上海交通大学学报

上海交通大学学报 >

2017 , Vol. 51 >Issue 8: 897 - 902

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2017.08.001

兵器工业

 基于改进粒子群算法的配电网综合运行优化

展开
  •  上海交通大学 电力传输与功率变换控制教育部重点实验室， 上海 200240

网络出版日期: 2017-08-30

基金资助

 

收起

 Comprehensive Optimal Dispatch of Distribution Network Based on
 Improved Particle Swarm Optimization Algorithm

Expand
  •  Key Laboratory of Control of Power Transmission and Conversion of
    Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Online published: 2017-08-30

Supported by

 

Fold

摘要

 在无功优化、分布式电源(DG)有功优化和网络重构协同的条件下，以有功网损最小为目标函数、多种电气限制和网络拓扑结构为约束条件建立了配电网综合运行优化模型；针对基本粒子群算法容易陷入局部最优、收敛速度慢等缺点，提出一种改进的粒子群(IPSO)算法，并将其用于求解配电网综合运行优化模型.结果表明，所建配电网综合运行优化模型能够同时优化补偿电容器投切容量、有载调压变压器变比、DG出力和网络开关状态，从而获得配电网的最佳运行状态.同时，通过IEEE 33节点配电网算例的仿真结果验证了配电网综合运行优化模型的有效性和IPSO算法的高效性.

关键词：  ; 配电网; 综合运行优化; 无功优化; 分布式电源有功优化; 网络重构

本文引用格式

李珂，邰能灵，张沈习 .  基于改进粒子群算法的配电网综合运行优化[J]. 上海交通大学学报, 2017 , 51(8) : 897 -902 . DOI: 10.16183/j.cnki.jsjtu.2017.08.001

Abstract

 The paper proposes a novel model of the comprehensive optimal dispatch of distribution network, the objective function is to minimize the active power losses and the constraints are various electrical conditions and the network topology. Basic particle swarm optimization algorithm has the shortcomings which are easy to fall into local optimum and slow to converge. In order to overcome the shortcomings, an improved particle swarm optimization algorithm is proposed, and it is applied to solve the model. Case studies are carried out on the IEEE 33bus distribution network, and the results show the effectiveness of the model and the high efficiency of the algorithm.

Key words： network reconfiguration;  ; distribution network; comprehensive optimal dispatch; reactive power optimization; distributed generation (DG) active power optimization

参考文献

 ［1］刘丽军, 李捷, 蔡金锭. 基于强引导粒子群与混沌优化的电力系统无功优化［J］. 电力自动化设备, 2010, 30(4): 7175.
LIU Lijun, LI Jie, CAI Jinding. Reactive power optimization based on inductionenhanced particle swarm optimization and chaos search［J］. Electric Power Automation Equipment, 2010, 30(4): 7175.
［2］LIANG R H, CHEN Y K, CHEN Y T. Volt/Var control in a distribution system by a fuzzy optimization approach［J］. International Journal of Electrical Power & Energy Systems, 2011, 33(2): 278287.
［3］熊宁, 程浩忠. 基于开关组的禁忌算法在配电网动态重构中的应用［J］. 电力系统自动化, 2008, 32(11): 5660.
XIONG Ning, CHENG Haozhong. Switch groupbased Tabu algorithm applied in distribution network dynamic reconfiguration［J］. Automation of Electric Power Systems, 2008, 32(11): 5660.
［4］陈曦, 程浩忠, 戴岭, 等. 邻域退火粒子群算法在配电网重构中的应用［J］. 高电压技术, 2008, 34(1): 148153.
CHEN Xi, CHENG Haozhong, DAI Ling, et al. Application of simulated annealing particle swarm optimization algorithm in reconfiguration of distribution networks［J］. High Voltage Engineering, 2008, 34(1): 148153.
［5］TULADHAR S, SINGH J G, ONGSAKUL W. A multiobjective approach for distribution network reconfiguration with optimal DG power factor using NSPSO［J］. IET Generation, Transmission & Distribution, 2016, 10(12): 28422851.
［6］ASRARI A, WU T, LOTFIFARD S. The impacts of distributed energy sources on distribution network reconfiguration［J］. IEEE Transactions on Energy Conversion, 2016, 31(2): 606613.
［7］吕清洁, 王韶, 刘庭磊. 含分布式发电的配电网有功—无功综合优化［J］. 电力系统保护与控制, 2012, 40(10): 7176.
L Qingjie, WANG Shao, LIU Tinglei. Active/reactive power integrated optimization in distribution networks with distributed generation［J］. Power System Protection and Control, 2012, 40(10): 7176.
［8］NIKNAM T. A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering distributed generators［J］. Applied Energy, 2011, 88(3): 778788.
［9］ULINUHA A, MOHAMMAD A S M, SYED M I. Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionarybased algorithms［J］. IEEE Transactions on Power Delivery, 2008, 23(1): 434441.
［10］SU C T, LEE C S. Feeder reconfiguration and capacitor setting for loss reduction of distribution systems［J］. Electric Power Systems Rresearch, 2001, 58(2): 97102.
［11］KENNEDY J, EBERHART R. Particle swarm optimization［C］∥Proceedings of IEEE Conference on Neural Networks. Perth, Australia: IEEE, 1995: 19421948.
［12］张君则, 艾欣. 基于粒子群算法的多类型分布式电源并网位置与运行出力综合优化算法［J］. 电网技术, 2014, 38(12): 33723377.
ZHANG Junze, AI Xin. A particle swarm optimization based comprehensive optimization algorithm for gridconnected positions and operation parameters of multitype distribution generations［J］. Power System Technology, 2014, 38(12): 33723377.
［13］BAJPAI P, SINGH S N. Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market［J］. IEEE Transactions on Power Systems, 2007, 22(4): 21522160.
［14］NIKNAM T, IROUZI B B, OSTADI A. A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators［J］. Applied Energy, 2010, 87(6): 19191928.
［15］WANG L X. A course in fuzzy systems and control ［M］. USA：PrenticeHall International, 1997.
［16］欧阳武, 程浩忠, 张秀彬, 等. 基于随机生成树策略的配网重构遗传算法［J］. 高电压技术, 2008, 34(8): 17261730.
OUYANG Wu, CHENG Haozhong, ZHANG Xiubin, et al. Genetic algorithm based on random spanning trees in distribution network reconfiguration［J］. High Voltage Engineering, 2008, 34(8): 17261730.
［17］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.
Options
文章导航

/

〈 〉