基于系统生存性的骨干网架搜索方法
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摘要
构建骨干网架,是差异化规划电网以提高其抵抗自然灾害能力的关键手段。基于系统生存性理论并考虑风机出力不确定性的影响,提出一种可以平衡以线路与节点数量之和为代表的经济性和系统综合生存能力的骨干网架构建方法。文章采用改进的生物地理学优化算法搜索骨干网架,通过改进算法迁移模型,引入随机扰动算子和基于最小生成树理论的连通性修复策略以提高算法收敛速度和精度,并与传统的生物地理学算法、遗传算法、二进制粒子群算法对比分析。算例结果表明,该方法能够快速搜索出综合经济性和生存性最优且满足连通性约束及系统安全运行条件的骨干网架,且最优骨干网架会限制风电出力比例以提高系统生存性。
Identifying backbone grid has great significance to the differential planning of power grid,which can improve the ability to withstand natural disasters effectively. Based on the theory of system survivability,this paper presents an approach to construct backbone grid by taking the uncertainty of wind power into account. The approach can balance the survivability and cost,which represented by the sum of lines and busses. The biogeography-based optimization algorithm( BBO) is applied to search for backbone grid. In order to improve the convergence rate and precision,modified migration model,random perturbation operator,clear duplicate operator and repairing strategy which is based on the theory of the minimum spanning tree are employed in the algorithm. Compared to the traditional BBO,genetic algorithm( GA) and binary particle swarm optimization( BPSO),the improved algorithm can figure out backbone grid effectively,which has optimal indices of survivability and cost. Meanwhile,the optimal case satisfies constraint conditions of power grid safe operation and connectivity. The backbone can weaken the influence of wind power output uncertainty to a certain extent and improve the survivability of system.
Identifying backbone grid has great significance to the differential planning of power grid,which can improve the ability to withstand natural disasters effectively. Based on the theory of system survivability,this paper presents an approach to construct backbone grid by taking the uncertainty of wind power into account. The approach can balance the survivability and cost,which represented by the sum of lines and busses. The biogeography-based optimization algorithm( BBO) is applied to search for backbone grid. In order to improve the convergence rate and precision,modified migration model,random perturbation operator,clear duplicate operator and repairing strategy which is based on the theory of the minimum spanning tree are employed in the algorithm. Compared to the traditional BBO,genetic algorithm( GA) and binary particle swarm optimization( BPSO),the improved algorithm can figure out backbone grid effectively,which has optimal indices of survivability and cost. Meanwhile,the optimal case satisfies constraint conditions of power grid safe operation and connectivity. The backbone can weaken the influence of wind power output uncertainty to a certain extent and improve the survivability of system.
引文
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[11]赵一婕,刘涤尘,吴军,等.基于线性判别分析与主成分分析的骨干网架生存性评估方法[J].电网技术,2014,38(2):388-394.Zhao Yijie,Liu Dichen,Wu Jun,et al. Survivability evaluation of backbone network based on linear discriminant analysis and principal component analysis[J]. Power System Technology,2014,38(2):388-394.
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[13]李翔硕,王淳.基于生物地理学优化算法的输电网络规划[J].电网技术,2013,37(2):477-481.Li Xiangshuo,Wang Chun. Application of biogeography-based optimization in transmission network planning[J]. Power System Technology,2013,37(2):477-481.
[14]刘思革,程浩忠,崔文佳,等.基于粗糙集理论的多目标电网规划最优化模型[J].中国电机工程学报,2007,27(7):65-69.Liu Sige,Cheng Haozhong,Cui Wenjia,et al. Optimal model of multiobjective electric power network planning based on rough set theory[J].Proceedings of the CSEE,2007,27(7):65-69
[15]刘文颖,谢昶,文晶,等.基于小生境多目标粒子群算法的输电网检修计划优化[J].中国电机工程学报,2013,33(4):141-148.Liu Wenying,Xie Chang,Wen Jing,et al. Optimization of transmission network maintenance scheduling based on niche multi-objective particle swarm algorithm[J]. Proceedings of the CSEE, 2013, 33(4):141-148.
[16]陈道君,龚庆武,乔卉,等.采用改进生物地理学算法的风电并网电力系统多目标发电调度[J].中国电机工程学报,2012,32(31):150-158.Chen Daojun,Gong Qingwu,Qiao Hui,et al. Multi-objective generation dispatching for wind power integrated system adopting improved biogeography-based optimization algorithm[J]. Proceedings of the CSEE,2012,32(31):150-158.
[17]Storn R,Price K V,Lampinen J. Differential evolution about practical approach to global optimization. Berlin, Germany:SpringerVerlag,2005.
[18]封全喜.生物地理学优化算法研究及其应用[D].西安:西安电子科技大学,2014.Feng Quanxi. Research on biogeography based optimization and its application[D]. Xi’an:Xidian University,2014.
[2]胡毅,胡建勋,刘廷.我国南方地区电网大范围覆冰灾害的特点分析与防治措施[J].电力设备,2008,9(6):1-4.Hu Yi,Hu Jianxun,Liu Ting. Analysis and countermeasures for large area icing accident on power grid in northern China[J]. Electrical Equipment,2008,9(6):1-4.
[3]刘建琴,孙珂,李晖,等.推行差异化设计以提高电网抗灾能力[J].华北电力技术,2009,(5):41-44,54.Liu Jianqin,Sun Ke,Li Hui,et al. Practicing different design for improving grid ability against calamity[J]. North China Electric Power,2009,(5):41-44,54.
[4]黎灿兵,梁锦照.电网差异化规划新方法[J].电力系统自动化,2009,33(24):11-15.Li Canbing,Liang Jinzhao. A novel method of power grid differential planning[J]. Automation of Electric Power Systems,2009,33(24):11-15.
[5]曹一家,陈晓刚,孙可.基于复杂网络理论的大型电力系统脆弱线路辨识[J].电力自动化设备,2006,26(12):1-5.Cao Yijia,Chen Xiaogang,Sun Ke. Identification of vulnerable lines in power grid based on complex network theory[J]. Electric Power Automation Equipment,2006,26(12):1-5.
[6]洪运福.基于复杂网络的输电网差异化规划研究[D].北京:华北电力大学,2012.Hong Yunfu. Transmission network differential planning based on complex network theory[D]. Beijin:North China Electric Power University,2012.
[7]王亮,刘艳,顾雪平,等.综合考虑节点重要度和线路介数的网络重构[J].电力系统自动化,2010,34(12):29-33.Wang Liang,Liu Yan,Gu Xueping,et al. Skeleton-network reconfiguration based on node importance and line betweenness[J]. Automation of Electric Power Systems,2010,34(12):29-33
[8]杨文辉,毕天姝,黄少峰,等.基于电网生存性评估的关键线路识别方法[J].中国电机工程学报,2011,31(7):29-35.Yang Wenhui,Bi Tianshu,Huang Shaofeng,et al. An approach for critical lines identification based on the survivability of power grid[J].Proceedings of the CSEE,2011,31(7):29-35.
[9] Sangjoon P,Jiyoung S,Byunggi K. A survivability strategy in mobile networks[J]. IEEE Trans. on Vehicular Technology,2006,55(1):328-340.
[10]陈伟,杨鼎才.网络生存性的测度[J].无线电工程,2006,36(2):1-2,64.Chen Wei,Yang Dingcai. Survivability measure for networks[J]. Radio Engineering of China,2006,36(2):1-2,64.
[11]赵一婕,刘涤尘,吴军,等.基于线性判别分析与主成分分析的骨干网架生存性评估方法[J].电网技术,2014,38(2):388-394.Zhao Yijie,Liu Dichen,Wu Jun,et al. Survivability evaluation of backbone network based on linear discriminant analysis and principal component analysis[J]. Power System Technology,2014,38(2):388-394.
[12]董飞飞,刘涤尘,吴军,等.基于改进BBO优化算法和电网生存性的核心骨干网架构建[J].中国电机工程学报,2014,34(16):2659-2667.Dong Feifei,Liu Dichen,Wu Jun,et al. A method of constructing core backbone grid based on improved BBO[J]. Proceedings of the CSEE,2014,34(16):2659-2667.
[13]李翔硕,王淳.基于生物地理学优化算法的输电网络规划[J].电网技术,2013,37(2):477-481.Li Xiangshuo,Wang Chun. Application of biogeography-based optimization in transmission network planning[J]. Power System Technology,2013,37(2):477-481.
[14]刘思革,程浩忠,崔文佳,等.基于粗糙集理论的多目标电网规划最优化模型[J].中国电机工程学报,2007,27(7):65-69.Liu Sige,Cheng Haozhong,Cui Wenjia,et al. Optimal model of multiobjective electric power network planning based on rough set theory[J].Proceedings of the CSEE,2007,27(7):65-69
[15]刘文颖,谢昶,文晶,等.基于小生境多目标粒子群算法的输电网检修计划优化[J].中国电机工程学报,2013,33(4):141-148.Liu Wenying,Xie Chang,Wen Jing,et al. Optimization of transmission network maintenance scheduling based on niche multi-objective particle swarm algorithm[J]. Proceedings of the CSEE, 2013, 33(4):141-148.
[16]陈道君,龚庆武,乔卉,等.采用改进生物地理学算法的风电并网电力系统多目标发电调度[J].中国电机工程学报,2012,32(31):150-158.Chen Daojun,Gong Qingwu,Qiao Hui,et al. Multi-objective generation dispatching for wind power integrated system adopting improved biogeography-based optimization algorithm[J]. Proceedings of the CSEE,2012,32(31):150-158.
[17]Storn R,Price K V,Lampinen J. Differential evolution about practical approach to global optimization. Berlin, Germany:SpringerVerlag,2005.
[18]封全喜.生物地理学优化算法研究及其应用[D].西安:西安电子科技大学,2014.Feng Quanxi. Research on biogeography based optimization and its application[D]. Xi’an:Xidian University,2014.