基于改进蝙蝠算法的微电网优化调度

doi:10.3969/j.issn.1000-7229.2015.06.017

摘要/Abstract

摘要：

在分析微电网中相关电源发电技术的基础上，建立以发电成本、污染物排放费用、甩负荷补偿费用和微电网网损补偿费用最小的多目标数学优化模型，应用超效率数据包分析评价方法，将多目标优化转换为单目标规划问题。鉴于传统蝙蝠群体易聚集于局部极值，导致早熟，将混沌序列以及自适应调整策略融入到蝙蝠优化算法，提出一种改进型多目标蝙蝠优化算法，为克服算法本身对缺乏变异机制的缺陷，利用混沌理论以及动态自适应调整机制的特性，对蝙蝠算法参数进行调整。最后通过算例验证所提算法具有良好的实用性和适应性，同时也验证了所提模型的实际意义。

关键词: 微电网, 多目标优化, 蝙蝠算法, 混沌理论, 自适应调整

Abstract:

Based on the analysis on power generation technologies related to microgrid, this paper established a multi-objective optimization model, aiming at minimizing the generation cost, pollutant emission cost, the compensation cost of load shedding and microgrid network loss, and used super efficiency data packet analysis evaluation methods to transform multi-objective optimization problem into a single-objective programming one. The traditional bat group easily gathered in local minima, which would lead to premature. So the chaotic sequence and adaptive adjustment strategy were applied into bat optimization algorithm, and a improved multi-objective bat optimization algorithm was proposed, in which the characteristics of chaos theory and dynamic adaptive adjustment mechanism were used to adjust the parameters of the bat algorithm, in order to overcome the defect that the algorithm itself was lack of variation mechanism. Finally, the good practicability and adaptability of proposed algorithm were verified through numerical example, as well as the practical significance of the proposed model.

Key words: microgrid, multi-objective optimization, bat algorithm, chaos theory, adaptive adjustment

中图分类号:

TM 862

翟云峰，蒋云峰，易国伟，潘浩，代希雷. 基于改进蝙蝠算法的微电网优化调度[J]. 电力建设, 2015, 36(6): 103-108.

ZHAI Yunfeng，JIANG Yunfeng，YI Guowei，PAN Hao，DAI Xilei. Optimal Power Flow of Microgrid Based on Improved Bat Algorithm[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(6): 103-108.

参考文献

［1］BASU A K. Microgrids：energy management by strategic deployment of DERs-a comprehensive survey ［J］.Renewable and Sustainable Energy Reviews，2011，9(15)：4348-4856.
［2］王成山，李鹏.分布式发电、微网与智能配电网的发展与挑战［J］.电力系统自动化，2010，34(2)：10-14.
Wang Chengshan，Li Peng.Development and challenges of distributed generation，the micro-grid and smart distribution system［J］.Automation of Electric Power Systems，2010，34(2)：10-14.
［3］陈洁，杨秀，朱兰，等.微网多目标经济调度优化［J］.中国电机工程学报，2013，33(19)：57-66.
Chen Jie，Yang Xiu，Zhu Lan，et al.Microgrid multi-objective economic dispatch optimization ［J］.Proceedings of the CSEE，2013，33(19)：57-66.
［4］石庆均，耿光超，江全元.独立运行模式下的微网实时能量优化调度［J］.中国电机工程学报，2012，32(16)：26-35.
Shi Qingjun，Geng Guangchao，Jiang Quanyuan.Real-time optimal energy dispatch of standalone microgrid［J］.Proceedings of the CSEE，2012，32(16)：26-35.
［5］喻洁，李扬，夏安邦.兼顾环境保护与经济效益的发电调度分布式优化策略［J］.中国电机工程学报，2009，19(16)：63-68.
Yu Jie，Li Yang，Xia Anbang.Distributed optimization of generation dispatch scheduling considering environmental protection and economic profits ［J］.Proceedings of the CSEE，2009，29(16)：63-68.
［6］黄伟，黄婷，周欢，等.基于改进微分进化算法的微电网动态经济优化调度［J］.电力系统自动化，2014，38(9)：211-217.
Huang Wei，Huang Ting，Zhou Huan，et al.Dynamic economical dispatch for microgrid based on improved differential evolution algorithm［J］.Automation of Electric Power Systems 2014，38(9)：211-217.
［7］Yang Xinshe.A new met heuristic bat-inspired algorithm［C］//Nature Inspired Cooperative Strategies for Optimization，2010: 65-74.
［8］肖辉辉，段艳明.基于DE算法改进的蝙蝠算法的研究及应用［J］.计算机仿真，2014，01(32)：32-38.
Xiao Huihui，Duan Yanming.Research and application of improved bat algroithm based on DE algorithm ［J］.Computer Simulation，2014，01(32)：32-38.
［9］贺兴时，丁文静，杨新社.基于模拟退火高斯扰动的蝙蝠优化算法［J］.计算机应用研究，2013，2(31)：392-397.
Hao Xingshi，Ding Wenjing，Yang Xinshe.Bat algorithm based on simulated annealing and Gaussian perturbations［J］.Application Research of Computers，2013，2(31)：392-397.
［10］楼旭明，窦彩兰，汪贵浦.基于DEA的中国电力改革绩效相对有效性评价［J］.当代财经，2006(4)：90-93.
Lou Xuming，Dou Cailan，Wang Guipu.Research on the reform of Chinese electric power work based on DEA model［J］.Contemporary Finance &Economics，2006(4)：90-93.
［11］华瑶，袁宏波，于海龙.基于DEA的火电上市公司业绩分析［J］.经济，2008(12)：28-30.
Hua Yao，Yuan Hongbo，Yu Hailong.The evaluation of the listed thermal power enterprises based on data envolopment analysis［J］.Economy，2008(12)：28-30.
［12］Yang Xinshe.A new metaheuristic bat-inspired algorithm［J］.Nature Inspired Cooperative Strategies for Optimization，Springer, 2010，SCI 284：65-74.
［13］Altringham J.Bats：biology and behaviour ［M］.Oxford：Oxford University Press，1996：37-64.
［14］周勇，陈家俊，姜飞，等.基于改进萤火虫算法的分布式电源优化配置研究［J］.现代电力，2014，31(5):54-58.
Zhou Yong，Chen Jiajun，Jiang Fei, et al.Optimal power flow of distribution network with distributed generation based on modified firefly algorithm［J］.Modern Electric Power，2014，31(5):54-58.
［15］Peng Li, Duo Xu.Optimal operation of microgrid based on improved binary particle swarm optimization algorithm with double-structure coding［C］//Power System Technology (POWERCON), 2014 International Conference on，Chengdu，2014：3141-3146.

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