基于改进遗传算法的海上风电场消纳拓扑结构优化模型
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摘要
在对海上风电场功率汇集系统进行分析的基础上,建立数学模型对其经济性和可靠性进行综合评估,提出基于改进单亲遗传算法的海上风电场功率汇集系统拓扑结构优化模型。对包含多个海上变电站的大型海上风电场提出基于范式距离最优的区域划分方法。研究结果表明:所提出的模型可以在统筹优化经济性和可靠性的同时快速获得拓扑连接优化结果,与传统的遗传算法相比收敛速度更快,优化结果更好;通过对海上风电场经济性和可靠性的权重赋值,还可得到不同权重下的拓扑结构优化结果,从而为大型海上风电场建设中的功率汇集系统拓扑优化提供参考。
Based on the analysis of the power collection system of offshore wind farm, a mathematical model was established to evaluate its economy and reliability, and a topology optimization mode of the power collection system of offshore wind farm was proposed based on the improved single parents genetic algorithm. For large offshore wind farms that contain multiple offshore substations, the optimal regional division method was also proposed based on the paradigm distance. The results show that the proposed model can consider the optimization of economy and reliability in overall rapidly and the topology optimization results can be obtained. Compared with the traditional genetic algorithm, the rate of convergence is faster and the optimization results are better. Through the weight assignment of the economy and reliability of offshore wind farm, the topology optimization results can also be obtained with different weights, which can provide reference for the design of large-scale photovoltaic power station of power collection system topology.
Based on the analysis of the power collection system of offshore wind farm, a mathematical model was established to evaluate its economy and reliability, and a topology optimization mode of the power collection system of offshore wind farm was proposed based on the improved single parents genetic algorithm. For large offshore wind farms that contain multiple offshore substations, the optimal regional division method was also proposed based on the paradigm distance. The results show that the proposed model can consider the optimization of economy and reliability in overall rapidly and the topology optimization results can be obtained. Compared with the traditional genetic algorithm, the rate of convergence is faster and the optimization results are better. Through the weight assignment of the economy and reliability of offshore wind farm, the topology optimization results can also be obtained with different weights, which can provide reference for the design of large-scale photovoltaic power station of power collection system topology.
引文
[1]汤广福,罗湘,魏晓光.多端直流输电与直流电网技术[J].中国电机工程学报,2013,33(10):8-17.TANG Guangfu,LUO Xiang,WEI Xiaoguang.Multi-terminal HVDC and DC-grid technology[J].Proceedings of the CSEE,2013,33(10):8-17.
[2]杜燕飞,王静.可再生能源发展“十三五”规划[EB/OL].[2016-12-19].http://energy.people.com.cn/n1/2016/1219/c71661-28959415.html.DU Yanfei,WANG Jing.“13th five-year plan”of renewable energy development[EB/OL].[2016-12-19].http://energy.people.com.cn/n1/2016/1219/c71661-28959415.html.
[3]HUANG Lingling,CHEN Ning,ZHANG Hongyue,et al.Optimization of large-scale offshore wind farm electrical collection systems based on improved FCM[C]//Sustainable Power Generation and Supply(SUPERGEN 2012)International Conference.Hangzhou,China,2012:1-6.
[4]GONZALEZ-LONGATT F M.Optimal offshore wind farms'collector design based on the multiple travelling salesman problem and genetic algorithm[C]//IEEE PowerTech.Grenoble,France,2013:1-6.
[5]黄玲玲,符杨,郭晓明.大型海上风电场电气接线方案优化研究[J].电网技术,2008,32(8):77-81.HUANG Lingling,FU Yang,GUO Xiaoming.Research on optimization of electrical connection scheme for a large offshore wind farm[J].Power System Technology,2008,32(8):77-81.
[6]LI Dongdong,HE Chao,SHU Haiyan.Optimization of electric distribution system of large offshore wind farm with improved genetic algorithm[C]//IEEE Power and Energy Society General Meeting.Pittsburgh,PA,2008:1-6.
[7]LI Dongdong,HE Chao,FU Yang.Optimization of internal electric connection system of large offshore wind farm with hybrid genetic and immune algorithm[C]//IEEE Electric Utility Deregulation and Restructuring and Power Technologies.Nanjing,2008:1-6.
[8]ZHAO M,CHEN Z,BLAABJERG F.Optimization of electrical system for offshore wind farms via genetic algorithm[J].Renewable Power Generation,2009,3(2):205-216.
[9]ZHAO Menghua,CHEN Zhe,HJERRILD J.Analysis of the behavior of genetic algorithm applied in optimization of electrical system design for offshore wind farms[C]//Proc 32nd Annu IEEE IECON.Paris,France,2006:2335-2340.
[10]HUANG Lingling,FU Yang,GUO Xiaoming.Optimization of electrical connection scheme for large offshore wind farm with genetic algorithm[C]//Proc Int Conf Sustainable Power Generation Supply(SUPERGEN).Nanjing,China,2009:1-4.
[11]DUTTA S,OVERBYE T J.Optimal wind farm collector system topology design considering total trenching length[J].IEEETransactions on Sustainable Energy,2012,3(3):339-348.
[12]LAKERVI E,HOLMES E J.配电网络规划与设计[M].范明天,张祖平,岳宗赋,译.北京:清华大学出版社,1998:35-52.LAKERVI E,HOLMES E J.Electricity distribution network design[M].FAN Mingtian,ZHANG Zuping,YUE Zongfu,translate.Beijing:Tsinghua University Press,1998:35-52.
[13]DUTTA S,OVERBYE T J.Optimal wind farm collector system topology design considering total trenching length[J].IEEETransactions on Sustainable Energy,2012,3(3):339-348.
[14]陈宁.大型海上风电场集电系统优化研究[D].上海:上海电力学院电气工程学院,2011:25-46.CHEN Ning.Optimization study of electric field of large offshore wind farm[D].Shanghai:Shanghai University of Electric Power.School of Electrical Engineering,2011:25-46.
[15]王建东,李国杰.海上风电场内部电气系统布局经济性对比[J].电力系统自动化,2009,33(11):99-103.WANG Jiandong,LI Guojie.Economic comparison of different collector networks for offshore wind farms[J].Power System Automation,2009,33(11):99-103.
[16]王锡凡,王碧阳,王秀丽,等.面向低碳的海上风电系统优化规划研究[J].电力系统自动化,2014,38(17):4-13.WANG Xifan,WANG Biyang,WANG Xiuli,et al.Study of optimal planning methods for offshore wind power systems oriented low-carbon[J].Power System Automation,2014,38(17):4-13.
[17]ZHOU Jie,ZHUO Fang,HUANG Lei,et al.Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm[J].Journal of Central South University,2015,22(9):3287-3295.
[18]肖鹏,李茂军,张军平,等.车辆路径问题的单亲遗传算法[J].计算技术与自动化,2000,19(1):26-30.XIAO Peng,LI Maojun,ZHANG Junping,et al.Single-parent genetic algorithm for vehicle routing problems[J].Computing Technology and Automation,2000,19(1):26-30.
[19]张弛,涂立,王加阳.新型蚁群算法在TSP问题中的应用[J].中南大学学报(自然科学版),2015,46(8):2944-2949.ZHANG Chi,TU Li,WANG Jiayang.Application of self-adaptive ant colony optimization in TSP[J].Journal of Central South University(Science and Technology),2015,46(8):2944-2949.
[2]杜燕飞,王静.可再生能源发展“十三五”规划[EB/OL].[2016-12-19].http://energy.people.com.cn/n1/2016/1219/c71661-28959415.html.DU Yanfei,WANG Jing.“13th five-year plan”of renewable energy development[EB/OL].[2016-12-19].http://energy.people.com.cn/n1/2016/1219/c71661-28959415.html.
[3]HUANG Lingling,CHEN Ning,ZHANG Hongyue,et al.Optimization of large-scale offshore wind farm electrical collection systems based on improved FCM[C]//Sustainable Power Generation and Supply(SUPERGEN 2012)International Conference.Hangzhou,China,2012:1-6.
[4]GONZALEZ-LONGATT F M.Optimal offshore wind farms'collector design based on the multiple travelling salesman problem and genetic algorithm[C]//IEEE PowerTech.Grenoble,France,2013:1-6.
[5]黄玲玲,符杨,郭晓明.大型海上风电场电气接线方案优化研究[J].电网技术,2008,32(8):77-81.HUANG Lingling,FU Yang,GUO Xiaoming.Research on optimization of electrical connection scheme for a large offshore wind farm[J].Power System Technology,2008,32(8):77-81.
[6]LI Dongdong,HE Chao,SHU Haiyan.Optimization of electric distribution system of large offshore wind farm with improved genetic algorithm[C]//IEEE Power and Energy Society General Meeting.Pittsburgh,PA,2008:1-6.
[7]LI Dongdong,HE Chao,FU Yang.Optimization of internal electric connection system of large offshore wind farm with hybrid genetic and immune algorithm[C]//IEEE Electric Utility Deregulation and Restructuring and Power Technologies.Nanjing,2008:1-6.
[8]ZHAO M,CHEN Z,BLAABJERG F.Optimization of electrical system for offshore wind farms via genetic algorithm[J].Renewable Power Generation,2009,3(2):205-216.
[9]ZHAO Menghua,CHEN Zhe,HJERRILD J.Analysis of the behavior of genetic algorithm applied in optimization of electrical system design for offshore wind farms[C]//Proc 32nd Annu IEEE IECON.Paris,France,2006:2335-2340.
[10]HUANG Lingling,FU Yang,GUO Xiaoming.Optimization of electrical connection scheme for large offshore wind farm with genetic algorithm[C]//Proc Int Conf Sustainable Power Generation Supply(SUPERGEN).Nanjing,China,2009:1-4.
[11]DUTTA S,OVERBYE T J.Optimal wind farm collector system topology design considering total trenching length[J].IEEETransactions on Sustainable Energy,2012,3(3):339-348.
[12]LAKERVI E,HOLMES E J.配电网络规划与设计[M].范明天,张祖平,岳宗赋,译.北京:清华大学出版社,1998:35-52.LAKERVI E,HOLMES E J.Electricity distribution network design[M].FAN Mingtian,ZHANG Zuping,YUE Zongfu,translate.Beijing:Tsinghua University Press,1998:35-52.
[13]DUTTA S,OVERBYE T J.Optimal wind farm collector system topology design considering total trenching length[J].IEEETransactions on Sustainable Energy,2012,3(3):339-348.
[14]陈宁.大型海上风电场集电系统优化研究[D].上海:上海电力学院电气工程学院,2011:25-46.CHEN Ning.Optimization study of electric field of large offshore wind farm[D].Shanghai:Shanghai University of Electric Power.School of Electrical Engineering,2011:25-46.
[15]王建东,李国杰.海上风电场内部电气系统布局经济性对比[J].电力系统自动化,2009,33(11):99-103.WANG Jiandong,LI Guojie.Economic comparison of different collector networks for offshore wind farms[J].Power System Automation,2009,33(11):99-103.
[16]王锡凡,王碧阳,王秀丽,等.面向低碳的海上风电系统优化规划研究[J].电力系统自动化,2014,38(17):4-13.WANG Xifan,WANG Biyang,WANG Xiuli,et al.Study of optimal planning methods for offshore wind power systems oriented low-carbon[J].Power System Automation,2014,38(17):4-13.
[17]ZHOU Jie,ZHUO Fang,HUANG Lei,et al.Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm[J].Journal of Central South University,2015,22(9):3287-3295.
[18]肖鹏,李茂军,张军平,等.车辆路径问题的单亲遗传算法[J].计算技术与自动化,2000,19(1):26-30.XIAO Peng,LI Maojun,ZHANG Junping,et al.Single-parent genetic algorithm for vehicle routing problems[J].Computing Technology and Automation,2000,19(1):26-30.
[19]张弛,涂立,王加阳.新型蚁群算法在TSP问题中的应用[J].中南大学学报(自然科学版),2015,46(8):2944-2949.ZHANG Chi,TU Li,WANG Jiayang.Application of self-adaptive ant colony optimization in TSP[J].Journal of Central South University(Science and Technology),2015,46(8):2944-2949.