2024, 45 (12): 100- 111.

XIA Jiawei, ZHANG Yifan, LEI Hao, et al. Low-carbon economic dispatch of integrated energy system considering efficient hydrogen utilization and carbon capture equipment[J]. Electric Power Construction, 2024, 45 (12): 100- 111.

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