计及可靠性的分布式风电源多目标优化配置
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摘要
分布式风电源(distributed wind generation,DWG)和负荷出力的时序性和不确定性,将造成停电损失功率的时序性和不确定性,进而影响配电网供电可靠性。本文基于Monte?Carlo随机模拟,将考虑时序性和不确定性的停电损失可靠性指标引入模型。建立综合考虑最小化DWG投资成本、最小化网损以及最小化停电损失的多目标优化配置模型。为了提高算法的收敛性能,采用改进多目标粒子群优化算法求解。该算法运用小生境技术进行多目标全局寻优,同时利用NSGA-II非支配排序策略和拥挤度距离选择最优粒子,引导粒子跳出局部最优。针对已获得的Pareto解,采用最短归一化距离的方法选择最终方案。最后,以IEEE 33节点配电网为例进行仿真实验,结果验证了所提模型和算法的可行性和有效性。
The timing and uncertainty of distributed wind generation(DWG) and load output will result in the timing and uncertainty of outage power, which in turn will affect the power supply reliability of the distribution network. In this paper, the power outage loss index of the timing and uncertainty is introduced in the model based on Monte-Carlo stochastic simulation. A multi-objective optimal allocation model of DWG is established with consideration of minimum investment cost of DWG, minimum network loss and minimum power outage. For improving the convergence performance of the algorithm, an improved multi-objective particle swarm optimization algorithm is used for the calculation. In this algorithm, the multi-objective global optimization is performed by the niche technology and,at the same time,non-dominated sorting strategy and crowding distance is used to select optimal particles, which lead the particles to jump out of local optimum. As for the obtained Pareto solution, the final solution is selected with the shortest normalized distance method. Finally, the simulation experiment is carried out with IEEE 33 node distribution network, and the feasibility and effectiveness of the proposed model and algorithm are verified.
The timing and uncertainty of distributed wind generation(DWG) and load output will result in the timing and uncertainty of outage power, which in turn will affect the power supply reliability of the distribution network. In this paper, the power outage loss index of the timing and uncertainty is introduced in the model based on Monte-Carlo stochastic simulation. A multi-objective optimal allocation model of DWG is established with consideration of minimum investment cost of DWG, minimum network loss and minimum power outage. For improving the convergence performance of the algorithm, an improved multi-objective particle swarm optimization algorithm is used for the calculation. In this algorithm, the multi-objective global optimization is performed by the niche technology and,at the same time,non-dominated sorting strategy and crowding distance is used to select optimal particles, which lead the particles to jump out of local optimum. As for the obtained Pareto solution, the final solution is selected with the shortest normalized distance method. Finally, the simulation experiment is carried out with IEEE 33 node distribution network, and the feasibility and effectiveness of the proposed model and algorithm are verified.
引文
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[10] PILO F, CELLI G, MOCCI S, et al. Multiobjective rogramming for optimal DG integration in active distribution systems[C]//Power and Energy Society General Meeting,Minneapolis, MN, 2010:1-7.
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[12]姜欣,陈红坤,向铁元,等.考虑调峰特性的电网风电接人能力分析[J].电力自动化设备,2014,34(12):13-18.JIANG Xin,CHEN Hongkun, XIANG Tieyuan,et al. Wind power penetration considering peak regulation characteristics[J]. Electric Power Automation Equipment, 2014, 34(12):13-18.
[13] MOKRYANI G. Active distribution networks planning with integration of demand response[J]. Solar Energy, 2015, 122(3):1362-1370.
[14] HAN Fei,YAO Haifen,LING Qinghua et al. An improved evolutionary extreme learning machine based on particle swarm optimization[J]. Neurocomputing,2013,116:87-93.
[15]黄平,于金杨,元泳泉.一种改进的小生境多目标粒子群优化算法[J].计算机工,2011,37(18):1-3.HUANG Ping, YU Jinyang, YUAN Yongquan. Improved niching multi-objective particle swarm optimization algorithm[J]. Computer Engineering, 2011,37(18):1-3.
[16]BARAN M E, Wu F F. Network reconfiguration in distribution systems for loss reduction and load balancing[J]. IEEE Transactions on Power Delivery, 1989,4(2):1401-1407.
[2]康龙云,郭红霞,吴捷,等.分布式电源及其接人电力系统时若干研究课题综述[J].电网技术,2010,324(11):43-47.KANG Longyun,GUO Hongxia, WU Jie,et al. Characteristics of distributed generation system and research issues caused by connecting it to power system[J]. Power System Technology, 2010,324(11):43-47.
[3]鲍巧敏.考虑分布式电源的配电网可靠性评估[D].北京华北电力大学,2013.
[4]夏澍,周明,李庚银.分布式电源选址定容的多目标优化算法[J].电网技术,2011,35(9):115-121.XIA Shu, ZHOU Ming, LI Gengyin. Multi-objective optimization algorithm for distributed generation locating and sizing[J]. Power System Technology, 2011,35(9):115-121.
[5]陈海东,庄平,夏建矿,等.基于改进萤火虫算法的分布式电源优化配置[J].电力系统保护与控制,2016,44(1):149-154.CHEN Haidong,ZHUANG Ping,XIA Jiankuang,et al. Optimal power flow of distribution network with distributed Generation based on modified firefly algorithm[J]. Power System Protection and Control, 2016,44(1):149-154.
[6]王瑞琪,李珂,张承慧,等.基于多目标混沌量子遗传算法的分布式电源规划[J].电网技术,2011,35(12):183-189.WANG Ruiqi,LI Ke, ZHANG Chenghui,et al. Distributed generation planning based on multi-objective chaotic quantum genetic algorithm[J]. Power System Technology, 2011,35(12):183-189.
[7]马麟,刘建鹏.考虑时序特性和环境效益的多目标多类型分布式电源规划[J].电力系统保护与控制,2016,44(19):32-40.MA Lin, LIU Jianpeng. Multi-objective planning of multitype distributed generation considering timing characteristics and environmental benefits[J]. Power System Protection and Control,2016,44(19):32-40.
[8]李亮,唐巍,白牧可,等.考虑时序特性的多目标分布式电源选址定容规划[J].电力系统自动化,2013,37(3):58-63,128.LI Liang, TANG Wei, BAI Muke, et al. Multi-objective loating and sizing of distributed generators based on time-sequence characteristics[J]. Automation of Electric Power Systems,2013,37(3):5 8-63,128.
[9] LI Ming. A novel swarm intelligence optimization inspired by evolution process of a bacterial colony[C]//Proceedings of the 10th World Congress on Intelligent Control and Automation.[S.l.]:[s. n.],2012:450-453.
[10] PILO F, CELLI G, MOCCI S, et al. Multiobjective rogramming for optimal DG integration in active distribution systems[C]//Power and Energy Society General Meeting,Minneapolis, MN, 2010:1-7.
[11] LIU Zhipeng, WEN Fushuan, LEDWICH G. Optimal siting and sizing of distributed generators in distribution systems considering uncertainties[J]. IEEE Transactions on Power Delivery, 2011,26(4):2541-2551.
[12]姜欣,陈红坤,向铁元,等.考虑调峰特性的电网风电接人能力分析[J].电力自动化设备,2014,34(12):13-18.JIANG Xin,CHEN Hongkun, XIANG Tieyuan,et al. Wind power penetration considering peak regulation characteristics[J]. Electric Power Automation Equipment, 2014, 34(12):13-18.
[13] MOKRYANI G. Active distribution networks planning with integration of demand response[J]. Solar Energy, 2015, 122(3):1362-1370.
[14] HAN Fei,YAO Haifen,LING Qinghua et al. An improved evolutionary extreme learning machine based on particle swarm optimization[J]. Neurocomputing,2013,116:87-93.
[15]黄平,于金杨,元泳泉.一种改进的小生境多目标粒子群优化算法[J].计算机工,2011,37(18):1-3.HUANG Ping, YU Jinyang, YUAN Yongquan. Improved niching multi-objective particle swarm optimization algorithm[J]. Computer Engineering, 2011,37(18):1-3.
[16]BARAN M E, Wu F F. Network reconfiguration in distribution systems for loss reduction and load balancing[J]. IEEE Transactions on Power Delivery, 1989,4(2):1401-1407.