考虑风光发电与储氢系统耦合的日前运行优化调度模型

马瑞; 郝晓光; 金飞; 李剑锋; 杜天硕

doi:10.19725/j.cnki.1007-2322.2023.0322

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

考虑风光发电与储氢系统耦合的日前运行优化调度模型

能源互联网 | 更新时间：2025-12-11

- 考虑风光发电与储氢系统耦合的日前运行优化调度模型
- An Optimal Dispatching Model for Day-ahead Operation Considering Coupling of Wind and Photovoltaic Power Generation and Hydrogen Storage System
- 现代电力 2025年第6期页码：1339-1348
- 作者机构：
  
  1. 国网河北省电力有限公司电力科学研究院,河北省,石家庄市,050031
  2. 天津大学电气自动化与信息工程学院, 天津市南开区,300072
- 作者简介：
  
  [ "马瑞(1980)，男，通信作者，硕士，教授级高级工程师，主要研究方向为新能源调频能力评估，E-mail：250440485@qq.com" ]
- 基金信息：
  
  国网河北省电力有限公司科技项目(kj2022-025)。
- DOI：10.19725/j.cnki.1007-2322.2023.0322
  中图分类号：
- 纸质出版：2025
- 稿件说明：
移动端阅览
马瑞, 郝晓光, 金飞, 等. 考虑风光发电与储氢系统耦合的日前运行优化调度模型[J]. 现代电力, 2025,(6):1339-1348.

MA Rui, HAO Xiaoguang, JIN Fei, et al. An Optimal Dispatching Model for Day-ahead Operation Considering Coupling of Wind and Photovoltaic Power Generation and Hydrogen Storage System[J]. 2025, (6): 1339-1348.
马瑞, 郝晓光, 金飞, 等. 考虑风光发电与储氢系统耦合的日前运行优化调度模型[J]. 现代电力, 2025,(6):1339-1348. DOI： 10.19725/j.cnki.1007-2322.2023.0322.

MA Rui, HAO Xiaoguang, JIN Fei, et al. An Optimal Dispatching Model for Day-ahead Operation Considering Coupling of Wind and Photovoltaic Power Generation and Hydrogen Storage System[J]. 2025, (6): 1339-1348. DOI： 10.19725/j.cnki.1007-2322.2023.0322.

摘要

风光等可再生能源发电存在的随机性与间歇性问题将直接影响电网的安全稳定运行，采用风光氢储互补系统并结合系统日前调度的方法可以有效解决风光发电波动等问题。为此，以负荷端氢气供应需求为核心，联合考虑电化学储能和氢储能技术，建立了风光互补氢储能系统的运行优化模型，采用反向混沌麻雀优化算法求解，并将结果与传统优化算法进行比较。结果表明：采用改进算法求解的日前运行方案可全天节省约10%的系统运行成本。计算实例的分析结果表明，建立的模型充分考虑了系统中设备的实际运行特点，在分时电价机制下，通过调整从电网购买的电量和蓄电池的充放电功率，可以有效减少弃风弃光。而且通过优化系统的日前调度，在确保制氢电力满足氢气需求的同时，实现了系统日常运行成本的最小化。

Abstract

The randomness and intermittence of renewable energy generation

such as wind and photovoltaic power generation

directly affect the safe and stable operation of the power grid system. The utilization of wind

photovoltaic

and solar hydrogen storage complementary system combined with system day-ahead scheduling can effectively address the issue of wind power generation fluctuation. Therefore

in this paper we focuse on the demand for hydrogen supply at the load end

and combine the electrochemical energy storage and hydrogen energy storage technologies to establish an operational optimization model for the wind-photovoltaic complementary hydrogen energy storage system. The reverse chaotic sparrow optimization algorithm is employed to solve the problem

and a comparison is made with traditional optimization algorithms. The results demonstrate that the day-ahead operation scheme solved by the improved algorithm yields about 28% savings in the system operation cost throughout the day. The analysis results of calculation examples indicate that the established model comprehensively takes into account the actual operation characteristics of the equipment in the system. Under the time-sharing electricity price mechanism

the abandoned wind and photovoltaic can be effectively reduced by adjusting the amount of electricity purchased from the power grid and the charging and discharging power of the battery. The daily operating cost of the system is minimized by optimizing the day-ahead scheduling of the system

while ensuring that the hydrogen production power meets the hydrogen demand.

关键词

Keywords

references

CHEN Zexi, SUN Yushu, ZHANG Yan, et al. Research on optimal configuration of energy storage considering wind-solar complementarity[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 145−153(in Chinese).

陈泽西, 孙玉树, 张妍, 等. 考虑风光互补的储能优化配置研究[J]. 电工技术学报, 2021, 36(S1): 145−153.

WANG Chengshan, WU Zhen, LI Peng. Application prospects and challenges of distributed electric energy storage technology[J]. Automation of Electric Power Systems, 2014, 38(16): 1−8+73(in Chinese).

王成山, 武震, 李鹏. 分布式电能存储技术的应用前景与挑战[J]. 电力系统自动化, 2014, 38(16): 1−8+73.

JI Yang, AI Qian, XIE Da. The coordinated development trend of distributed generation technology and smart grid technology[J]. Power System Technology, 2010, 34(12): 15−23(in Chinese).

季阳, 艾芊, 解大. 分布式发电技术与智能电网技术的协同发展趋势[J]. 电网技术, 2010, 34(12): 15−23.

YAN Qunmin, MU Jiahao, MA Yongxiang, et al. Summary of distributed energy storage application mode and optimal configuration[J]. Electric Power Engineering Technology, 2022, 41(02): 67−74(in Chinese).

闫群民, 穆佳豪, 马永翔, 等. 分布式储能应用模式及优化配置综述[J]. 电力工程技术, 2022, 541(02): 67−74.

YANG Zilong, SONG Zhenhao, PAN Jing, et al. Multi-mode coordinated control strategy for distributed photovoltaic/energy storage system[J]. Proceedings of the CSEE, 2019, 39(08): 2213−2220(in Chinese).

杨子龙, 宋振浩, 潘静, 等. 分布式光伏/储能系统多运行模式协调控制策略[J]. 中国电机工程学报, 2019, 39(08): 2213−2220.

HE Xiaoliang, ZHANG Leiqi, TAN Haiyun, et al. Coordinated control strategy of distributed power supply and multi-energy storage in microgrid[J]. Journal of Power Supply, 2016, 14(02): 103−111(in Chinese).

何小良, 章雷其, 谭海云, 等. 微网中分布式电源和多储能的协调控制策略[J]. 电源学报, 2016, 14(02): 103−111.

HONG Z P, WEI Z X, HAN X J. Optimization scheduling control strategy of wind-hydrogen system considering hydrogen production efficiency[J]. Journal of Energy Storage, 2022, 47(103609).

XU Chuanbo, ZHAO Yunhao, WANG Xiaochen, et al. Optimal configuration of hydrogen storage in wind and solar power plants considering electric hydrogen coupling under the carbon neutrality vision[J]. Electric Power Construction, 2022, 43(01): 10−18(in Chinese).

许传博, 赵云灏, 王晓晨, 等. 碳中和愿景下考虑电氢耦合的风光场站氢储能优化配置[J]. 电力建设, 2022, 43(01): 10−18.

KONG Lingguo, WANG Jiaqi, HAN Zijiao, et al. Online power regulation of wind light storage hydrogen coupling system based on weight adjustment model predictive control[J]. Transactions of China Electrotechnical Society, 2023, 38(15): 4192−4207(in Chinese).

孔令国, 王嘉祺, 韩子娇, 等. 基于权重调节模型预测控制的风–光–储–氢耦合系统在线功率调控[J]. 电工技术学报, 2023, 38(15): 4192−4207.

LI Zheng, LUO Xiaorui, XU Ruosi, et al. Multi-objective capacity optimization configuration of wind hydrogen storage microgrid system[J]. Journal of Engineering for Thermal Energy and Power, 2023, 38(04): 131−138(in Chinese).

李争, 罗晓瑞, 徐若思, 等. 风光–氢储微电网系统多目标容量优化配置[J]. 热能动力工程, 2023, 38(04): 131−138.

PENG Shengjiang, YANG Dezhou, SUN Chuanshuai, et al. Capacity planning of hydrogen energy systems based on hydrogen load demand[J]. Electric Power, 2023, 56(07): 13−20+32(in Chinese).

彭生江, 杨德州, 孙传帅, 等. 基于氢负荷需求的氢能系统容量规划[J]. 中国电力, 2023, 56(07): 13−20+32.

JIA Chengzhen, WANG Lingmei, MENG Enlong, et al. Capacity optimization configuration and forward optimization scheduling of wind hydro coupled power generation systems[J]. Electric Power, 2020, 53(10): 80−87(in Chinese).

贾成真, 王灵梅, 孟恩隆, 等. 风光氢耦合发电系统的容量优化配置及日前优化调度[J]. 中国电力, 2020, 53(10): 80−87.

DING Suyang, LIN Xiangning, CHEN Zhe, et al. Study on the design and scheduling strategy of storage-hydrogen-cooling combined renewable energy consumption system on resource enrichment island[J]. Proceedings of the CSEE, 2019, 39(16): 4659−4673(in Chinese).

丁苏阳, 林湘宁, 陈哲, 等. 资源富集岛蓄–氢–冷联合可再生能源消纳系统设计及调度策略研究[J]. 中国电机工程学报, 2019, 39(16): 4659−4673.

GAO Cong, WU Dinghui, PAN Tinglong, et al. Economic operation optimization of microgrid based on immune particle swarm optimization[J]. Journal of System Simulation, 2018, 30(02): 636−646(in Chinese).

高聪, 吴定会, 潘庭龙, 等. 基于免疫粒子群算法的微电网经济运行优化[J]. 系统仿真学报, 2018, 30(02): 636−646.

SHANG Qingxiao, SUN Ming. Research on optimal dispatching of power grid based on wind power and storage (heat) heating system[J]. Journal of Solar Energy, 2021, 42(07): 65−70(in Chinese).

尚庆晓, 孙鸣. 基于风电、储(热)供暖系统的电网优化调度研究[J]. 太阳能学报, 2021, 42(07): 65−70.

WANG Luhao, LI Qiqiang, WANG Guirong, et al. Environmental and economic optimal dispatching of multi-renewable energy cooling, heating and power supply microgrid system[J]. Control and Decision, 2016, 31(05): 913−918(in Chinese).

王鲁浩, 李歧强, 王桂荣, 等. 多可再生能源冷热电联供微网系统环境经济优化调度[J]. 控制与决策, 2016, 31(05): 913−918.

SHAO Zhifang, WU Jilan. Optimization of capacity allocation for hydrogen production based on dynamic electricity price[J]. Journal of Solar Energy, 2020, 41(08): 227−235(in Chinese).

邵志芳, 吴继兰. 基于动态电价风光电制氢容量配置优化[J]. 太阳能学报, 2020, 41(08): 227−235.

CHEN Chenpeng, ZHAO Xin, BI Guihong, et al. Short term wind speed prediction model based on multi-mode decomposition and sparrow optimized residual network[J]. Power System Technology, 2022, 46(08): 2975−2985(in Chinese).

陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期速预测模型[J]. 电网技术, 2022, 46(08): 2975−2985.

LOU Ao, YAO Minli, JIA Weimin, et al. Tent chaos and GSA for local search optimization in variable neighborhood[J]. Journal of Xidian University, 2019, 46(05): 120−127(in Chinese).

娄奥, 姚敏立, 贾维敏, 等. Tent混沌和变邻域局部搜索优化的GSA[J]. 西安电子科技大学学报, 2019, 46(05): 120−127.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

考虑综合需求响应和阶梯型碳交易的综合能源系统协调优化调度

耦合氢储能的综合能源园区系统容量配置与运行优化