基于遗传算法的输电线路覆冰灾害应急响应物资储备决策优化模型
|
摘要
为研究在输电线路覆冰情况下如何合理有效地储备应急物资,基于遗传算法最优决策理论,提出了1种输电线路覆冰灾害下应急响应物资储备模型。根据覆冰灾害的预测情况,计算出每月应急物资的需求量,以每月的采购量为决策变量,以最小储存成本为目标,得出最优储备方案,同时用动态规划对此模型进行求解,对2种寻优方法结果进行对比。以云南某供电局的冬季应急物资储备为例验证模型的有效性,其结果有助于提高电网部门应对冰冻灾害的能力和经济效益。
In order to study how to reserve the emergency material reasonably and effectively in the icing situation of power transmission line,a model of emergency response material reserve in the icing disaster of power transmission line was put forward based on the optimal decision-making theory of genetic algorithm. The demand of emergency material per month was calculated according to the prediction of icing disaster,and the optimal reserve scheme was obtained by taking the monthly purchase volume as the decision-making variable and taking the minimum reserve cost as the goal. Meanwhile,the model was solved by the dynamic programming,and the results of two optimizing methods were compared. The effectiveness of the model was verified by taking the emergency material reserve of a power supply bureau in Yunnan during the winter as an example.The results contribute to improve the capability and economic effect of the power grid departments to deal with the freezing disaster.
In order to study how to reserve the emergency material reasonably and effectively in the icing situation of power transmission line,a model of emergency response material reserve in the icing disaster of power transmission line was put forward based on the optimal decision-making theory of genetic algorithm. The demand of emergency material per month was calculated according to the prediction of icing disaster,and the optimal reserve scheme was obtained by taking the monthly purchase volume as the decision-making variable and taking the minimum reserve cost as the goal. Meanwhile,the model was solved by the dynamic programming,and the results of two optimizing methods were compared. The effectiveness of the model was verified by taking the emergency material reserve of a power supply bureau in Yunnan during the winter as an example.The results contribute to improve the capability and economic effect of the power grid departments to deal with the freezing disaster.
引文
[1]孙才新.重视和加强防止复杂气候环境及输变电设备故障导致电网大面积事故的安全技术研究[J].中国电力,2004,37(6):1-8.SUN Caixin. Pay attention to and strengthen the safety technology research to prevent power grid large-area accidents caused by complex climate environment and power transmission and transformation equipment failure[J]. China Power,2004,37(6):1-8.
[2]王守礼.影响电线覆冰因素的研究与分析[J].电网技术,1994,18(4):18-24.WANG Shouli. Research and analysis of factors affecting wire ice coating[J]. Power grid technology,1994,18(4):18-24.
[3]林韩.电网防灾减灾应急管理系统建设与应用[M].北京:国防工业出版社,2009
[4]胡毅.输电线路大范围冰害事故分析及对策[J].高电压技术,2005,31(4):14-15.HU Yi. Analysis and countermeasures of large-scale ice damage accidents on transmission lines[J]. High Voltage Engineering,2005,31(4):14-15.
[5]蒋兴良,易辉.输电线路覆冰及防护[M].北京:中国电力出版社,2002
[6]袁仲熊,张斌杰.电网设备抢修备品备件库存模型[J].华东电力,2012,11(40):1888-1890.YUAN Zhongxiong,ZHANG Binjie. Grid equipment repair spare parts inventory model[J]. East China Power,2012,11(40):1888-1890.
[7]李世停,朱波.基于可靠性的舰船随行维修备件最优储备决策研究[J].中国船修,2005(1):36-38.LI Shiting,ZHU Bo. Research on optimal reserve decision of ships’maintenance spare parts based on reliability[J]. Chinese Ship Repair,2005(1):36-38.
[8]吴在栋,林广发.突发河流污染事件应急资源调度动态规划模型研究[J].地球信息科学,2018,20(6):800-804.WU Zaidong,LIN Guangfa. Research on dynamic planning model of emergency resource dispatching for sudden river pollution events[J]. Earth Information Science,2018,20(6):800-804.
[9] AYUSH JAIN,GANESHK. RAO. Memetic algorithm to optimize level of repair and spare part decisions for fleet system[C]//IEEE International Conference on Industrial Engineering and Management,Singapore,2017.
[10] REN X. Spare parts varieties level configuration based on Grey Situation decision[C]//The 26 th Chinese Control and Decision Conference,Changsha.
[11]赵洪山,刘宏杨.风电机组大部件的备品备件区域库存优化控制策略[J].可再生能源,2018,36(3):422-425.ZHAO Hongshan,LIU Hongyang. Spare parts area inventory optimization control strategy for large components of wind turbines[J].Renewable Energy,2018,36(3):422-425.
[12]刘皓,胡明昕.基于遗传算法和支持向量机回归的锂电池健康状态预测[J].南京理工大学学报,2018,42(3):330-334.LIU Hao,HU Mingxin. Prediction of health status of lithium batteries based on genetic algorithm and support vector machine regression[J]. Journal of Nanjing University of Science and Technology,2018,42(3):330-334.
[13]毛云英.动态系统与最优控制[M].北京:高等教育出版社,1994
[2]王守礼.影响电线覆冰因素的研究与分析[J].电网技术,1994,18(4):18-24.WANG Shouli. Research and analysis of factors affecting wire ice coating[J]. Power grid technology,1994,18(4):18-24.
[3]林韩.电网防灾减灾应急管理系统建设与应用[M].北京:国防工业出版社,2009
[4]胡毅.输电线路大范围冰害事故分析及对策[J].高电压技术,2005,31(4):14-15.HU Yi. Analysis and countermeasures of large-scale ice damage accidents on transmission lines[J]. High Voltage Engineering,2005,31(4):14-15.
[5]蒋兴良,易辉.输电线路覆冰及防护[M].北京:中国电力出版社,2002
[6]袁仲熊,张斌杰.电网设备抢修备品备件库存模型[J].华东电力,2012,11(40):1888-1890.YUAN Zhongxiong,ZHANG Binjie. Grid equipment repair spare parts inventory model[J]. East China Power,2012,11(40):1888-1890.
[7]李世停,朱波.基于可靠性的舰船随行维修备件最优储备决策研究[J].中国船修,2005(1):36-38.LI Shiting,ZHU Bo. Research on optimal reserve decision of ships’maintenance spare parts based on reliability[J]. Chinese Ship Repair,2005(1):36-38.
[8]吴在栋,林广发.突发河流污染事件应急资源调度动态规划模型研究[J].地球信息科学,2018,20(6):800-804.WU Zaidong,LIN Guangfa. Research on dynamic planning model of emergency resource dispatching for sudden river pollution events[J]. Earth Information Science,2018,20(6):800-804.
[9] AYUSH JAIN,GANESHK. RAO. Memetic algorithm to optimize level of repair and spare part decisions for fleet system[C]//IEEE International Conference on Industrial Engineering and Management,Singapore,2017.
[10] REN X. Spare parts varieties level configuration based on Grey Situation decision[C]//The 26 th Chinese Control and Decision Conference,Changsha.
[11]赵洪山,刘宏杨.风电机组大部件的备品备件区域库存优化控制策略[J].可再生能源,2018,36(3):422-425.ZHAO Hongshan,LIU Hongyang. Spare parts area inventory optimization control strategy for large components of wind turbines[J].Renewable Energy,2018,36(3):422-425.
[12]刘皓,胡明昕.基于遗传算法和支持向量机回归的锂电池健康状态预测[J].南京理工大学学报,2018,42(3):330-334.LIU Hao,HU Mingxin. Prediction of health status of lithium batteries based on genetic algorithm and support vector machine regression[J]. Journal of Nanjing University of Science and Technology,2018,42(3):330-334.
[13]毛云英.动态系统与最优控制[M].北京:高等教育出版社,1994