基于灰靶决策和NSGA-Ⅱ的配电系统分布式电源多目标优化
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摘要
为优化分布式电源(Distributed Generation,DG)的安装位置和容量,以节点电压总偏差、DG投资运行维护费用、网损费用和购电费用为目标函数建立DG优化配置的多目标数学模型。模型中采用拉丁超立方抽样对DG和负荷的概率模型进行状态抽样,以计及DG出力和负荷的不确定性。运用带精英策略的非支配排序遗传算法(NSGA-Ⅱ)算法求解建立的模型得到Pareto解集。采用基于信息熵赋权的多目标灰靶决策算法从Pareto解集中选择最优方案。对25节点配电系统进行仿真计算,结果验证了所提方法的可行性和有效性。
In order to optimize the installation position and capacity of distributed generation( DG),a multi-objective model of DG optimal allocation is established,in which the total deviation of node voltage,the investment of DG and the cost of operation and maintenance,the cost of network loss as well as the cost of purchasing power are taken as the objective functions. The probabilistic model of DG and the load is sampled by Latin hypercube sampling for the purpose of taking into account the uncertainties of the distributed generation power output and the load. The multi-objective model is solved to obtain a Pareto solution set by non-dominated sorting genetic algorithm-Ⅱ( NSGA-Ⅱ). On the basis of information entropy weight,an algorithm of multi-objective grey target decision-making with information entropy weight is applied to choose the optimal solution from the Pareto solution set. The simulation is conducted on a 25-node distribution system. The results verify the feasibility and effectiveness of the proposed method.
In order to optimize the installation position and capacity of distributed generation( DG),a multi-objective model of DG optimal allocation is established,in which the total deviation of node voltage,the investment of DG and the cost of operation and maintenance,the cost of network loss as well as the cost of purchasing power are taken as the objective functions. The probabilistic model of DG and the load is sampled by Latin hypercube sampling for the purpose of taking into account the uncertainties of the distributed generation power output and the load. The multi-objective model is solved to obtain a Pareto solution set by non-dominated sorting genetic algorithm-Ⅱ( NSGA-Ⅱ). On the basis of information entropy weight,an algorithm of multi-objective grey target decision-making with information entropy weight is applied to choose the optimal solution from the Pareto solution set. The simulation is conducted on a 25-node distribution system. The results verify the feasibility and effectiveness of the proposed method.
引文
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[13]陈明彩,孟涛.一种主动配电网分布式电源优化配置新方法[J].水电能源科学,2015,33(10):178-181,203.Ghen Mingcai,Meng Tao.A new Optimization Algorithm for Planning of Distributed Generations in Action Distribution Network[J].Water Resources and Power,2015,33(10):178-181,203.
[14]Karaki S H,Chedid R B,Ramadan R.Probabilistic performance assessment of autonomous solar-wind energy conversion systems[J].IEEE Transactions on Energy Conversion,1999,14(3):766-772.
[15]Yu H,Chung C Y,Wong K P,Lee HW,Zhang J H.Probabilistic load flow evaluation with hybrid latin hypercube sampling and cholesky decomposition[J].IEEE Transactions on Power Systems,2009,24(2):661-667.
[16]Deb K,Pratap A,Agarwal S,Meyarivan T.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[17]杨耀红,谭攀静.基于多目标加权灰靶决策的供应链供应商选择[J].人民长江2012,43(5):102-104.Yang Yaohong,Tan Panjing.Selection of supplier in supply chains based on multi-objective weighted grey target decision[J].Yangtze River,2012,43(5):102-104.
[18]Nahman J M,Peric D M.Optimal planning of radial distribution networks by simulated annealing technique[J].IEEE Transactions on Power Systems,2008,23(2):790-795.
[2]Georgilakis P S,Hatziargyriou N D.Optimal distributed generation placement in power distribution networks:models,methods,and future research[J].IEEE Transactions on Power systems,2013,28(3):3420-3428.
[3]栗然,马慧卓,祝晋尧,等.分布式电源接入配电网多目标优化规划[J].电力自动化设备,2014,34(1):6-13.Li Ran,Ma Huizhuo,Zhu Jinyao,et al.Multi-objective optimization for DG integration into distribution system[J].Electric Power Automation Equipment,2014,34(1):6-13.
[4]唐林权,,李芝荣,李如琦,等.基于最优引导策略的分布式电源优化配置[J].现代电力,2014,31(1):23-27.Tang Linquan,Li Zhirong,Li Ruqi,et al Distributed Generation Planning Based on the Optimized Guidance Strategy[J].Modern Electric Power,2014,31(1):23-27.
[5]程晓悦,卢锦玲.考虑不确定性的分布式电源多目标优化配置[J].电力科学与工程,2014,30(11):16-21.Cheng Xiaoyue,Lu Jinling.Multi-objective Planning of Distributed Generations Considering Uncertainties[J].Electric Power Science and Engineering,2014,30(11):16-21.
[6]潘超,孟涛,蔡国伟,等.广义电源多目标优化配置与运行[J].电网技术,2015,39(12):3505-3512.Pan Chao,Meng Tao,Cai Guowei,et al.Multi-Objective Optimal Configuration and Operation of Generalized Power Sources[J].Power System Technology.2015,39(12):3505-3512.
[7]孙建龙,吴锁平,陈燕超.基于改进NSGA2算法的配电网分布式电源优化配置[J].电力建设,2014,35(2):86-90.Sun Jianlong,Wu Suoping,Chen Yanchao.Optimal Configuration of Distributed Generation in Distribution Network Based on Improved NSGA2[J].Electric Power Construction,2014,35(2):86-90.
[8]孟佳,周峰,朴在林.基于改进非支配排序遗传算法的含DG配电网优化配置[J].可再生能源,2014,32(9):1301-1305.Meng Jia,Zhou Feng,Piao Zailin.Optimal allocation of distribution network containing distributed generation based on improved NSGA-Ⅱ[J].Renewable Energy Resources,2014,32(9):1301-1305.
[9]李兰芳,马明,刘正富,等.计及不确定性和谐波畸变的分布式电源最优配置[J].电测与仪表,2014,51(23):110-114.LI Lanfang,MA Ming,LIU Zhengfu,et al.Optimal Configuration of the Distributed Generator Considering Uncertainties and Harmonic Distortion[J].Electrical Measurement&Instrumentation,2014,51(23):110-114.
[10]韩斐,杨洪耕,王佳兴,等.考虑电能质量的分布式电源优化配置[J].电测与仪表,2015,52(13):83-87.Han Fei,Yang Honggeng,Wang Jiaxing,et al.Optimal distributed generator allocation method considering power quality[J].Electrical Measurement&Instrumentation,2015,52(13):83-87.
[11]盛万兴,叶学顺,刘科研,等.基于NSGA-II算法的分布式电源与微电网分组优化配置[J].中国电机工程学报,2015,35(18):4655-4662.Sheng Wanxing,Ye Xueshun,Liu Keyan,et al.Optimal Allocation Between Distributed Generations and Microgrid Based on NSGA-II Algorithm[J].Proceedings of the CSEE,2015,35(16):4655-4662.
[12]彭春华,于蓉,孙惠娟.基于K-均值聚类多场景时序特性分析的分布式电源多目标规划[J].电力自动化设备,2015,35(10):58-65.Peng Chunhua,Yu Rong,Sun Huijuan.Multi-objective DG planning based on K-means clustering and multi-scenario timing characteristics analysis[J].Electric Power Automation Equipment,2015,35(10):58-65.
[13]陈明彩,孟涛.一种主动配电网分布式电源优化配置新方法[J].水电能源科学,2015,33(10):178-181,203.Ghen Mingcai,Meng Tao.A new Optimization Algorithm for Planning of Distributed Generations in Action Distribution Network[J].Water Resources and Power,2015,33(10):178-181,203.
[14]Karaki S H,Chedid R B,Ramadan R.Probabilistic performance assessment of autonomous solar-wind energy conversion systems[J].IEEE Transactions on Energy Conversion,1999,14(3):766-772.
[15]Yu H,Chung C Y,Wong K P,Lee HW,Zhang J H.Probabilistic load flow evaluation with hybrid latin hypercube sampling and cholesky decomposition[J].IEEE Transactions on Power Systems,2009,24(2):661-667.
[16]Deb K,Pratap A,Agarwal S,Meyarivan T.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[17]杨耀红,谭攀静.基于多目标加权灰靶决策的供应链供应商选择[J].人民长江2012,43(5):102-104.Yang Yaohong,Tan Panjing.Selection of supplier in supply chains based on multi-objective weighted grey target decision[J].Yangtze River,2012,43(5):102-104.
[18]Nahman J M,Peric D M.Optimal planning of radial distribution networks by simulated annealing technique[J].IEEE Transactions on Power Systems,2008,23(2):790-795.