Optimal configuration of energy storage power station in multi-energy system based on weight adaptive whale optimization algorithm

doi:10.19799/j.cnki.2095-4239.2021.0608

Abstract

Abstract:

The nonlinear programming equation is produced by using the least fluctuation of the output power of a thermal power plant as the goal function, taking into account the operation features and restrictions of a wind-photovoltaic-thermal-storage multi-energy system. The whale optimization algorithm is enhanced by introducing nonlinear adaptive weights coefficient S1 and S2 in the process of encircling prey, bubble-net attacking, and searching for prey, which can coordinate the global search and local search ability of the algorithm. In a multi-energy system that includes wind, solar, and thermal storage, the enhanced method is utilized to solve the optimal design issue of an energy storage power station. The IEEE33-node system is used as the simulation model. The energy storage is positioned at the 13th node, with a configured capacity of 40.2 MWh and an ideal running cost of 13.29 million yuan per year, according to the findings of the upgraded whale optimization algorithm. The results demonstrate that the energy storage configuration strategy proposed in this study can effectively suppress the power fluctuation of the thermal power plant in a multi-energy system, and the peak-valley difference of thermal power plant decreases by 90.79% after the addition of energy storage, which effectively assists in the peak regulation. The optimal configuration technique described in this study is beneficial in boosting the development and construction of energy storage power plants and will give essential assistance for attaining the objectives of "dual carbon" and fostering energy revolution.

Key words: whale optimization algorithm, weight adaptation, energy storage configuration, power fluctuation, transmission losses

CLC Number:

F 407.6

Wei ZENG, Junjie XIONG, Jianlin LI, Suliang MA, Yiwen WU. Optimal configuration of energy storage power station in multi-energy system based on weight adaptive whale optimization algorithm[J]. Energy Storage Science and Technology, 2022, 11(7): 2241-2249.

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Standard test function"

函数	维数	定义域	最优值
$f 1 (x) = ∑ i = 1 N x i + ∏ i = 1 N x i$	30	[-10，10]	0
$f 2 (x) = - 20 e x p - 0.2 ∑ i = 1 N x i 2 - e x p 1 N ∑ i = 1 N c o s 2 π x i + 20 + e$	30	[-32，32]	0

Table 1

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 3

Fig. 7

Fig. 8

Fig. 9

Table 4

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统计结果	算法	f₁	f₂
平均值	WWOA	1.15×10^-35	2.64
	WOA	2.17×10^-28	1.86
	GA	6.07×10^-13	5.42E
均方差	WWOA	2.55×10^-34	1.14
	WOA	7.83×10^-26	9.57
	GA	4.99×10^-11	18.7

符号	取值	参数解释
η_c	0.9	充电效率
η_c	0.95	放电效率
δ	0.1%	自放电率
SOC_min	0.2	电池放电下限状态
SOC_max	0.8	电池充电上限状态
SOC₀	0.6	电池初始状态

功率	平均值 /MW	标准差 /MW	峰值 /MW	谷值 /MW	峰谷差 /MW
风电	362.8	69.4	494.0	234.2	299.1
光伏	35.9	46.2	129.9	0	129.9
火电（无储能）	190.1	55.8	256.7	53.5	203.2
火电（配储能）	195.6	6.3	208.9	190.1	18.7

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