考虑风、光出力时空相关性的电-氢协同储能系统经济性优化调度研究

doi:10.3969/j.issn.2097-0706.2025.12.004

摘要/Abstract

摘要：

我国风能、太阳能资源富集地与负荷存在时空错配问题，利用同一区域内风能和太阳能之间的相关性与互补性，采用电-氢协同储能模式，是缓解可再生能源出力对电网不良影响的有效技术路径。使用非参数核密度估计法拟合风、光出力数据的概率分布规律，结合Copula理论生成计及风光时空相关性的时序场景；考虑风、光发电的相关性，进一步建立了电-氢协同储能综合能源系统的经济性日前优化调度模型，并使用自适应模拟退火粒子群优化（ASA-PSO）算法求解。仿真结果表明：相比于基本PSO算法，ASA-PSO算法具有更高的求解速度与精度；电-氢协同储能系统的日前经济优化调度方案节省了约19%的日运行成本，可以避免系统在电价高峰时段大量购入电力并实现了波动性新能源的就地消纳，可为友好型规模化电网的构建提供电-氢柔性匹配方法。

关键词: 电-氢耦合, 储能, 可再生能源, Copula理论, 核密度估计, 经济性, 优化调度, ASA-PSO算法

Abstract:

In China， there is a spatiotemporal mismatch between the areas rich in wind and solar energy resources and the load centers. By leveraging the correlation and complementarity between wind and solar energy within the same region， an electricity-hydrogen coordinated energy storage model was proposed， which was an effective technical approach to mitigate the adverse impact of renewable energy output on the power grid. The probability distribution patterns of wind and photovoltaic power output data were fitted using the nonparametric kernel density estimation method， and time-series scenarios considering the spatiotemporal correlation of wind and photovoltaic power were generated using Copula theory. By considering the correlation between wind and photovoltaic power generation， an economic day-ahead optimal scheduling model for the integrated electricity-hydrogen coordinated energy system was further established， and solved using the adaptive simulated annealing particle swarm optimization （ASA-PSO） algorithm. The simulation results showed that compared to the basic PSO algorithm， the ASA-PSO algorithm demonstrated superior solving speed and accuracy. The economic day-ahead optimal scheduling scheme for the electricity-hydrogen coordinated energy storage system reduced daily operating costs by approximately 19%， avoided large-scale electricity purchases during peak price periods， and enabled local consumption of fluctuating renewable energy. It provides a flexible electricity-hydrogen matching approach for constructing a grid-friendly and large-scale power system.

Key words: electricity-hydrogen coupling, energy storage, renewable energy, Copula theory, kernel density estimation, economic efficiency, optimal scheduling, ASA-PSO algorithm

中图分类号:

TK01：TK91

王芊瑞, 阮景昕, 王跃社. 考虑风、光出力时空相关性的电-氢协同储能系统经济性优化调度研究[J]. 综合智慧能源, 2025, 47(12): 34-45.

WANG Qianrui, RUAN Jingxin, WANG Yueshe. Economic optimal scheduling of electricity-hydrogen coordinated energy storage system considering spatiotemporal correlation of wind and photovoltaic power outputs[J]. Integrated Intelligent Energy, 2025, 47(12): 34-45.

图/表 26

表1

图1

图2

图3

表2

表3

常用核函数及其数学表达式

核函数	表达式
Uniform（或Box）	$12 I (u h ≤ 1)$
Triangle	$(1 - u h) I (u h ≤ 1)$
Epanechnikov	$34 1 - u h 2 I (u h ≤ 1)$
Gaussian	$e - u h 2 2 / 2 π$

表3

图4

图5

表4

表5

基于KDE的风光出力拟合检验结果

项目	$χ 2$ 检验	K-S检验	RMSE
风电出力（标幺值）	25.421 4	0.114 2	0.033 1
光伏出力（标幺值）	10.001 9	0.270 8	0.013 4

表5

表6

图6

图7

图8

图9

表7

图10

表8

图11

表9

储能设备运行参数

参数	数值	参数	数值
$P e l e m a x$ /kW	1 000	$E b a t$ /(kW·h)	1 000
$P e l e m i n$ /kW	200	σ/%	4.6
$ρ$ /[m³·(kW·h)^-1]	0.25	$η c$ /%	95
$η e l$ /%	82	$S O C m a x$ /%	95
$P c h m a x$ /kW	800	$S O C m i n$ /%	10
$P d i s m a x$ /kW	800

表9

表10

系统其他参数

参数	数值	参数	数值
$C p e r_e l e$ /[元·(kW·h)^-1]	0.50	$K p v$ /[元·(kW·h)^-1]	0.04
$C p e r_b a t$ /[元·(kW·h)^-1]	0.05	$k w$ /[元·(kW·h)^-1]	0.23
$C p e r_c o m$ /(元·m^-3)	0.21	$k p v$ /[元·(kW·h)^-1]	0.23
$K w$ /[元·(kW·h)^-1]	0.03

表10

图12

图13

图14

表11

表12

参考文献 29

[1]	许传博, 刘建国. 氢储能在我国新型电力系统中的应用价值、挑战及展望[J]. 中国工程科学, 2022, 24(3): 89-99. doi: 10.15302/J-SSCAE-2022.03.010
	XU Chuanbo, LIU Jianguo. Hydrogen energy storage in China's new-type power system: Application value, challenges, and prospects[J]. Strategic Study of CAE, 2022, 24(3): 89-99. doi: 10.15302/J-SSCAE-2022.03.010
[2]	韩世旺, 赵颖, 张兴宇, 等. 面向碳中和的新型电力系统氢储能调峰技术研究[J]. 综合智慧能源, 2022, 44(9): 20-26. doi: 10.3969/j.issn.2097-0706.2022.09.003
	HAN Shiwang, ZHAO Ying, ZHANG Xingyu, et al. Researches on hydrogen storage peak-shaving technology for new power systems to achieve carbon neutrality[J]. Integrated Intelligent Energy, 2022, 44(9): 20-26. doi: 10.3969/j.issn.2097-0706.2022.09.003
[3]	GOMES I L R, POUSINHO H M I, MELICIO R, et al. Stochastic coordination of joint wind and photovoltaic systems with energy storage in day-ahead market[J]. Energy, 2017, 124:310-320. doi: 10.1016/j.energy.2017.02.080
[4]	CHOI J, EOM H, BAEK S M. A wind power probabilistic model using the reflection method and multi-kernel function kernel density estimation[J]. Energies, 2022, 15(24):9436. doi: 10.3390/en15249436
[5]	GUAN J S, LIN J, GUAN J J, et al. A novel probabilistic short-term wind energy forecasting model based on an improved kernel density estimation[J]. International Journal of Hydrogen Energy, 2020, 45(43): 23791-23808. doi: 10.1016/j.ijhydene.2020.06.209
[6]	朱怡莹, 周荣生, 罗龙波. 考虑需求响应与光伏不确定性的电力系统经济调度[J]. 太阳能学报, 2023, 44 (1): 62-68. doi: 10.19912/j.0254-0096.tynxb.2021-0844
	ZHU Yiying, ZHOU Rongsheng, LUO Longbo. Economic dispatch of power system considering demand response and pv uncertainty[J]. Acta Energiae Solaris Sinica, 2023, 44 (1): 62-68. doi: 10.19912/j.0254-0096.tynxb.2021-0844
[7]	MAISAM W, BARAA M, EL-FOULY T H M, et al. Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling[J]. Energy Conversion and Management, 2022, 266:115811. doi: 10.1016/j.enconman.2022.115811
[8]	LI Y, ZOU Y, TAN Y, et al. Optimal stochastic operation of integrated low-carbon electric power, natural gas, and heat delivery system[J]. IEEE Transactions on Sustainable Energy, 2018, 9(1): 273-283. doi: 10.1109/TSTE.2017.2728098
[9]	高圣溥. 面向电力系统规划的风光出力序列生成和场景缩减方法[D]. 重庆: 重庆大学, 2021.
	GAO Shengpu. Wind/photovoltaic power time series generation and scenarios reduction methods for power system planning[D]. Chongqing: Chongqing University, 2021.
[10]	WANG Z, WANG W S, LIU C, et al. Probabilistic forecast for multiple wind farms based on regular vine Copulas[J]. IEEE Transactions on Power Systems, 2018, 33(1): 578-589. doi: 10.1109/TPWRS.2017.2690297
[11]	PAN Y, SHI L B, NI Y X. Modelling of multiple wind farms output correlation based on Copula theory[J]. The Journal of Engineering, 2017, 13:2303-2308.
[12]	XIE Z Q, JI T Y, LI M S, et al. Quasi-Monte Carlo based probabilistic optimal power flow considering the correlation of wind speeds using Copula function[J]. IEEE Transactions on Power Systems, 2018, 33(2): 2239-2247. doi: 10.1109/TPWRS.2017.2737580
[13]	SUN C, BIE Z H, XIE M, et al. Fuzzy Copula model for wind speed correlation and its application in wind curtailment evaluation[J]. Renewable Energy, 2016, 93: 68-76. doi: 10.1016/j.renene.2016.02.049
[14]	朱晓荣, 金绘民, 王羽凝. 基于混合高斯模型与Copula函数结合的光伏电站功率相依结构建模[J]. 太阳能学报, 2019, 48(7): 1912-1919.
	ZHU Xiaorong, JIN Huimin, WANG Yuning. Analysis on dependence structure among photovoltaic power outputs based on combination of Copula function and Gaussian mixture model[J]. Acta Energiae Solaris Sinica, 2019, 48(7): 1912-1919.
[15]	SCHMIDT J, CANCELLA R, PEREIRA O A. The role of wind power and solar PV in reducing risks in the Brazilian hydro-thermal power system[J]. Energy, 2016, 115:1748-1757. doi: 10.1016/j.energy.2016.03.059
[16]	钟嘉庆, 李茂林, 江静, 等. 基于Copula理论的风/光出力预测误差分析方法的研究[J]. 电工电能新技术, 2017, 36(6): 39-46.
	ZHONG Jiaqing, LI Maolin, JIANG Jing, et al. Method of wind/solar output forecast error analysis based on Copula theory[J]. Advanced Technology of Electrical Engineering and Energy, 2017, 36(6): 39-46.
[17]	GAO J W, YANG Y, GAO F J, et al. Optimization of electric vehicles based on Frank-Copula-GlueCVaR combined wind and photovoltaic output scheduling research[J]. Energies, 2021, 14(19):6080. doi: 10.3390/en14196080
[18]	HAN Q K, CHU F L. Directional wind energy assessment of China based on nonparametric Copula models:ScienceDirect[J]. Renewable Energy, 2020, 164:1334-1349. doi: 10.1016/j.renene.2020.10.149
[19]	ZHENG W D, ZHANG M, LI Y X, et al. Optimal dispatch for reversible solid oxide cell-based hydrogen/electric vehicle aggregator via stimuli-responsive charging decision estimation[J]. International Journal of Hydrogen Energy, 2022, 47(13): 8502-8513. doi: 10.1016/j.ijhydene.2021.12.157
[20]	任宇路, 程昱舒, 张雪瑞, 等. 碳交易下含氢储能的综合能源系统运行优化[J/OL]. 电测与仪表, 2024: 1-9 (2024-06-21)[2025-05-10]. https://link.cnki.net/urlid/23.1202.th.20240621.1203.004.
	REN Yulu, CHENG Yushu, ZHANG Xuerui, et al. Operation optimization of integrated energy system with hydrogen storageunder carbon trading[J/OL]. Electrical Measurement & Instrumentation, 2024:1-9(2024-06-21)[2025-05-10]. https://link.cnki.net/urlid/23.1202.th.2024

[21]	VISHNU S, PRZEMYSLAW J, MICHAL J, et al. Microgrid energy management using metaheuristic optimization algorithms[J]. Applied Soft Computing, 2023, 134: 109981. doi: 10.1016/j.asoc.2022.109981
[22]	SULAIMAN A A, ALROBAIAN A A. Optimization of combined heat and power systems by meta-heuristic algorithms: An overview[J]. Energies, 2022, 15(16):5977. doi: 10.3390/en15165977
[23]	ELLAHI M, ABBAS G. A hybrid metaheuristic approach for the solution of renewables-incorporated economic dispatch problems[J]. IEEE Access, 2020, 8:127608-127621. doi: 10.1109/Access.6287639
[24]	ASHTARI B, ALIZADEH BIDGOLI M, BABAEI M, et al. A two-stage energy management framework for optimal scheduling of multi-microgrids with generation and demand forecasting[J]. Neural Computing and Applications, 2022, 34(14):12159-12173. doi: 10.1007/s00521-022-07103-w
[25]	XU X Y, YAN Z, XU S L. Estimating wind speed probability distribution by diffusion-based kernel density method[J]. Electric Power Systems Research, 2015, 121:28-37. doi: 10.1016/j.epsr.2014.11.029
[26]	王海鸣, 张润之, 周家辉, 等. 离网式风光互补制氢合成绿氨系统容量配置优化分析[J]. 综合智慧能源, 2024, 46(11): 73-82. doi: 10.3969/j.issn.2097-0706.2024.11.009
	WANG Haiming, ZHANG Runzhi, ZHOU Jiahui, et al. Optimal capacity configuration of off-grid wind-solar hybrid hydrogen production and green ammonia synthesis system[J]. Integrated Intelligent Energy, 2024, 46(11): 73-82. doi: 10.3969/j.issn.2097-0706.2024.11.009
[27]	宋宇, 李涵, 楚皓翔, 等. 计及可靠性的风光互补发电系统容量优化配比研究[J]. 电气技术, 2022(6): 49-58.
	SONG Yu, LI Han, CHU Haoxiang, et al. Optimal proportion study of wind-solar hybrid generation system considering reliability[J]. Electrical Engineering, 2022(6): 49-58.
[28]	张元曦, 杨国华, 杨娜, 等. 基于K-means聚类的LSTM-SVR-DE光伏功率组合预测[J]. 综合智慧能源, 2025, 47(2): 71-78. doi: 10.3969/j.issn.2097-0706.2025.02.007
	ZHANG Yuanxi, YANG Guohua, YANG Na, et al. Photovoltaic power prediction based on K-means clustering and the LSTM-SVR-DE model[J]. Integrated Intelligent Energy, 2025, 47(2): 71-78. doi: 10.3969/j.issn.2097-0706.2025.02.007
[29]	闫群民, 马瑞卿, 马永翔, 等. 一种自适应模拟退火粒子群优化算法[J]. 西安电子科技大学学报, 2021, 48(4): 120-127.
	YAN Qunmin, MA Ruiqing, MA Yongxiang, et al. Adaptive simulated annealing particle swarm optimization algorithm[J]. Journal of Xidian University, 2021, 48(4): 120-127.

Copula函数	秩相关系数		欧氏距离
Copula函数	Spearman	Kendall	欧氏距离
正态Copula	0.047 2	0.031 5	116.260 4
t-Copula	0.061 7	0.041 3	84.598 0
Frank-Copula	0.035 7	0.023 8	81.085 5
Gumbel-Copula	0.035 6	0.023 6	11.228 4
Clayton-Copula	0.190 7	0.128 0	24.261 6
样本数据	0.031 4	0.023 0

性能基准函数	收敛速度最高	收敛精度最高
Griewank function	AW-PSO	ASA-PSO
Rastringin function	ASA-PSO	ASA-PSO
Sphere function	ASA-PSO	相同
Schaffer function	AW-PSO和ASA-PSO	ASA-PSO

时段	Copula函数	拟合参数	时段	Copula函数	拟合参数
5	Gumbel	3.027 6	13	Frank	3.039 4
6	Gumbel	2.009 8	14	Frank	4.365 9
7	Gumbel	1.440 6	15	Frank	5.620 1
8	Gumbel	1.353 3	16	Frank	6.279 9
9	Gumbel	1.272 6	17	Gumbel	2.230 7
10	Gumbel	1.308 7	18	Gumbel	2.010 4
11	Gumbel	1.363 3	19	Gumbel	1.783 8
12	Frank	2.155 5

场景	概率
场景	风电	光伏
1	0.374 9	0.163 0
2	0.156 9	0.115 4
3	0.096 9	0.172 4
4	0.163 6	0.221 1
5	0.082 4	0.173 2
6	0.125 3	0.154 9

时段		电价
低谷	00:00 —07:00	0.308
平时	07:00 —09:00，11:00 —19:00	0.594
高峰	09:00 —11:00，19:00 —24:00	0.880