改进遗传算法的风、光互补发电系统优化设计
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摘要
由于风、光互补发电系统的多目标优化设计模型涉及较广,不仅要充分考虑到风、光互补发电系统的输出量,同时也要考虑成本及供电可靠性,因此,建立了以经济性指标LCE,功率的最大化指标Pout max和系统可靠性指标LPSP为多目标的函数模型。为了克服遗传算法容易出现早熟和收敛慢的特点,从适应度值标定和种群多样化两方面对其提出改进。并针对上海南汇地区某用户的需求,得出光伏组件数量N_(pv)、风力机数量N_w及蓄电池数量N_b这3个非线性相关参数的最佳值,并对其进行分析研究。实例研究结果表明,改进的遗传算法对系统的优化设计是有效可行的。
The multi-objective optimization design model of the autonomous hybrid wind-PV system( HWPS) is broader. We should consider not only the output of HWPS,but also the costs and the power supply reliability.Therefore,a mathematical model on multi-objective is established in this article,based on economic indicators LCE,maximized power index Pout maxand system reliability index LPSP. In order to avoid premature and convergence of genetic algorithm,we present to optimize the fitness values and the diversity of population. According to the requirements of customer in Nanhui region in Shanghai,the three nonlinear correlation parameters including the number of PV array( N_(pv)) 、the number of wind turbine( N_w) and number of storage battery( N_b) are determined by the improved genetic algorithm,and the result is studied. The case indicates that the improved genetic algorithm is an effective method for optimization.
The multi-objective optimization design model of the autonomous hybrid wind-PV system( HWPS) is broader. We should consider not only the output of HWPS,but also the costs and the power supply reliability.Therefore,a mathematical model on multi-objective is established in this article,based on economic indicators LCE,maximized power index Pout maxand system reliability index LPSP. In order to avoid premature and convergence of genetic algorithm,we present to optimize the fitness values and the diversity of population. According to the requirements of customer in Nanhui region in Shanghai,the three nonlinear correlation parameters including the number of PV array( N_(pv)) 、the number of wind turbine( N_w) and number of storage battery( N_b) are determined by the improved genetic algorithm,and the result is studied. The case indicates that the improved genetic algorithm is an effective method for optimization.
引文
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[18]孙涛,孙文磊,何连英.基于遗传算法的风力机可靠性优化分配方法研究[J].可再生能源,2016,34(5):699-704.
[19]Dufo-López R,Cristóbal-Monreal IR,Yusta JM.Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation[J].Renewable Energy,2016,94:280-293.
[20]李绍金,周任军,周胜瑜,等.电力系统环保经济负荷分配的模糊自修正粒子群算法[J].电力系统保护与控制,2014(8):15-21.
[21]龚正宇,刘继春,武云霞,等.含风光储的多微网接入配网的联合调度策略[J].可再生能源,2014,32(11):1665-1670.
[22]Huang Jingfang,Honggang N G,Yi A N.Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid[J].IEEE Transactions on Smart Grid,2013,4(1):78-86.
[2]毛晓明,陈深,吴杰康,等.分时电价机制下含蓄电池微网的经济调度[J].电网技术,2015,39(5):1192-1197.
[3]陈洁,杨秀,朱兰,等.微网多目标经济调度[J].中国电动机工程学报,2013,33(19):57-66.
[4]林虹江,周步祥,胡庆有,等.基于风光互补电力入网的电力系统动态经济调度[J].可再生能源,2014,32(11):1671-1678.
[5]朱兰,严正,杨秀,等.计及需求侧响应的微网综合资源规划方法[J].中国电动机工程学报,2014,34(16):2621-2628.
[6]曾博,欧阳邵杰,张建华,等.考虑复杂预想场景下光伏高效利用的微电网综合规划[J].中国电动机工程学报,2014,34(25):4259-4269.
[7]Yin Zi Heng,Li Peng,Dou Pengchong,et al.Reaearch on mechanical properties of disel generator with loop shaping optimization control for islanded microgrid frequency[J].Applied Mechanics and Materials,508(2014):200-203.
[8]Liu Yongsheng,Gu Minan,Zhao Chunjiang.Economical and environmental analysis for the photovoltaic noise barrier system in Shanghai,China[C]∥Proceedings of the IASTED International Conference on Modeling,Simulation,and Identification(MSI2009).Beijing,2009:658-230.
[9]杨苏,袁越,陆丹,等.基于风速-负荷联合状态的发输电系统可靠性评估研究[J].可再生能源,2016,34(7):1024-1031.
[10]Mokheimer EMA,Al-Sharafi A,Habib MA,et al.A new study for hybrid PV/Wind off-grid power generation systems with the comparison of results from homer[J].International Journal of Green Energy,2015,12(5):526-542.
[11]Gamarra Carlos,Guerrero Josep M.Computational optimization techniques applied to microgrids planning:a review[J].Renewable and Sustainable Energy Reviews,48(2015):413-424.
[12]Sechilariu Manuela,Wang Bao Chao,Locment Fabrice.Supervision control for optimal energy cost management in DCmicrogrid:Design and simulation[J].Electrical Power and Energy System,58(2014):140-149.
[13]Hina Fathima A,Palanisamy K.Optimization in microgrids with hybrid energy system-a review[J].Renewable and Sustainable Energy Reviews,45(2015):431-446.
[14]Maleki A,Pourfayaz F.Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms[J].Solar Energy,2015,115:471-483.
[15]Belmili H,Haddadi M,Bacha S,et al.Sizing stand-alone photovoltaic–wind hybrid system:Techno-economic analysis and optimization[J].Renewable&Sustainable Energy Reviews,2014,30(2):821-832.
[16]何舜,郑毅,蔡旭,等.微网能源系统的滚动优化管理[J].电网技术,2014,38(9):2349-2355.
[17]赵媛媛,袁澎,艾芊,等.考虑拓扑约束并采用改进遗传算法的PMU优化配置[J].电网技术,2014,38(8):2063-2070.
[18]孙涛,孙文磊,何连英.基于遗传算法的风力机可靠性优化分配方法研究[J].可再生能源,2016,34(5):699-704.
[19]Dufo-López R,Cristóbal-Monreal IR,Yusta JM.Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation[J].Renewable Energy,2016,94:280-293.
[20]李绍金,周任军,周胜瑜,等.电力系统环保经济负荷分配的模糊自修正粒子群算法[J].电力系统保护与控制,2014(8):15-21.
[21]龚正宇,刘继春,武云霞,等.含风光储的多微网接入配网的联合调度策略[J].可再生能源,2014,32(11):1665-1670.
[22]Huang Jingfang,Honggang N G,Yi A N.Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid[J].IEEE Transactions on Smart Grid,2013,4(1):78-86.