Optimal Configuration of Microgrid Considering Static Voltage Stability of Distribution Network

doi:10.12204/j.issn.1000-7229.2022.08.009

Abstract

Abstract:

Considering that the output of photovoltaic and wind power is complementary in time, and that the energy storage devices may provide bidirectional power flow, the renewable energy and storage devices are often connected to the distribution grid in the form of microgrid to realize the high proportion of renewable energy access. The static voltage stability index of the existing distribution grid is improved, and the static voltage stability and operation economy of the distribution network are used as the target to study the location and capacity determination of the energy storage in the microgrid. The multi-scenario technology based on K-means++ method is used to deal with the uncertainty of renewable energy output, and the optimal capacity allocation ratio of three types of scenery storage devices is calculated for the renewable energy output characteristics of the distribution network in different areas. The equilibrium optimizer (EO) algorithm is improved by using chaotic sequences generated by Tent mapping instead of randomly generated initial populations to achieve multi-objective problem solution according to congestion and non-dominated ranking. The effectiveness of the model and algorithm in this paper is verified by simulation in the IEEE 33-node and PG&E-69 system.

Key words: distribution network, static voltage stability, multi-objective optimization, distributed generation, multi-scenario analysis, microgrid

CLC Number:

TM76

XU Yanchun, ZHANG Jin, WANG Ping, MI Lu. Optimal Configuration of Microgrid Considering Static Voltage Stability of Distribution Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 87-101.

Figures/Tables 26

Fig.1 P-V curve

Fig.2 Function characteristics of f(x)

Table A1 Parameter settings suitable for different voltage limits

Table A2 Comparison of indicators of different algorithms in the test function UF1

Table A3 Comparison of indicators of different algorithms in the test function UF2

Table A4 Comparison of indicators of different algorithms in the test function UF3

Table A5 Comparison of indicators of different algorithms in the test function UF4

Table A6 Comparison of indicators of different algorithms in the test function UF5

Table A7 Comparison of indicators of different algorithms in the test function UF6

Fig.3 Flow chart of microgrid optimal configuration

Fig.B1 IEEE 33-node system diagram

Fig.4 Indices and voltage extreme value change with load growth in IEEE 33-node system

Table 1 Computing time comparison of different methods

Fig.B2 Annual active power output of renewable energy in high altitude areas

Fig.B3 Annual active power output of renewable energy in gully areas

Fig.B4 Annual active power output of renewable energy in normal areas

Fig.5 Sum of square errors in clusters with different K values

Fig.6 Specific proportion of each scenario

Fig.7 Pareto optimal frontier

Table 2 Optimization results of MOEO algorithm in IEEE 33-node system

Table 3 Optimization results in IEEE 33-node system without considering voltage stability

Table 4 Optimization results of MOGWO algorithm in IEEE 33-node system

Fig.B5 PG&E-69 node system diagram

Table 5 Optimization results of MOEO algorithm in PG&E-69 system

Table 6 Optimization results of MOGWO algorithm in PG&E-69 system

Table 7 The number of Pareto solutions obtained by the two algorithms

References 30

[1]	董新伟, 裴晨晨, 邓薇, 等. 含新能源接入的配电网中分布式储能系统控制策略[J]. 电力建设, 2021, 42(5): 81-89.
	DONG Xinwei, PEI Chenchen, DENG Wei, et al. Research on control strategy of distributed energy storage system in distribution network with new energy[J]. Electric Power Construction, 2021, 42(5): 81-89.
[2]	许晓艳, 马烁, 黄越辉, 等. 大规模风电接入对电网静态电压稳定性影响机理研究[J]. 中国电力, 2015, 48(3): 139-143.
	XU Xiaoyan, MA Shuo, HUANG Yuehui, et al. Mechanical study on influence of large scale wind power on static voltage stability[J]. Electric Power, 2015, 48(3): 139-143.
[3]	胡伟, 戚宇辰, 张鸿轩, 等. 风光水多能源电力系统互补智能优化运行策略[J]. 发电技术, 2020, 41(1): 9-18.
	HU Wei, QI Yuchen, ZHANG Hongxuan, et al. Complementary intelligent optimization operation strategy of wind-solar-hydro multi-energy power system[J]. Power Generation Technology, 2020, 41(1): 9-18.
[4]	李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
	LI Junhui, ZHANG Jiahui, LI Cuiping, et al. Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
[5]	XU Y C, ZHANG J, WANG P, et al. Research on the bi-level optimization model of distribution network based on distributed cooperative control[J]. IEEE Access, 2021, 9: 11798-11810. doi: 10.1109/ACCESS.2021.3051464 URL
[6]	卢光辉, 滕欢, 廖寒逊. 基于变量相关性特征聚类的多场景分布式电源规划[J]. 电力系统及其自动化学报, 2020, 32(12): 95-101.
	LU Guanghui, TENG Huan, LIAO Hanxun. Multi-scenario distributed generation planning based on clustering of variable correlation features[J]. Proceedings of the CSU-EPSA, 2020, 32(12): 95-101.
[7]	MURTY V V S N, KUMAR A. Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth[J]. International Journal of Electrical Power & Energy Systems, 2015, 69: 246-256. doi: 10.1016/j.ijepes.2014.12.080 URL
[8]	蔡国伟, 刘旭, 张旺, 等. 基于改进灰狼优化算法的分布式电源优化配置[J]. 太阳能学报, 2019, 40(1): 134-141.
	CAI Guowei, LIU Xu, ZHANG Wang, et al. Optimal configuration of distributed generation based on improved grey optimization algorithm[J]. Acta Energiae Solaris Sinica, 2019, 40(1): 134-141.
[9]	许珊, 李扬. 考虑时序特性的配电网分布式电源优化配置[J]. 现代电力, 2019, 36(2): 8-16.
	XU Shan, LI Yang. Optimal allocation of distributed generators in distribution network based on time-sequence characteristics[J]. Modern Electric Power, 2019, 36(2): 8-16.
[10]	饶萍, 向月, 姚昊天, 等. 市场环境下配电网分布式光储协同规划[J]. 中国电力, 2022, 55(1): 178-188.
	RAO Ping, XIANG Yue, YAO Haotian, et al. Collaborative planning for distribution network and PV-ESS in the marketization environment[J]. Electric Power, 2022, 55(1): 178-188.
[11]	彭春华, 于蓉, 孙惠娟. 基于K-均值聚类多场景时序特性分析的分布式电源多目标规划[J]. 电力自动化设备, 2015, 35(10): 58-65.
	PENG Chunhua, YU Rong, SUN Huijuan. Multi-objective DG planning based on K-means clustering and multi-scenario timing characteristics analysis[J]. Electric Power Automation Equipment, 2015, 35(10): 58-65.
[12]	李建林, 谭宇良, 王含, 等. 配网及光储微网储能系统配置优化策略[J]. 高电压技术, 2022, 48(5): 1893-1902.
	LI Jianlin, TAN Yuliang, WANG Han, et al. Research on configuration optimization of energy storage system in distribution network and optical storage microgrid[J]. High Voltage Engineering, 2022, 48(5): 1893-1902.
[13]	黄雨佳, 孙秋野, 王睿, 等. 面向综合能源系统的静态电压稳定性分析[J]. 中国电机工程学报, 2019, 39(S1): 44-53.
	HUANG Yujia, SUN Qiuye, WANG Rui, et al. Static voltage stability analysis for integrated energy system[J]. Proceedings of the CSEE, 2019, 39(S1): 44-53.
[14]	齐伟夫, 杨红磊, 李君明, 等. 适应多节点电压稳定性的模拟评估指标[J]. 电网技术, 2013, 37(6): 1639-1644.
	QI Weifu, YANG Honglei, LI Junming, et al. Simulation evaluation indices adapted to voltage stability of multi-node system[J]. Power System Technology, 2013, 37(6): 1639-1644.
[15]	YESURATNAM G, THUKARAM D. Congestion management in open access based on relative electrical distances using voltage stability criteria[J]. Electric Power Systems Research, 2007, 77(12): 1608-1618. doi: 10.1016/j.epsr.2006.11.007 URL
[16]	PÉREZ-LONDOÑO S, RODRÍGUEZ L F, OLIVAR G. A simplified voltage stability index (SVSI)[J]. International Journal of Electrical Power & Energy Systems, 2014, 63: 806-813. doi: 10.1016/j.ijepes.2014.06.044 URL
[17]	罗旷, 王莉, 陈超, 等. 考虑电压稳定性与越限的配电网电压质量评估指标[J]. 南方电网技术, 2019, 13(4): 48-53.
	LUO Kuang, WANG Li, CHEN Chao, et al. Voltage quality assessment index for distribution network considering voltage stability and over-limit[J]. Southern Power System Technology, 2019, 13(4): 48-53.
[18]	白斌, 韩明亮, 林江, 等. 含风电和光伏的可再生能源场景削减方法[J]. 电力系统保护与控制, 2021, 49(15): 141-149.
	BAI Bin, HAN Mingliang, LIN Jiang. Scenario reduction method of renewable energy including wind power and photovoltaic[J]. Power System Protection and Control, 2021, 49(15): 141-149.
[19]	张悦, 刘文泽. 考虑功率波动及储能寿命的微电网储能容量优化配置[J]. 电测与仪表, 2021, 58(10): 59-66.
	ZHANG Yue, LIU Wenze. Optimal allocation of micro-grid energy storage capacity considering power fluctuation and energy storage life[J]. Electrical Measurement & Instrumentation, 2021, 58(10): 59-66.
[20]	吴盛军, 李群, 刘建坤, 等. 基于储能电站服务的冷热电多微网系统双层优化配置[J]. 电网技术, 2021, 45(10): 3822-3832.
	WU Shengjun, LI Qun, LIU Jiankun, et al. Bi-level optimal configuration for combined cooling heating and power multi-microgrids based on energy storage station service[J]. Power System Technology, 2021, 45(10): 3822-3832.
[21]	FARAMARZI A, HEIDARINEJAD M, STEPHENS B, et al. Equilibrium optimizer: A novel optimization algorithm[J]. Knowledge-Based Systems, 2020, 191: 105190. doi: 10.1016/j.knosys.2019.105190 URL
[22]	GE L J, LI Y L, YAN J, et al. Short-term load prediction of integrated energy system with wavelet neural network model based on improved particle swarm optimization and chaos optimization algorithm[J]. Journal of Modern Power Systems and Clean Energy, 2021, 9(6): 1490-1499. doi: 10.35833/MPCE.2020.000647 URL
[23]	ZHANG Q, ZHOU A, ZHAO S, et al. Multi-objective optimization Test Instances for the CEC 2009 Special Session and Competition[J]. Mechanical Engineering, 2008, 264: 1-30.
[24]	MIRJALILI S, JANGIR P, MIRJALILI S Z, et al. Optimization of problems with multiple objectives using the multi-verse optimization algorithm[J]. Knowledge-Based Systems, 2017, 134: 50-71. doi: 10.1016/j.knosys.2017.07.018 URL
[25]	MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization[J]. Expert Systems With Applications, 2016, 47: 106-119. doi: 10.1016/j.eswa.2015.10.039 URL
[26]	王涛, 刘雪飞, 郑重, 等. 基于潮流线性化的分布式发电选址定容新算法[J]. 电力自动化设备, 2020, 40(8): 117-128.
	WANG Tao, LIU Xuefei, ZHENG Zhong, et al. Novel locating and sizing algorithm for distributed generation based on power flow linearization[J]. Electric Power Automation Equipment, 2020, 40(8): 117-128.
[27]	LIU R H, XUE A C, LI M K, et al. Impact of large-scale wind power penetration on the static voltage stability based on the L index[J]. Applied Mechanics and Materials, 2015, 713/714/715: 1111-1114.
[28]	薛安成, 刘瑞煌, 李铭凯, 等. 基于支路电压方程的在线电压稳定指标[J]. 电工技术学报, 2017, 32(7): 95-103.
	XUE Ancheng, LIU Ruihuang, LI Mingkai, et al. A new on-line voltage stability index based on voltage equation of the branch[J]. Transactions of China Electrotechnical Society, 2017, 32(7): 95-103.
[29]	吕涛, 唐巍, 丛鹏伟, 等. 分布式电源与配电网架多目标协调规划[J]. 电力系统自动化, 2013, 37(21): 139-145.
	LÜ Tao, TANG Wei, CONG Pengwei, et al. Multi-objective coordinated planning of distribution network incorporating distributed generators[J]. Automation of Electric Power Systems, 2013, 37(21): 139-145.
[30]	杨柳青, 马群凯, 刘晓军, 等. 含风光柴储的独立微电网容量优化配置研究[J]. 吉林电力, 2020, 48(2): 19-23.
	YANG Liuqing, MA Qunkai, LIU Xiaojun, et al. Optimal allocation of optical storage capacity of independent microgrid containing wind-solar-diesel-battery hybrid power generation[J]. Jilin Electric Power, 2020, 48(2): 19-23.