风光互补独立供电系统的多目标优化设计
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摘要
摘要：在风光互补独立供电系统的设计中,如何配置风力发电机、太阳能电池板和蓄电池,在满足负荷需求的前提下,使风能和太阳能这样的清洁能源得到充分利用,负荷的供电可靠性较高,而系统成本较低,这是一个多目标优化设计的问题。
本文就是针对这样的风光互补独立供电系统,进行多目标的优化配置。首先建立了影响光伏发电和风力发电的天气模型,并使用了尖峰负荷小时(PSH)的方法和威布尔分布的方法分别模拟太阳能辐射和风速状况,采用蒙特卡罗仿真对天气情况进行仿真计算；其次建立了风力发电、光伏发电和蓄电池储能的模型,然后针对风光互补供电系统的研究重点,本文提出了衡量供电可靠性的失负荷概率LOLP、衡量清洁能源浪费的清洁能源浪费概率LOEP和系统成本三个指标,把它们作为多目标问题的优化目标。
为了解决风光互补独立供电系统中提出的多目标优化问题,本文提出了一种混沌自适应进化算法(CSEA),新算法的混沌初始种群算子提高了初代种群的多样性,分组选择策略保证了各代中一定数量的劣势个体可以参与进化,自适应遗传算子增加了劣势个体的交叉和变异概率,从而避免算法早熟,增强了算法的全局搜索能力。算例表明,CSEA算法比传统单目标遗传算法的结果更加接近实际运行的Pareto优化前端,综合效果更优。
另外,本文运用CSEA算法对风光互补独立供电方式与纯光伏、纯风力独立供电方式进行比较,证实了风光互补独立供电方式在经济性和可靠性等方面更为合理。
可见,采用混沌自适应进化算法进行风光互补独立供电系统的优化设计,对于提高供电系统可靠性,降低成本,减少能源浪费具有非常重要的意义。
ABSTRACT:In optimal sizing of stand-alone hybrid wind/PV system, how to make more full use of wind and solar energy source, and how to configure PV modules, wind generators and batteries based on the load demand with higher reliability, lower system cost and lower lost of clean energy is a multi-objective optimization problem in nature.
In this paper, weather modeling which is build at fist is one of the most important factors. Peak-Sun-Hours method is used to calculate solar radiation data, while the weibull distribution is used to simulate the wind speed. Monte Carlo method is used to simulate weather condition after considering the random condition. Then wind generators and PV Systems are modeling, LOLP and LOEP indices which reflect the reliability level and the lost of clean energy are proposed and calculated through Monte Carlo simulation.
To solve the multi-objective optimization problem, a so called CSEA Multi-Objective Evolutionary Algorithm has been proposed in detail. Simulation results show that, The Chaotic Initial Generation helps to improve the initial diversity, the Grouping Selection Strategy and Self-adaptive Genetic Operator help to avoid pre-maturity and enhance the global searching ability of the algorithm. Comparisons with the single-objective optimization show that CSEA outperforms GA in terms of diversity preservation and in converging closer to the pareto-optimal frontier in one-run-time.
Photovoltaic system and wind power system are compared with the hybrid wind/PV system in double-objective evolutionary algorithm. The result shows that stand-alone hybrid wind/PV system is more reasonable comparing with PV system or wind farm in power system.
Application of CSEA on the Optimal Sizing of Stand-alone hybrid wind/PV system can improve system reliability, reduce the cost and energy waste, which has great significance.
本文就是针对这样的风光互补独立供电系统,进行多目标的优化配置。首先建立了影响光伏发电和风力发电的天气模型,并使用了尖峰负荷小时(PSH)的方法和威布尔分布的方法分别模拟太阳能辐射和风速状况,采用蒙特卡罗仿真对天气情况进行仿真计算；其次建立了风力发电、光伏发电和蓄电池储能的模型,然后针对风光互补供电系统的研究重点,本文提出了衡量供电可靠性的失负荷概率LOLP、衡量清洁能源浪费的清洁能源浪费概率LOEP和系统成本三个指标,把它们作为多目标问题的优化目标。
为了解决风光互补独立供电系统中提出的多目标优化问题,本文提出了一种混沌自适应进化算法(CSEA),新算法的混沌初始种群算子提高了初代种群的多样性,分组选择策略保证了各代中一定数量的劣势个体可以参与进化,自适应遗传算子增加了劣势个体的交叉和变异概率,从而避免算法早熟,增强了算法的全局搜索能力。算例表明,CSEA算法比传统单目标遗传算法的结果更加接近实际运行的Pareto优化前端,综合效果更优。
另外,本文运用CSEA算法对风光互补独立供电方式与纯光伏、纯风力独立供电方式进行比较,证实了风光互补独立供电方式在经济性和可靠性等方面更为合理。
可见,采用混沌自适应进化算法进行风光互补独立供电系统的优化设计,对于提高供电系统可靠性,降低成本,减少能源浪费具有非常重要的意义。
ABSTRACT:In optimal sizing of stand-alone hybrid wind/PV system, how to make more full use of wind and solar energy source, and how to configure PV modules, wind generators and batteries based on the load demand with higher reliability, lower system cost and lower lost of clean energy is a multi-objective optimization problem in nature.
In this paper, weather modeling which is build at fist is one of the most important factors. Peak-Sun-Hours method is used to calculate solar radiation data, while the weibull distribution is used to simulate the wind speed. Monte Carlo method is used to simulate weather condition after considering the random condition. Then wind generators and PV Systems are modeling, LOLP and LOEP indices which reflect the reliability level and the lost of clean energy are proposed and calculated through Monte Carlo simulation.
To solve the multi-objective optimization problem, a so called CSEA Multi-Objective Evolutionary Algorithm has been proposed in detail. Simulation results show that, The Chaotic Initial Generation helps to improve the initial diversity, the Grouping Selection Strategy and Self-adaptive Genetic Operator help to avoid pre-maturity and enhance the global searching ability of the algorithm. Comparisons with the single-objective optimization show that CSEA outperforms GA in terms of diversity preservation and in converging closer to the pareto-optimal frontier in one-run-time.
Photovoltaic system and wind power system are compared with the hybrid wind/PV system in double-objective evolutionary algorithm. The result shows that stand-alone hybrid wind/PV system is more reasonable comparing with PV system or wind farm in power system.
Application of CSEA on the Optimal Sizing of Stand-alone hybrid wind/PV system can improve system reliability, reduce the cost and energy waste, which has great significance.
引文
[1]Mukund R.Patel.姜齐荣,张春鹏,李虹.风能与太阳能发电系统——设计、分析与运行.第二版.北京.机械工业出版社.2009.3-4.
[2]殷志强.中国可再生能源发展战略研究丛书——太阳能卷.北京.中国电力出版社.2008.7-8.
[3]李代广.风与风能.北京.北京化工出版社.2009.36-37.
[4]刘山凤,龙江,方韬.风光互补新能源成新趋势.电气技术.2008年.
[5]李爽.风/光互补混合发电系统优化设计[学位论文].北京.中国科学院电工研究所.2001.
[6]沈辉,曾祖勤.太阳能光伏发电技术.北京.化学工业出版社.2005.
[7]贺德馨.中国可再生能源发展战略研究丛书——风能卷.北京.中国电力出版社.2008.11-14.
[8]中国电力企业联合会.中国风电年内有望跃居世界第二.北京.中国电力企业联合学会.2009.
[9]张东凤.离网型风光互补发电系统的匹配与效益分析—以金湖县宝应湖为例[学位论文].南京.南京农业大学.2006.
[10]王宇.风光互补发电控制系统的研究与开发[学位论文].天津.天津大学.2004.
[11]郑锁珍.风光互补发电时最合理的独立电源系统.机械管理开发.2008.
[12]A.G.Bhave. Hybrid solar-wind domestic power generating system-a case study. Renewable Energy.1999(17).355-358.
[13]Bogdan S.Borowy, Ziyad M. Salameh. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transaction on Energy Conversion.1996.11(2).367-375.
[14]Ai B, Yang H, Shen H, Liao X. Computer aided design of PV/wind hybrid system.Renewable Energy.2003.(28) 1491-1512.
[15]艾斌,杨洪兴,沈辉,廖显伯.风光互补发电系统的优化设计(1)CAD设计方法.太阳能学报.2003.24.1-7.
[16]徐大明,康龙云,曹秉刚.风光互补独立供电系统的优化设计.太阳能学报.2006.27.1-3.
[17]A.H.Shahirinia, S.M.M.Tafreshi, A.Hajizadeh Gastaj, A.R.Moghaddamjoo, Optimal Sizing of Hybrid Power System Using Genetic Algorithm,2005 International Conference on Future Power Systems,16-18 Nov.2005, pp.6.
[18]谢可新,韩健,林友联.最优化方法.第二版.天津.天津大学出版社.2008.100-123.
[19]崔逊学.多目标进化算法及其应用.北京.国防工业出版社.2006.
[20]姜祥元.太阳能路灯的设计与应用.福建建筑.2008.7.
[21]北京科诺伟业科技有限公司,国奥投资发展有限公司.国家体育馆太阳能发电技术.建筑科技.2008.13.
[22]董伟.美国推出屋顶风力发电机.能源研究与信息.2004.20.
[23]揭婷,段善旭,刘邦银.风光互补发电系统的能量管理研究.变频器世界.2008.9.
[24]李德孚.户用“风-光”发电系统技术与应用.农业工程学报.2006.10.
[25]曾叶凡,刘志峰,张爱平.光伏发电系统最大输出效率探索.制造自动化.2001.9.
[26]狄丹.太阳能光伏发电是理想的可再生能源.华中电力.2008.5.
[27]Anca D.Hansen,Poul Sorensen,Lars H.Hansen,Henrik Bindner.Models for a Stand-Alone PV System.Riso National Laboratory,Roskilde.2000.12.
[28]张书元,赵秀田.太阳能光伏发电系统及其电池技术研究.2004.8.
[29]孙耀杰.分布式电源中风力发电的建模与控制.西安.西安交通大学.2003.
[30]左巍.分布式发电系统中风能和太阳能的模拟.西安.西安交通大学.2004.
[31]Darning Xu, Longyun Kang, Liuchen Chang. Optimal sizing of standalone hybrid wind/PV power systems using genetic algorithms. Electrical and Computer Engineering 2005.1722-1725.
[32]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究.中国电机工程学报.2005.6.
[33]Rachid Belfkira, Omessad Hajji, Cristian Nichita, Georges Barakat. Optimal sizing of stand-alone hybrid wind/PV system with battery storage.Power Electronics and Applications. 2007.1-10.
[34]钟勇.风光互补发电系统中蓄电池充放电控制器的研究.学位论文.合肥工业大学.2006.
[35]G.B.Shrestha, L Goel. A study on optimal sizing of stand-alone photovoltaic stations. Energy Conversion.1998.373-378.
[36]IEEE Standards Coordinating Committee 21 on Fuel Cells, Photovoltaics, Dispersed Generation, and Energy Storage. IEEE Guide for Array and Battery Sizing in Stand-Alone Photovoltaic (PV) Systems. IEEE Std.2008.
[37]K.Deb. Multiobjective Optimization Using Evolutionary Algorithms. Chichester.2001.
[38]王建字,周春光,郭东伟,马或.基于Rank的进化算法解决多目标TSP问题[J].计算机工程与科学.2008.2.
[39]樊纪山,刘冠蓉,王鲁,时忠伟.一种改进快速稳定的多目标优化算法[J].计算机应用研究.2007.4.
[40]井祥鹤,魏冬峰,周献中.运输方式选择多目标优化问题的混合遗传算法[J].计算机工程与应用.2008.44(6).
[41]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用.北京.科学出版社.2002.
[42]周明,孙树栋.遗传算法原理与应用.北京.国防工业出版社.1999.
[43]J. Knowles and D. Corne. The Pareto archived evolution strategy:A new baseline algorithm for multiobjective optimization. Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway. NJ. IEEE Press.1999.98-105.
[44]E. Zitzler. Evolutionary algorithms for multiobjective optimization, Methods and applications. Doctoral dissertation ETH 13398. Swiss Federal Institute of Technology. Zurich. Switzerland. 1999.
[45]N.Srinivas and K.Deb. Multiobjective function optimization using nondominated sorting genetic algorithms. IEEE Transactions on Evolutionary Computation.1995.2(3).221-248.
[46]K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-Ⅱ. IEEE Transactions on Evolutionary Computation.2002.6(2).
[47]Aguirre A H, Rionda S B, Coello C A. Handling Constraints using Multiobjective Optimization Concepts[J].Civil Environmental System.2000.17.
[48]刘海林,王宇平,刘永清.带约束多目标最优化问题的一种新的进化算法[J].计算机科学.2002.29(7).
[49]王志良,邱林,付强.混沌优化算法在非线性约束规划问题中的应用.华北水利水电学院学报.2002.23(2).
[50]张成,李影,邢伟.基于适应度分组的进化策略.系统仿真学报.2007.19(21).
[51]刘勇,张晓红.遗传算法的多目标优化资源选择算法.火力与指挥控制.2008.33(2).
[2]殷志强.中国可再生能源发展战略研究丛书——太阳能卷.北京.中国电力出版社.2008.7-8.
[3]李代广.风与风能.北京.北京化工出版社.2009.36-37.
[4]刘山凤,龙江,方韬.风光互补新能源成新趋势.电气技术.2008年.
[5]李爽.风/光互补混合发电系统优化设计[学位论文].北京.中国科学院电工研究所.2001.
[6]沈辉,曾祖勤.太阳能光伏发电技术.北京.化学工业出版社.2005.
[7]贺德馨.中国可再生能源发展战略研究丛书——风能卷.北京.中国电力出版社.2008.11-14.
[8]中国电力企业联合会.中国风电年内有望跃居世界第二.北京.中国电力企业联合学会.2009.
[9]张东凤.离网型风光互补发电系统的匹配与效益分析—以金湖县宝应湖为例[学位论文].南京.南京农业大学.2006.
[10]王宇.风光互补发电控制系统的研究与开发[学位论文].天津.天津大学.2004.
[11]郑锁珍.风光互补发电时最合理的独立电源系统.机械管理开发.2008.
[12]A.G.Bhave. Hybrid solar-wind domestic power generating system-a case study. Renewable Energy.1999(17).355-358.
[13]Bogdan S.Borowy, Ziyad M. Salameh. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transaction on Energy Conversion.1996.11(2).367-375.
[14]Ai B, Yang H, Shen H, Liao X. Computer aided design of PV/wind hybrid system.Renewable Energy.2003.(28) 1491-1512.
[15]艾斌,杨洪兴,沈辉,廖显伯.风光互补发电系统的优化设计(1)CAD设计方法.太阳能学报.2003.24.1-7.
[16]徐大明,康龙云,曹秉刚.风光互补独立供电系统的优化设计.太阳能学报.2006.27.1-3.
[17]A.H.Shahirinia, S.M.M.Tafreshi, A.Hajizadeh Gastaj, A.R.Moghaddamjoo, Optimal Sizing of Hybrid Power System Using Genetic Algorithm,2005 International Conference on Future Power Systems,16-18 Nov.2005, pp.6.
[18]谢可新,韩健,林友联.最优化方法.第二版.天津.天津大学出版社.2008.100-123.
[19]崔逊学.多目标进化算法及其应用.北京.国防工业出版社.2006.
[20]姜祥元.太阳能路灯的设计与应用.福建建筑.2008.7.
[21]北京科诺伟业科技有限公司,国奥投资发展有限公司.国家体育馆太阳能发电技术.建筑科技.2008.13.
[22]董伟.美国推出屋顶风力发电机.能源研究与信息.2004.20.
[23]揭婷,段善旭,刘邦银.风光互补发电系统的能量管理研究.变频器世界.2008.9.
[24]李德孚.户用“风-光”发电系统技术与应用.农业工程学报.2006.10.
[25]曾叶凡,刘志峰,张爱平.光伏发电系统最大输出效率探索.制造自动化.2001.9.
[26]狄丹.太阳能光伏发电是理想的可再生能源.华中电力.2008.5.
[27]Anca D.Hansen,Poul Sorensen,Lars H.Hansen,Henrik Bindner.Models for a Stand-Alone PV System.Riso National Laboratory,Roskilde.2000.12.
[28]张书元,赵秀田.太阳能光伏发电系统及其电池技术研究.2004.8.
[29]孙耀杰.分布式电源中风力发电的建模与控制.西安.西安交通大学.2003.
[30]左巍.分布式发电系统中风能和太阳能的模拟.西安.西安交通大学.2004.
[31]Darning Xu, Longyun Kang, Liuchen Chang. Optimal sizing of standalone hybrid wind/PV power systems using genetic algorithms. Electrical and Computer Engineering 2005.1722-1725.
[32]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究.中国电机工程学报.2005.6.
[33]Rachid Belfkira, Omessad Hajji, Cristian Nichita, Georges Barakat. Optimal sizing of stand-alone hybrid wind/PV system with battery storage.Power Electronics and Applications. 2007.1-10.
[34]钟勇.风光互补发电系统中蓄电池充放电控制器的研究.学位论文.合肥工业大学.2006.
[35]G.B.Shrestha, L Goel. A study on optimal sizing of stand-alone photovoltaic stations. Energy Conversion.1998.373-378.
[36]IEEE Standards Coordinating Committee 21 on Fuel Cells, Photovoltaics, Dispersed Generation, and Energy Storage. IEEE Guide for Array and Battery Sizing in Stand-Alone Photovoltaic (PV) Systems. IEEE Std.2008.
[37]K.Deb. Multiobjective Optimization Using Evolutionary Algorithms. Chichester.2001.
[38]王建字,周春光,郭东伟,马或.基于Rank的进化算法解决多目标TSP问题[J].计算机工程与科学.2008.2.
[39]樊纪山,刘冠蓉,王鲁,时忠伟.一种改进快速稳定的多目标优化算法[J].计算机应用研究.2007.4.
[40]井祥鹤,魏冬峰,周献中.运输方式选择多目标优化问题的混合遗传算法[J].计算机工程与应用.2008.44(6).
[41]李敏强,寇纪淞,林丹.遗传算法的基本理论与应用.北京.科学出版社.2002.
[42]周明,孙树栋.遗传算法原理与应用.北京.国防工业出版社.1999.
[43]J. Knowles and D. Corne. The Pareto archived evolution strategy:A new baseline algorithm for multiobjective optimization. Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway. NJ. IEEE Press.1999.98-105.
[44]E. Zitzler. Evolutionary algorithms for multiobjective optimization, Methods and applications. Doctoral dissertation ETH 13398. Swiss Federal Institute of Technology. Zurich. Switzerland. 1999.
[45]N.Srinivas and K.Deb. Multiobjective function optimization using nondominated sorting genetic algorithms. IEEE Transactions on Evolutionary Computation.1995.2(3).221-248.
[46]K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-Ⅱ. IEEE Transactions on Evolutionary Computation.2002.6(2).
[47]Aguirre A H, Rionda S B, Coello C A. Handling Constraints using Multiobjective Optimization Concepts[J].Civil Environmental System.2000.17.
[48]刘海林,王宇平,刘永清.带约束多目标最优化问题的一种新的进化算法[J].计算机科学.2002.29(7).
[49]王志良,邱林,付强.混沌优化算法在非线性约束规划问题中的应用.华北水利水电学院学报.2002.23(2).
[50]张成,李影,邢伟.基于适应度分组的进化策略.系统仿真学报.2007.19(21).
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