考虑储能与多类型制氢装置协同的虚拟电厂优化调度

doi:10.11930/j.issn.1004-9649.202507028

中国电力 ›› 2026, Vol. 59 ›› Issue (2): 37-46.DOI: 10.11930/j.issn.1004-9649.202507028

• “十五五”电力系统源网荷储协同规划运行关键技术 • 上一篇下一篇

考虑储能与多类型制氢装置协同的虚拟电厂优化调度

黄松涛¹(), 赵雪楠¹, 尚国政¹, 赵鹏宇¹, 董文静¹, 张亚健²(), 杨毅²

1. 国网内蒙古东部电力有限公司，内蒙古呼和浩特　010020?
2. 上海大学机电工程与自动化学院，上海　200444

收稿日期:2025-07-09 修回日期:2026-01-10 发布日期:2026-03-04 出版日期:2026-02-28
作者简介:
黄松涛（1981），男，副高级工程师，从事电力调度运行管理研究，E-mail：mddldkzx@163.com
张亚健（1991），男，通信作者，副教授，从事电氢耦合系统协同优化运行研究，E-mail：zhang_ya_jian@shu.edu.cn
基金资助:
上海市自然科学基金资助项目（25ZR1402170）；国网蒙东电力科技项目（52660024000G）。

Optimization scheduling of virtual power plants with collaborations of energy storage devices and multi-type power-to-hydrogen units

HUANG Songtao¹(), ZHAO Xuenan¹, SHANG Guozheng¹, ZHAO Pengyu¹, DONG Wenjing¹, ZHANG Yajian²(), YANG Yi²

1. State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Hohhot 010020, China
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received:2025-07-09 Revised:2026-01-10 Online:2026-03-04 Published:2026-02-28
Supported by:
This work is supported by Natural Science Foundation of Shanghai (No.25ZR1402170); Science and Technology Project of State Grid East Inner Mongolia Electric Power Supply Co., Ltd. (No.52660024000G).

摘要/Abstract

摘要：

为应对风光出力波动性对虚拟电厂调度及可再生能源消纳的挑战，提出一种储能与多类型电制氢装置协同的优化调度方法。首先，通过经验模态分解将风光出力分解为低、中、高频分量，匹配碱性电解槽、质子交换膜电解槽及储能系统的特性以实现分频协同消纳。其次，考虑电解槽功率约束、储能荷电状态约束及能量平衡等约束条件，构建以配置成本与运行成本最小化为目标的优化模型。最后，设计三阶段求解算法用于制定最优调度方案。仿真结果表明，与单类型电解槽协同调度策略或储能-单类型电解槽协同调度策略相比，所提策略可将弃电率降至0.14%，显著提升风光消纳效率与系统经济性。

关键词: 虚拟电厂, 优化调度, 经验模态分解, 电制氢, 协同优化

Abstract:

To address the challenges posed by the volatility of wind and solar power output on virtual power plant scheduling and renewable energy consumption, a coordinated optimization scheduling method for energy storage and multi-type power-to-hydrogen (P2H) facilities is proposed. Firstly, the output of wind and solar power is decomposed into low-, medium-, and high-frequency components using empirical mode decomposition, which are then matched with the respective characteristics of alkaline electrolyzer, proton exchange membrane electrolyzer, and energy storage systems to achieve frequency-division and collaborative consumption of renewable energy. Secondly, an optimization model is constructed to minimize the configuration and operation costs, considering such constraints as electrolyzing powers, state-of-charge of energy storage devices, and power balances. Finally, a three-stage algorithm is designed to determine the optimal scheduling scheme. Simulation results demonstrate that, compared to the collaborative dispatch strategy utilizing a single-type electrolyzer or the strategy combining energy storage with a single-type electrolyzer, the proposed method reduces the curtailment rate to 0.14%, significantly enhancing the consumption efficiency of renewable energy and improving system economics.

Key words: virtual power plant, optimization scheduling, empirical mode decomposition, power-to-hydrogen, collaborative optimization

黄松涛, 赵雪楠, 尚国政, 赵鹏宇, 董文静, 张亚健, 杨毅. 考虑储能与多类型制氢装置协同的虚拟电厂优化调度[J]. 中国电力, 2026, 59(2): 37-46.

HUANG Songtao, ZHAO Xuenan, SHANG Guozheng, ZHAO Pengyu, DONG Wenjing, ZHANG Yajian, YANG Yi. Optimization scheduling of virtual power plants with collaborations of energy storage devices and multi-type power-to-hydrogen units[J]. Electric Power, 2026, 59(2): 37-46.

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图/表 8

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参数	数值	参数	数值
储能容量投资成本/ (元·(kW·h)^–1)	2000	单位水耗成本/(元·吨^–1)	5
单位用电成本/ (元·(kW·h)^–1)	0.3	ALK电解槽投资成本/(元·kW^–1)	1200
氢气售价/(元·kg^–1)	40	PEM电解槽投资成本/ (元·kW^–1)	6000

参数	数值	参数	数值
储能容量投资成本/ (元·(kW·h)^–1)	2000	单位水耗成本/(元·吨^–1)	5
单位用电成本/ (元·(kW·h)^–1)	0.3	ALK电解槽投资成本/(元·kW^–1)	1200
氢气售价/(元·kg^–1)	40	PEM电解槽投资成本/ (元·kW^–1)	6000

场景	ESS储能容量/(kW·h)	ALK电解槽功率/kW	PEM电解槽功率/kW	投资成本/ 元	运维成本/ 元	水、电成本/ 元	弃电成本/ 元	综合成本/ 元	售氢收益/ 元	利润/ 元	弃电率/ %
1	134.41	935.46	332.69	744.7	34.2	8026.3	11.9	8817.1	19051.8	10234.7	0.14
2	—	1207.56	—	318.6	12.7	7786.3	263.1	8380.7	18483.9	10103.2	3.14
3	—	—	1219.89	1609.1	80.5	8009.8	31.5	9730.9	19008.1	9637.2	0.39
4	134.41	1239.34	—	373.0	14.9	7895.1	142.1	8425.1	18542.1	10117.0	1.75
5	134.41	—	1202.90	1645.8	81.7	8004.2	35.9	9767.6	18994.9	9227.3	0.44

场景	ESS储能容量/(kW·h)	ALK电解槽功率/kW	PEM电解槽功率/kW	投资成本/ 元	运维成本/ 元	水、电成本/ 元	弃电成本/ 元	综合成本/ 元	售氢收益/ 元	利润/ 元	弃电率/ %
1	134.41	935.46	332.69	744.7	34.2	8026.3	11.9	8817.1	19051.8	10234.7	0.14
2	—	1207.56	—	318.6	12.7	7786.3	263.1	8380.7	18483.9	10103.2	3.14
3	—	—	1219.89	1609.1	80.5	8009.8	31.5	9730.9	19008.1	9637.2	0.39
4	134.41	1239.34	—	373.0	14.9	7895.1	142.1	8425.1	18542.1	10117.0	1.75
5	134.41	—	1202.90	1645.8	81.7	8004.2	35.9	9767.6	18994.9	9227.3	0.44

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考虑储能与多类型制氢装置协同的虚拟电厂优化调度

Optimization scheduling of virtual power plants with collaborations of energy storage devices and multi-type power-to-hydrogen units

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考虑储能与多类型制氢装置协同的虚拟电厂优化调度

Optimization scheduling of virtual power plants with collaborations of energy storage devices and multi-type power-to-hydrogen units

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