含功效系数法的电力系统多目标综合优化
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摘要
电力系统安全经济控制是一个多目标优化问题,针对此类多目标规划,引入了功效系数法从不同侧面对评价对象进行计算评分。文中选取了发电系统总发电成本、有功网损、电压偏移量、静态电压稳定裕度作为评价指标,采用改进层次分析法确定评价指标相应的权重,建立了电力系统综合优化模型。以IEEE-14节点为例,使用改进粒子群算法对综合优化模型进行检验,研究表明含功效系数法的电力系统综合优化结果可以提高电力系统运行的安全性和经济性。
Safety and economic control of power system is a multi-objective optimization problem,aiming at this kind of multi-objective programming,the efficacy coefficient method is introduced to calculate score evaluation objects from different angles. This paper selects the total power generation cost,active network loss and voltage offset,static voltage stability margin of power system as evaluation index,and the improved analytic hierarchy process( AHP) is adopted to determine the corresponding weights of evaluation indexes to establish a comprehensive optimization model of power system. Taking IEEE14 nodes as an example,the improved particle swarm optimization( PSO) algorithm is used for integrated optimization model test,and the results showed that of the integrated optimization results of power system containing efficacy coefficient method can improve the safety and economy of power system operation.
Safety and economic control of power system is a multi-objective optimization problem,aiming at this kind of multi-objective programming,the efficacy coefficient method is introduced to calculate score evaluation objects from different angles. This paper selects the total power generation cost,active network loss and voltage offset,static voltage stability margin of power system as evaluation index,and the improved analytic hierarchy process( AHP) is adopted to determine the corresponding weights of evaluation indexes to establish a comprehensive optimization model of power system. Taking IEEE14 nodes as an example,the improved particle swarm optimization( PSO) algorithm is used for integrated optimization model test,and the results showed that of the integrated optimization results of power system containing efficacy coefficient method can improve the safety and economy of power system operation.
引文
[1]孙伟卿,王承民,张焰.基于Pareto最优的电力系统有功—无功综合优化[J].电力系统自动化,2009,33(10):38-42.Sun Weiqing,Wang Chengmin,Zhang Yan. Active/Reactive Power Coordinated Optimization of Power System Based on Pareto Optimality[J].Automation of Electric Power Systems,2009,33(10):38-42.
[2]谢全敏,宋啸,杨文东.基于功效系数法的滑坡灾害风险评估研究[J].水利水电技术,2017,48(6):120-123.
[3]孙伟卿,王承民,张焰.电力系统综合节能的有功与无功功率协调优化[J].电机与控制学报,2010,14(7):41-47.Sun Weiqing,Wang Chengmin,Zhang Yan. Active/reactive power coordinated optimization in power systems comprehensive energy saving[J]. Electric Machines and Control,2010,14(7):41-47.
[4]胡博,谢开贵,曹侃.基于Beowulf集群的大规模电力系统牛顿法潮流求解的并行GMRES方法[J].电工技术学报,2011,26(4):145-152.Hu Bo,Xie Kaigui,Cao Kan. Parallel GMRES Techniques for Solving Newton Power Flow of Large Scale Power Systems on the Beowulf Cluster[J]. Transactions of China Electrotechnical Society,2011,26(4):145-152.
[5]孙健,江道灼.基于牛顿法的配电网络Zbus潮流计算方法[J].电网技术,2004,(15):40-44.SunJian,Jiang Daozhuo. A zbus Power flow calculation method for distributi0n network based on newton method[J]. Power System Technology,2004,(15):40-44.
[6]简金宝,杨林峰,全然.基于改进多中心校正解耦内点法的动态最优潮流并行算法[J].电工技术学报,2012,27(6):232-241.Jian Jinbao,Yang Linfeng,Quan Ran. Parallel Algorithm of Dynamic Optimal Power Flow Based on Improved Multiple Centrality Corrections Decoupling Interior Point Method[J]. Transactions of China Electrotechnical Society,2012,27(6):232-241.
[7]刘前进,许慧铭,施超.基于人工蜂群算法的多目标最优潮流问题的研究[J].电力系统保护与控制,2015,43(8):1-7.Liu Qianjin,Xu Huiming,Shi Chao. Research on power flow optimization based on multi-objective artificial bee colony algorithm[J]. Power System Protection and Control,2015,43(8):1-7.
[8]周海忠,周步祥,何春渝.基于蜜蜂双种群进化型云自适应遗传算法的电力系统多目标无功优化[J].电测与仪表,2016,53(5):103-108.Zhou Haizhong, Zhou Buxiang,He Chunyu. Multi-objective reactive power optimization for power system based on double bee population evolutionary cloud adaptive genetic algorithm[J]. Electrical Measurement&Instrumentation,2016,53(5):103-108.
[9]王秀云,赵宇,马万明.改进粒子群算法在无功优化中的应用[J].电测与仪表,2015,52(15):108-112.Wang Xiuyun,Zhao Yu,Wanming. Application of improved particle swarm algorithm in reactive power optimization[J]. Electrical Measurement&Instrumentation,2015,52(15):108-112.
[10]刘先正,温家良,潘艳.采用改进粒子群算法的直流电网最优潮流控制[J].电网技术,2017,41(3):715-720.Liu Xianzheng,Wen Jialiang,Pan Yan. OPF Control of DC-Grid Using Improved PSO Algorithm[J]. Power System Technology,2017,41(3):715-720.
[2]谢全敏,宋啸,杨文东.基于功效系数法的滑坡灾害风险评估研究[J].水利水电技术,2017,48(6):120-123.
[3]孙伟卿,王承民,张焰.电力系统综合节能的有功与无功功率协调优化[J].电机与控制学报,2010,14(7):41-47.Sun Weiqing,Wang Chengmin,Zhang Yan. Active/reactive power coordinated optimization in power systems comprehensive energy saving[J]. Electric Machines and Control,2010,14(7):41-47.
[4]胡博,谢开贵,曹侃.基于Beowulf集群的大规模电力系统牛顿法潮流求解的并行GMRES方法[J].电工技术学报,2011,26(4):145-152.Hu Bo,Xie Kaigui,Cao Kan. Parallel GMRES Techniques for Solving Newton Power Flow of Large Scale Power Systems on the Beowulf Cluster[J]. Transactions of China Electrotechnical Society,2011,26(4):145-152.
[5]孙健,江道灼.基于牛顿法的配电网络Zbus潮流计算方法[J].电网技术,2004,(15):40-44.SunJian,Jiang Daozhuo. A zbus Power flow calculation method for distributi0n network based on newton method[J]. Power System Technology,2004,(15):40-44.
[6]简金宝,杨林峰,全然.基于改进多中心校正解耦内点法的动态最优潮流并行算法[J].电工技术学报,2012,27(6):232-241.Jian Jinbao,Yang Linfeng,Quan Ran. Parallel Algorithm of Dynamic Optimal Power Flow Based on Improved Multiple Centrality Corrections Decoupling Interior Point Method[J]. Transactions of China Electrotechnical Society,2012,27(6):232-241.
[7]刘前进,许慧铭,施超.基于人工蜂群算法的多目标最优潮流问题的研究[J].电力系统保护与控制,2015,43(8):1-7.Liu Qianjin,Xu Huiming,Shi Chao. Research on power flow optimization based on multi-objective artificial bee colony algorithm[J]. Power System Protection and Control,2015,43(8):1-7.
[8]周海忠,周步祥,何春渝.基于蜜蜂双种群进化型云自适应遗传算法的电力系统多目标无功优化[J].电测与仪表,2016,53(5):103-108.Zhou Haizhong, Zhou Buxiang,He Chunyu. Multi-objective reactive power optimization for power system based on double bee population evolutionary cloud adaptive genetic algorithm[J]. Electrical Measurement&Instrumentation,2016,53(5):103-108.
[9]王秀云,赵宇,马万明.改进粒子群算法在无功优化中的应用[J].电测与仪表,2015,52(15):108-112.Wang Xiuyun,Zhao Yu,Wanming. Application of improved particle swarm algorithm in reactive power optimization[J]. Electrical Measurement&Instrumentation,2015,52(15):108-112.
[10]刘先正,温家良,潘艳.采用改进粒子群算法的直流电网最优潮流控制[J].电网技术,2017,41(3):715-720.Liu Xianzheng,Wen Jialiang,Pan Yan. OPF Control of DC-Grid Using Improved PSO Algorithm[J]. Power System Technology,2017,41(3):715-720.