Research on the Two-stage Multi-objective Optimal Configuration Method of Reactive Power Compensation Devices in Power Grid Based on Power Loss Index

doi:10.11985/2023.04.026

Abstract

Abstract:

In view of the fact that the current distribution network reactive power configuration optimization problem has poor solution efficiency and is easily trapped into local optimum, a study on the multi-objective optimal configuration of two-stage reactive power compensation considering power loss indices is carried out. A multi-objective optimal configuration model for two-stage reactive power compensation devices in power grids based on the power loss index is developed with the objective functions of minimizing loss cost, purchase cost, installation cost, and operation cost, and introducing penalty functions to solve the optimal installation location and capacity of reactive power compensation devices in power grids under constrained conditions using the remora optimization algorithm. The proposed method is validated by simulations carried out on IEEE-34 and PG-69 systems. The simulation results show that by considering the power loss index, it can effectively further reduce the network losses, improve the node voltage distribution level, and reduce the equipment cost and expense, while shortening the solution time of the optimization problem. In addition, the remora optimization algorithm used has a stronger global optimal solution search capability, which can better adapt to changes in the distribution network load level and the access of distributed power sources.

Key words: Reactive power compensation, optimal allocation, power loss index, penalty function, remora optimization algorithm

CLC Number:

TM08

GUO Ting, CHEN Zhonghao, XU Liangde, YANG Fan. Research on the Two-stage Multi-objective Optimal Configuration Method of Reactive Power Compensation Devices in Power Grid Based on Power Loss Index[J]. Journal of Electrical Engineering, 2023, 18(4): 239-250.

Figures/Tables 21

References 20

[1]	杨明, 罗隆福. 计及风电与负荷不确定性的电力系统无功随机优化调度[J]. 电力系统保护与控制, 2020, 48(19):134-141.
	YANG Ming, LUO Longfu. Stochastic optimal reactive power dispatch in a power system considering wind power and load uncertainty[J]. Power System Protection and Control, 2020, 48(19):134-141.
[2]	卢姬, 常俊晓, 张云阁, 等. 考虑DG不确定性的主动配电网两阶段无功机会约束优化方法[J]. 电力系统保护与控制, 2021, 49(21):28-35.
	LU Ji, CHANG Junxiao, ZHANG Yunge, et al. Two-stage reactive power chance-constrained optimization method for an active distribution network considering DG uncertainties[J]. Power System Protection and Control, 2021, 49(21):28-35.
[3]	任辰, 刘浪, 周健, 等. 基于柔性软开关的主动配电网两阶段鲁棒优化运行[J]. 现代电力, 2021, 38(6):610-619.
	REN Chen, LIU Lang, ZHOU Jian, et al. Two-stage robust optimization operation for active distribution network based on soft open point[J]. Modern Electric Power, 2021, 38(6):610-619.
[4]	郑能, 丁晓群, 管志成, 等. 基于场景法的配电网有功-无功协调优化[J]. 电网技术, 2019, 43(5):1640-1651.
	ZHENG Neng, DING Xiaoqun, GUAN Zhicheng, et al. Coordinated optimization of active power and reactive power in distribution network based on scenario method[J]. Power System Technology, 2019, 43(5):1640-1651.
[5]	张国彦, 姜磊, 战文华, 等. 基于负荷曲线分段的配电网无功优化策略[J]. 科学技术与工程, 2021, 21(25):10710-10717.
	ZHANG Guoyan, JIANG Lei, ZHAN Wenhua, et al. Reactivepower optimization strategy of distribution network based onsegmental load curve[J]. Science Technology and Engineering, 2021, 21(25):10710-10717.
[6]	顾洁, 孟璐, 朱瞳彤, 等. 数据驱动的无精确建模含源配电网无功运行优化[J]. 电力自动化设备, 2021, 41(1):1-11.
	GU Jie, MENG Lu, ZHU Tongtong, et al. Data-driven optimization for reactive power operation in source distribution network without accurate modeling[J]. E1ectric Power Automation Equipment, 2021, 41(1):1-11.
[7]	张俊涛, 程春田, 申建建, 等. 考虑风光不确定性的高比例可再生能源电网短期联合优化调度方法[J]. 中国电机工程学报, 2020, 40(18):5921-5932.
	ZHANG Juntao, CHENG Chuntian, SHEN Jianjian, et al. Short term joint optimal operation method for high proportion renewable energy grid considering wind-solar uncertainty[J]. Proceedings of the CSEE, 2020, 40(18):5921-5932.
[8]	李培帅, 吴在军, 张错, 等. 主动配电网分布式混合时间尺度无功/电压控制[J]. 电力系统自动化, 2021, 45(16):160-168.
	LI Peishuai, WU Zaijun, ZHANG Cuo, et al. Distributed hybrid-timescale voltage var control in active distribution networks[J]. Automation of Electric Power Systems, 2021, 45(16):160-168.
[9]	刘梦依, 邱晓燕, 张志荣, 等. 计及风光出力相关性的配电网多目标无功优化[J]. 电网技术, 2020, 44(5):1892-1899.
	LIU Mengyi, QIU Xiaoyan, ZHANG Zhirong, et al. Multi-objective reactive power optimization of distribution net-work considering output correlation between wind turbine sand photovoltaic units[J]. Power System Technology, 2020, 44(5):1892-1899.
[10]	寇凌峰, 吴鸣, 李洋, 等. 主动配电网分布式有功无功优化调控方法[J]. 中国电机工程学报, 2020, 40(6):1856-1866.
	KOU Lingfeng, WU Ming, LI Yang, et al. Optimization and control method of distributed active and reactive power in active distribution network[J]. Proceedings of the CSEE, 2020, 40(6):1856-1866.
[11]	张倩, 丁津津, 张道农, 等﹒基于集群划分的高渗透率分布式系统无功优化[J]. 电力系统自动化, 2019, 43(3):130-143.
	ZHANG Qian, DING Jinjin, ZHANG Daonong, et al. Reactive power optimization of high-penetrati on distributed generation system based on clusters partition[J]. Automation of Electric Power System, 2019, 43(3):130-143.
[12]	胡雪凯, 尹瑞, 时珉, 等. 基于改进粒子群算法的分布式光伏集群划分与无功优化策略[J]. 电力电容器与无功补偿, 2021, 42(4):14-21.
	HU Xuekai, YIN Rui, SHI Min, et al. Distributed photovoltaic cluster partition and reactive power optimization strategy based on improved particle swarm optimization algorithm[J]. Power Capacitors and Reactive Power Compensation, 2021, 42(4):14-21.
[13]	王岩, 陈文浩, 陈筱韵, 等. 基于和声搜索算法的配电网多目标无功优化[J]. 南方电网技术, 2014, 8(5):51-55.
	WANG Yan, CHEN Wenhao, CHEN Xiaoyun, et al. Multi objective reactive power optimization of distribution network based on harmony search algorithm[J]. Southern Power System Technology, 2014, 8(5):51-55.
[14]	郭堃, 薛太林, 耿杰, 等. 基于改进鸡群算法的无功优化综合分析[J]. 电气自动化, 2021, 43(6):36-38.
	GUO Kun, XUE Tailin, GENG Jie, et al. Comprehensive analysis of reactive power optimization based on improved chicken swarm algorithm[J]. Electrical Automation, 2021, 43(6):36-38.
[15]	杨秀, 焦楷丹, 孙改平, 等. 考虑负荷多无功用电场景的城市配电网无功优化配置[J]. 电力建设, 2022, 43(8):42-52. doi: 10.12204/j.issn.1000-7229.2022.08.005
	YANG Xiu, JIAO Kaidan, SUN Gaiping, et al. Reactive power optimization of urban distribution network considering multiple reactive power scenarios of loads[J]. Electric Power Construction, 2022, 43(8):42-52. doi: 10.12204/j.issn.1000-7229.2022.08.005
[16]	滕德云, 滕欢, 潘晨, 等. 基于鲸鱼优化算法的无功优化调度[J]. 电测与仪表, 2018, 55(24):51-58.
	TENG Deyun, TENG Huan, PAN Chen, et al. Reactive power optimal dispatching based on whale optimization algorithm[J]. Electric Measurement and Instrumentation, 2018, 55(24):51-58.
[17]	潘静, 金炜, 丁津津, 等. 基于CS-PSO算法的配电网无功优化[J]. 合肥工业大学学报, 2018, 41(5):628-633.
	PAN Jing, JIN Wei, DING Jinjin, et al. Reactive power optimization of distribution network based on CS-PSO algorithm[J]. Journal of Hefei University of Technology, 2018, 41(5):628-633.
[18]	夏芃, 张倩, 王群京, 等. 模糊聚类下混沌优化人工鱼群算法的无功优化[J]. 现代电子技术, 2022, 45(13):135-139.
	XIA Peng, ZHANG Qian, WANG Qunjing, et al. Reactive power optimization of chaotic optimization artificial fish swarm algorithm under fuzzy clustering[J]. Modern Electronic Technology, 2022, 45(13):135-139.
[19]	从帆平, 周建萍, 茅大钧, 等. 基于改进教与学算法的含电能路由器的电力系统无功优化[J]. 电力建设, 2022, 43(6):110-118. doi: 10.12204/j.issn.1000-7229.2022.06.012
	CONG Fanping, ZHOU Jianping, MAO Dajun, et al. Reactive power optimization of power system with power router based on improved teaching and learning algorithm[J]. Electric Power Construction, 2022, 43(6):110-118. doi: 10.12204/j.issn.1000-7229.2022.06.012
[20]	JIA Heming, PENG Xiaoxu, LANG Chunbo. Remora optimization algorithm[J]. Expert Systems with Applications, 2021, 185:1-25.

对比指标	IEEE-34节点			PG-69节点
对比指标	无补偿	鮣鱼优化	鮣鱼优化+功率损失指数	无补偿	鮣鱼优化	鮣鱼优化+功率损失指数
有功损耗/kW	221.72	173.91	169.53	226.47	156.62	153.56
最小电压/p.u.	0.941 6	0.948 3	0.950 7	0.908 9	0.928 2	0.932 3
无功配置(节点/容量kVar)	—	BUS26/817.91 BUS13/636.45	BUS24/701.86 BUS20/816.25	—	BUS50/1 000 BUS48/210.61	BUS51/979.36 BUS49/233.21
成本费用/元	97 113.36	82 683.72	82 620.69	99 193.86	74 007.34	73 839.15
电容器费用/元	—	7 632.29	7 921.49	—	6 537.70	6 546.56
优化求解时间/s	—	2.72	1.92	—	4.26	3.53

对比指标	IEEE-34节点			PG-69节点
对比指标	方法1	方法2	方法3	方法1	方法2	方法3
有功损耗/kW	169.53	170.12	171.49	153.56	154.67	154.41
最小电压/p.u.	0.950 7	0.948 6	0.948 6	0.932 3	0.928 7	0.929 3
无功配置(节点/容量kVar)	BUS24/701.86 BUS20/816.25	BUS25/370.20 BUS24/276.40 BUS21/298.20 BUS20/573.13	BUS25/590.03 BUS21/857.36	BUS51/979.36 BUS49/233.21	BUS52/825.60 BUS49/387.59	BUS53/205.25 BUS51/978.38
成本费用/元	82 620.69	83 523.92	82 723.58	73 839.15	74 293.27	74 038.84
电容器费用/元	7 921.49	9 010.75	7 606.58	6 546.56	6 549.36	6 416.33
优化求解时间/s	1.92	1.25	2.02	3.53	2.69	3.72
最差值/元	87 361.52	85 423.60	89 399.04	89 962.27	85 288.35	79 047.01
均值/元	82 649.74	83 536.80	82 757.93	73 875.31	74 387.65	74 131.33
方差	62 097.48	20 465.45	105 578.84	521 930.11	718 890.92	166 520.63
标准差	249.19	143.06	324.92	722.44	847.87	408.07

对比指标	IEEE-34节点系统			PG-69节点系统
	负荷系数			负荷系数
	1	0.8	0.6	1	0.8	0.6
有功损耗/kW	169.53	111.26	47.54	153.56	100.44	60.42
最小电压/p.u.	0.950 7	0.959 5	0.977 3	0.932 3	0.951 9	0.954 6
无功配置(节点/容量kVar)	BUS24/701.86 BUS20/816.25	BUS24/649.91 BUS21/783.66	BUS26/549.98 BUS21/870.59	BUS51/979.36 BUS49/233.21	BUS52/215.92 BUS50/968.20	BUS51/1 000 BUS50/32.71
成本费用/元	82 620.69	56 952.32	28 369.52	73 839.15	49 774.73	31 717.77
电容器费用/元	7 921.49	7 541.10	7 482.59	6 546.56	6 417.74	5 737.15
优化求解时间/s	1.92	1.84	1.75	3.53	3.42	3.38

接入信息	IEEE-34节点系统		PG-69节点系统
接入信息	1 DG	2 DG	1 DG	2 DG
接入节点	BUS 26	BUS 26, 11	BUS 53	BUS 53, 17
容量/kVar	2 086	1 400, 850	1 700	1 560, 618.78

对比指标	IEEE-34节点			PG-69节点
对比指标	无DG	1 DG	2 DG	无DG	1 DG	2 DG
有功损耗/kW	169.53	61.20	48.33	153.56	46.90	34.31
最小电压/p.u.	0.950 7	0.976 1	0.982 5	0.932 3	0.969 7	0.985 2
无功配置/(节点/容量kVar)	BUS24/701.86 BUS20/816.25	BUS26/611.33 BUS20/881.79	BUS26/119.63 BUS25/623.86	BUS51/979.36 BUS49/233.21	BUS50/547.67	BUS50/473.40
成本费用/元	82 620.69	35 310.13	26 766.35	73 839.15	22 066.13	16 469.02
电容器费用/元	7 921.49	7 809.10	4 435.72	6 546.56	3 009.53	2 675.30
优化求解时间/s	1.92	1.86	1.78	3.53	3.44	3.36