Multi-Objective Optimization Strategy for Wind-Photovoltaic-Pumped Storage Combined System Based on Gray Wolf Algorithm

doi:10.16183/j.cnki.jsjtu.2023.049

Abstract

Abstract:

The output of wind power and photovoltaic has the characteristics of randomness, volatility, and intermittency. Direct grid connection will lead to a lower power generation income of the power station, a greater volatility of grid connection of electric energy, and more wind and photovoltaic power discards, resulting in lower carbon emission reductions. The addition of pumped storage power plants effectively reduces the above impacts. Therefore, this paper studies the application scenario of wind photovoltaic and pumped storage combined power generation, establishes a multi-objective optimization model that comprehensively considers the three objectives of maximizing the economic benefits of the combined system, minimizing the system power fluctuation, and maximizing carbon emission reduction, and converts the multi-objective problem into a single objective problem for solution by normalization. In this paper, the gray wolf algorithm, which can realize the adaptive adjustment of local search and global search, is used to simulate and optimize the grid connected power of wind power, photovoltaic, and pumped storage. The optimization results show that the established model can effectively improve the economic benefits of the system and greatly reduce the fluctuation of power grid connection. In addition, the efficient use of new energy also greatly improves the carbon emission reduction capacity of the joint system, which proves that the model has high feasibility.

Key words: carbon emission reduction, multi-objective optimization, normalization, gray wolf algorithm

CLC Number:

ZHANG Liang, ZHENG Lidong, LENG Xiangbiao, LÜ Ling, CAI Guowei. Multi-Objective Optimization Strategy for Wind-Photovoltaic-Pumped Storage Combined System Based on Gray Wolf Algorithm[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1554-1566.

Figures/Tables 15

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标度	定义
1	表示两个元素相比,具有同等重要性
3	表示两个元素相比,一个元素比另一个元素稍微重要
5	表示两个元素相比,一个元素比另一个元素明显重要
7	表示两个元素相比,一个元素比另一个元素强烈重要
9	表示两个元素相比,一个元素比另一个元素极端重要
2,4,6,8	上述相邻判断的中值

测试函数	维度	指标	GWO	PSO	AGWO	函数最优解
F₁	5	平均值	1.0586×10^-111	1.56837×10^-75	2.8089×10^-189	0
		标准差	2.0204×10^-111	4.78018×10^-75	0
		最优值	4.7246×10^-117	5.66403×10^-82	2.0224×10^-200
		最小运行时间/s	0.0625	0.0938	0.0625
F₃	5	平均值	5.1416×10^-110	3.03639×10^-66	5.6786×10^-185	0
		标准差	1.562×10^-109	9.38151×10^-66	0
		最优值	4.9023×10^-120	2.03774×10^-70	2.0189×10^-189
		最小运行时间/s	0.1250	0.1250	0.0313
F₄	10	平均值	8.206186494	6.925003246	6.038853119	0
		标准差	0.976010289	0.14160085	0.740366322
		最优值	6.25242962	6.662404072	4.946185531
		最小运行时间/s	0.1406	0.1406	0.0938
F₇	10	平均值	0.001194785	3.9968×10^-15	3.9968×10^-15	0
		标准差	0.003769227	0	0
		最优值	3.9968×10^-15	3.9968×10^-15	3.9968×10^-15
		最小运行时间/s	0.0938	0.125	0.0781
F₅	30	平均值	114.3494578	38.2086689	0	0
		标准差	19.73841416	9.941091481	0
		最优值	94.33174368	22.88404825	0
		最小运行时间/s	0.2031	0.2188	0.1406
F₆	30	平均值	23.30240464	4.262098719	0.916806479	0
		标准差	18.33678485	2.204487459	0.253678272
		最优值	2.041006692	2.295927461	0.382751422
		最小运行时间/s	0.3133	0.3159	0.1560
F₉	30	平均值	1.2887×10^-43	7.0961×10^-12	1.58352×10^-72	0
		标准差	1.49665×10^-43	1.17089×10^-11	1.9561×10^-72
		最优值	5.75044×10^-45	1.34152×10^-13	1.41472×10^-73
		最小运行时间/s	0.1563	0.2031	0.0781
F₂	30	平均值	1.8676×10^-154	1.85983×10^-17	6.2139×10^-269	0
		标准差	5.8446×10^-154	5.86839×10^-17	0
		最优值	7.8137×10^-168	5.39049×10^-24	7.041×10^-285
		最小运行时间/s	0.4063	0.4219	0.3594

时间	C_i/[元·(MW·h)^-1]	时段
10:00—15:00	1289.3	峰值
18:00—21:00	1289.3	峰值
7:00—10:00	873.1	平值
15:00—18:00	873.1	平值
21:00—23:00	873.1	平值
23:00—7:00	457.0	谷值

算法	系统经济效益/ (元·d^-1)	系统碳减排量/ (kg·d^-1)	系统输出功率平均波动率
加入前	2.85×10⁷	1.86×10⁷	0.3287
加入后AGWO优化	3.99×10⁷	2.36×10⁷	0.1314
加入后GWO优化	3.70×10⁷	2.34×10⁷	0.1174
加入后PSO优化	3.72×10⁷	2.30×10⁷	0.1210