Economic optimal scheduling of electricity-hydrogen coordinated energy storage system considering spatiotemporal correlation of wind and photovoltaic power outputs

doi:10.3969/j.issn.2097-0706.2025.12.004

Abstract

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

CLC Number:

TK01：TK91

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.

Figures/Tables 26

Table 1

Fig.1

Fig.2

Fig.3

Table 2

Table 3

Common kernel functions and their mathematical expressions

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	$e - u h 2 2 / 2 π$

Table 3

Fig.4

Fig.5

Table 4

Table 5

Verification results of wind and photovoltaic power output fitting based on KDE

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

Table 5

Table 6

Fig.6

Fig.7

Fig.8

Fig.9

Table 7

Fig.10

Table 8

Fig.11

Table 9

Operating parameters of energy storage devices

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

Table 9

Table 10

Other system parameters

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

Table 10

Fig.12

Fig.13

Fig.14

Table 11

Table 12

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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