基于权重自适应鲸鱼优化算法的多能系统储能电站最优配置

doi:10.19799/j.cnki.2095-4239.2021.0608

摘要/Abstract

摘要：

本文考虑风-光-火-储多能系统的运行特性和约束条件，将火电厂的输出功率波动最小作为目标函数，得到非线性规划方程。利用非线性自适应权重协调寻优算法的全局搜索及局部搜索能力这一特点，在包围猎物、气泡攻击及搜索猎物过程中引入非线性自适应权重系数S₁和S₂，对鲸鱼优化算法进行改进。改进后的算法用来求解多能系统储能电站的优化配置问题。利用IEEE33节点系统作为风-光-火-储系统的仿真模型，根据权重自适应鲸鱼优化算法的计算结果，储能选址在13节点，储能的配置容量为40.2 MWh，储能系统的年运行成本为1329万元。结果表明，本文提出的储能配置策略有效抑制了多能系统中火电厂的功率波动，加入储能后火电厂功率的峰谷差下降了90.79%，有效实现了辅助调峰。对于大规模新能源并网对电力系统造成的冲击，本文提出的优化配置策略有助于储能电站的规划与建设，将为实现“双碳”目标和推动能源革命提供重要支撑。

关键词: 鲸鱼优化算法, 权重自适应, 储能配置, 功率波动, 网损

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

中图分类号:

F 407.6

曾伟, 熊俊杰, 李建林, 马速良, 武亦文. 基于权重自适应鲸鱼优化算法的多能系统储能电站最优配置[J]. 储能科学与技术, 2022, 11(7): 2241-2249.

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.

图/表 13

图1

图2

表1

标准测试函数"

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

表1

表2

图3

图4

图5

图6

表3

图7

图8

图9

表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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