基于改进正余弦算法的配电网无功优化

doi:10.3969/j.issn.2097-0706.2024.10.006

摘要/Abstract

摘要：

分布式光伏的并网对潮流分布和节点电压产生了显著影响，给配电网的安全运行带来了挑战。因此，有效地优化控制含分布式光伏的配电网成为亟待解决的问题。为此，提出了一种基于改进正余弦算法（ISCA）的无功优化方法，该方法以减少节点电压偏差和有功网损为目标，并考虑了分布式光伏的无功出力，构建了相应的目标优化模型。ISCA在传统正余弦算法的基础上引入Lévy飞行、精英选择策略以及贪婪算法，增强了原算法的全局及局部搜索能力。在接入多个分布式光伏的IEEE 33系统中进行了仿真试验，算例结果表明使用ISCA后配电网的节点电压偏差和有功网损得到了有效降低。该方法有助于配电网在大量分布式光伏的接入下保证其安全、经济运行，且优化效果明显优于其他优化算法。

关键词: 分布式电源, 无功出力, 改进正余弦算法, 电压偏差, 有功网损, “双碳”目标

Abstract:

The integration of distributed photovoltaic （PV） systems into the grid significantly impacts power flow distribution and node voltage， posing challenges to the safe operation of the distribution network. Therefore， effectively optimized control on distribution networks with integrated distributed PV has become a pressing issue. A reactive power optimization method based on an improved sine-cosine algorithm （ISCA） is proposed， designed to reduce node voltage deviation and active power loss while accounting for the reactive power output of the distributed PV system. A corresponding objective optimization model was constructed. The ISCA improved the traditional sine-cosine algorithm by incorporating Lévy flights， elite selection strategies， and greedy algorithms， thereby enhancing both the global and local search capabilities. Simulation experiments conducted on an IEEE 33 system with multiple distributed PV stations showed that ISCA significantly reduced node voltage deviation and active power loss. This method ensures the safe and economical operation of distribution networks with distributed PV stations，and demonstrates superior optimization performance compared to other algorithms.

Key words: distributed power generation, reactive power output, improve sine-cosine algorithm, voltage deviation, active power loss, "dual carbon" target

中图分类号:

TK01

钱达, 陈浩, 马刚. 基于改进正余弦算法的配电网无功优化[J]. 综合智慧能源, 2024, 46(10): 40-47.

QIAN Da, CHEN Hao, MA Gang. Reactive power optimal scheduling of distribution network based on improved sine-cosine algorithm[J]. Integrated Intelligent Energy, 2024, 46(10): 40-47.

图/表 16

图1

图2

表1

典型函数

函数序号	函数	维度	范围
1	$F 1 = ∑ i = 1 n x i 2$	10	[-100,100]
2	$F 2 = m a x x i, 1 ≤ i ≤ n$	10	[-100,100]
3	$F 3 = ∑ i = 1 n i x i 4$	10	[-1.28,1.28]
4	$F 4 = ∑ i = 1 n x i 2 4 000 - ∏ i = 1 n 1 + c o s x i i$	10	[-600,600]

表1

表2

优化结果

函数	项目	ISCA	SCA	WOA	GWO
$F 1$	平均值	1.985×10^-16	9.818×10^-13	0.131 52	1.792×10^-5
$F 1$	标准差	6.581×10^-16	4.980×10^-12	0.076 74	2.236×10^-5
$F 2$	平均值	0.000 25	0.032 53	1.114 10	0.009 20
$F 2$	标准差	0.000 58	0.018 84	0.579 44	0.007 75
$F 3$	平均值	0.001 46	0.004 07	0.020 14	0.032 39
$F 3$	标准差	0.001 16	0.002 04	0.014 24	0.023 15
$F 4$	平均值	0.030 20	0.065 68	0.145 00	0.769 17
$F 4$	标准差	0.057 23	0.079 94	0.150 05	0.194 83

表2

图3

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表3

图12

表 4

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方案	有功网损/MW	减少率/%
优化前	4.979 2
WOA	4.602 3	7.60
GWO	4.578 2	8.06
ISCA	4.418 7	11.26

方案		有功网损/MW	减少率/%
IEEE 69	优化前	5.839 6	0
IEEE 69	优化后	4.929 4	15.58
IEEE 118	优化前	9.633 9	0
IEEE 118	优化后	8.286 1	13.99