基于差分进化入侵杂草算法的含分布式电源配电网重构
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摘要
文章提出了差分进化入侵杂草算法(DEIWO),对含分布式电源配电网重构模型及分布式电源(DG)接入位置和容量进行综合优化。利用柯西分布对入侵杂草算法进行空间扩散,在计算初始可以产生更多的可行解;引入差分进化策略,优化竞争生存操作过程,解决了入侵杂草算法收敛速度慢且容易陷入局部最优的问题。IEEE 33节点算例结果表明,优化配置的配电网重构可以有效降低网络损耗和减小节点电压偏差,实现分布式能源的高效消纳,算例结果验证了算法的可行性。
A distribution network reconfiguration model with distributed generation is established,and Differential Evolution Invasive Weed Optimization Algorithm(DEIWO) is proposed to reconstruct the distribution network and optimize the location and capacity of distributed generation(DG). By using Cauchy distribution instead of Gauss distribution, the spatial diffusion of invasive weed algorithm is carried out, and more feasible solutions can be generated at the beginning of computation. The differential evolution strategy is introduced to optimize the competition operation process, and the problem of slow convergence and easy falling into local optimum of the invasive weed algorithm is solved. IEEE33 node example results show that the distributed power access and optimal allocation of distribution network reconfiguration can effectively reduce the network loss and reduce the node voltage deviation, achieve efficient distributed energy consumption. At the same time, the numerical results verify the feasibility of the proposed algorithm.
A distribution network reconfiguration model with distributed generation is established,and Differential Evolution Invasive Weed Optimization Algorithm(DEIWO) is proposed to reconstruct the distribution network and optimize the location and capacity of distributed generation(DG). By using Cauchy distribution instead of Gauss distribution, the spatial diffusion of invasive weed algorithm is carried out, and more feasible solutions can be generated at the beginning of computation. The differential evolution strategy is introduced to optimize the competition operation process, and the problem of slow convergence and easy falling into local optimum of the invasive weed algorithm is solved. IEEE33 node example results show that the distributed power access and optimal allocation of distribution network reconfiguration can effectively reduce the network loss and reduce the node voltage deviation, achieve efficient distributed energy consumption. At the same time, the numerical results verify the feasibility of the proposed algorithm.
引文
[1] Arash Asrari,Thomas Wu,Saeed Lotfifard. The impacts of distributed energy sources on distribution network reconfiguration[J].IEEE Transactions on Energy Conversion,2016,103(10):606-613.
[2]吴忠强,赵立儒,贾文静,等.计及DG与STATCOM的配电网重构优化策略[J].电力自动化设备, 2016,36(1):111-116.
[3]刘科研,盛万兴,贾东梨,等.基于协同进化蚁群算法的含光伏发电的配电网重构[J].可再生能源,2017,35(5):702-708.
[4] Rao R S, Ravindra K, Satish K, et al. Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation[J].IEEE Transactions on Power Systems,2013,28(1):317-325.
[5]郭腾云,刘艳.基于博弈方法的含分布式电源配电网重构优化[J].电力系统保护与控制,2017,45(7):28-34.
[6] Yuan Kang Wu, Ching Yin Lee, Le Chang Liu,et al.Study of reconfiguration for the distribution system with distributed generators[J].IEEE trans.on Power Delivery,2010,25(3):1678-1684.
[7]丁磊,刘俊勇,刘友波,等.考虑分布式发电商投资的区域配电网光伏储能容量配置[J].可再生能源,2017,35(3):374-383.
[8]张涛,史苏怡,徐雪琴.基于二进制量子粒子群算法的含分布式电源配电网重构[J].电力系统保护与控制,2016,44(4):22-28.
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[10] Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous space[J].Journal of Global Optimization,1997,11(4):341-359.
[11]卢扬,吴俊勇,郝亮亮.基于改进MOBPSO算法的含分布式电源的多目标配电网重构[J].电力系统保护与控制,2016,44(7):62-68.
[12]向小蓉,刘涤尘,向农,等.基于并行禁忌搜索算法的配电网重构[J].电网技术,2012,36(8):100-105.
[13]王超学,吕志奇,董慧,等.基于改进蜜蜂进化型遗传算法的含分布式电源的配电网重构[J].电力系统保护与控制,2012,40(15):52-56,62.
[14]李春兰,马超,王长云,等.基于自适应算法的含分布式电源继电保护研究[J].可再生能源,2015,33(9):1329-1336.
[15] Saravanan B,Vasudevan E R,Kothari D P.Unit commitment problem solution using invasive weed optimization algorithm[J].Electrical Power and Energy Systems,2014,55:21-28.
[16] Nikoofardah,Hajimirsadeghi H,Rahimikian A,et al.Multiobjective invasive weed optimization:application to analysis of Pareto improvement models in electricity markets[J].Applied Soft Computing,2012,12(1):100-112.
[17] Price K,Storn R, Lampinen J.Differential Evolution-a Ppractical Approach to Global Optimization[M].Berlin:Springer,2005.
[18]张冠军.基于二进制差分进化算法的含有分布式电源的配电网重构研究[D].重庆:重庆大学,2011.
[19] B Esmailnezhad,H Shayeghi.Simultaneous distribution network reconfiguration and DG allocation for loss reduction by invasive weed optimization algorithm[A].2012 Conference on Smart Electric Grids Technology[C].Tehran:IEEE,2012.166-172.
[20] M F AlHajri, M R AlRashidi, M E El Hawary.Hybrid particle swarm optimization approach for optimal distribution generation sizing and allocation in distribution systems[A].IEEE Canadian Conference on Electrical and Computer Engineering[C].Ontario:IEEE,2007.1290-1293.
[2]吴忠强,赵立儒,贾文静,等.计及DG与STATCOM的配电网重构优化策略[J].电力自动化设备, 2016,36(1):111-116.
[3]刘科研,盛万兴,贾东梨,等.基于协同进化蚁群算法的含光伏发电的配电网重构[J].可再生能源,2017,35(5):702-708.
[4] Rao R S, Ravindra K, Satish K, et al. Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation[J].IEEE Transactions on Power Systems,2013,28(1):317-325.
[5]郭腾云,刘艳.基于博弈方法的含分布式电源配电网重构优化[J].电力系统保护与控制,2017,45(7):28-34.
[6] Yuan Kang Wu, Ching Yin Lee, Le Chang Liu,et al.Study of reconfiguration for the distribution system with distributed generators[J].IEEE trans.on Power Delivery,2010,25(3):1678-1684.
[7]丁磊,刘俊勇,刘友波,等.考虑分布式发电商投资的区域配电网光伏储能容量配置[J].可再生能源,2017,35(3):374-383.
[8]张涛,史苏怡,徐雪琴.基于二进制量子粒子群算法的含分布式电源配电网重构[J].电力系统保护与控制,2016,44(4):22-28.
[9] Mehrabian AR,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[10] Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous space[J].Journal of Global Optimization,1997,11(4):341-359.
[11]卢扬,吴俊勇,郝亮亮.基于改进MOBPSO算法的含分布式电源的多目标配电网重构[J].电力系统保护与控制,2016,44(7):62-68.
[12]向小蓉,刘涤尘,向农,等.基于并行禁忌搜索算法的配电网重构[J].电网技术,2012,36(8):100-105.
[13]王超学,吕志奇,董慧,等.基于改进蜜蜂进化型遗传算法的含分布式电源的配电网重构[J].电力系统保护与控制,2012,40(15):52-56,62.
[14]李春兰,马超,王长云,等.基于自适应算法的含分布式电源继电保护研究[J].可再生能源,2015,33(9):1329-1336.
[15] Saravanan B,Vasudevan E R,Kothari D P.Unit commitment problem solution using invasive weed optimization algorithm[J].Electrical Power and Energy Systems,2014,55:21-28.
[16] Nikoofardah,Hajimirsadeghi H,Rahimikian A,et al.Multiobjective invasive weed optimization:application to analysis of Pareto improvement models in electricity markets[J].Applied Soft Computing,2012,12(1):100-112.
[17] Price K,Storn R, Lampinen J.Differential Evolution-a Ppractical Approach to Global Optimization[M].Berlin:Springer,2005.
[18]张冠军.基于二进制差分进化算法的含有分布式电源的配电网重构研究[D].重庆:重庆大学,2011.
[19] B Esmailnezhad,H Shayeghi.Simultaneous distribution network reconfiguration and DG allocation for loss reduction by invasive weed optimization algorithm[A].2012 Conference on Smart Electric Grids Technology[C].Tehran:IEEE,2012.166-172.
[20] M F AlHajri, M R AlRashidi, M E El Hawary.Hybrid particle swarm optimization approach for optimal distribution generation sizing and allocation in distribution systems[A].IEEE Canadian Conference on Electrical and Computer Engineering[C].Ontario:IEEE,2007.1290-1293.