含大规模屋顶光伏电站接入农村配电网多目标优化配置方法
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摘要
针对大规模屋顶光伏电站接入农村配电网无序分布带来的分布式发电资源浪费等问题,以屋顶光伏电站建设投资成本最小化和系统网络损耗最小化为优化目标,综合考虑农村地区安装面积限制和配电网运行约束,构建了含大规模屋顶光伏电站接入农村配电网多目标优化配置模型,采用针对高维度解改进的带精英策略的非支配排序算法(NSGA-II)对配置模型进行优化,针对算法求解得到的Pareto解集,应用模糊贴近度进行筛选,得出最优方案。通过IEEE-33节点配电系统算例仿真分析,结果表明:所提出的配置方法可以在提高屋顶光伏电站投资经济性的同时,利用屋顶光伏电站的优化配置提高系统的供电可靠性。
Aiming at the problem of the waste of distributed generation resources caused by disorderly distribution of largescale rooftop PV power stations connected to the rural distribution network,a multi-objective optimization configuration mode of large-scale roof PV power stations accessing to rural distribution network with comprehensive consideration of restrictions of installation area in rural region and operation constraints of distribution network,aiming to minimize the investment cost of rooftop PV power stations and minimize the loss of system network,is established in this paper. A kind of improved elitist non-dominated sorting genetic algorithm( NSGA-Ⅱ) based on high-dimensional solutions for the optimization configuration model is proposed and the best configuration proposal is selected from Pareto solution set by applying fuzzy nearness. The proposed configuration method can improve the reliability of power supply and the economy of investment by using the optimization of rooftop PV power stations through simulation and analysis of distribution network example of IEEE-33 node.
Aiming at the problem of the waste of distributed generation resources caused by disorderly distribution of largescale rooftop PV power stations connected to the rural distribution network,a multi-objective optimization configuration mode of large-scale roof PV power stations accessing to rural distribution network with comprehensive consideration of restrictions of installation area in rural region and operation constraints of distribution network,aiming to minimize the investment cost of rooftop PV power stations and minimize the loss of system network,is established in this paper. A kind of improved elitist non-dominated sorting genetic algorithm( NSGA-Ⅱ) based on high-dimensional solutions for the optimization configuration model is proposed and the best configuration proposal is selected from Pareto solution set by applying fuzzy nearness. The proposed configuration method can improve the reliability of power supply and the economy of investment by using the optimization of rooftop PV power stations through simulation and analysis of distribution network example of IEEE-33 node.
引文
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[2]张宇,白建波,曹阳.积灰对屋顶光伏电站性能的影响[J].可再生能源,2013,31(11):9-12.Zhang Yu,Bai Jianbo,Cao Yang. Influence of dust deposition on the performance of rooftop solar power stations[J]. Renewable Energy Resources,2013,31(11):9-12.
[3]陈毅湛,陈惠秋,冼丽娴,等.基于高效PERC太阳电池屋顶光伏发电系统的设计及能效分析[J].硅酸盐通报,2017,(s1).Chen Yizhan,Chen Huiqiu,Xian Lixian,et al. Design and Energy Efficiency Analysis of Roof Photovoltaic System Based on High Efficiency PERC Solar Cells[J]. Bulletin of the Chinese Ceramic Society,2017,(s1).
[4]刘振东,张石定,贾晗.安阳市并网光伏系统案例经济和环境效益分析[J].中国电力,2013,46(8):43-47.Liu Zhendong,Zhang Shiding,Jia Han. Case Study on Economic and Environmental Benefits of Grid-Connected Photovoltaic Systems[J]. ELECTRIC POWER,2013,46(8):43-47.
[5]谢东,刘慧,张籍,等.小规模分布式光伏发电系统用户侧经济性分析[J].可再生能源,2015,33(5):736-740.Xie Dong,Liu Hui,Zhang Ji,et al. User-side economic analysis of small-scale distributed PV generation systems[J]. Renewable Energy Resources,2015,33(5):736-740.
[6]乔虹桥,冯相赛,张海磊,等.民用光伏发电系统经济效益分析[J].能源与节能,2016,(2):100-103.Qiao Hongqiao,Feng Xiangsai,Zhang Hailei,et al. Economic Benefit Analysis of Civilian Photovoltaic Power Generation Systems[J]. Energy and Energy Conservation,2016,(2):100-103.
[7]陈罡,陶顺,骆晨,等.一种分布式电源选址定容的两阶段优化规划方法[J].电测与仪表,2016,53(19):93-99.Chen Gang,Tao Shun,Luo Chen,et al. A two-stage approach for sitting and sizing problem of distributed generation[J]. Electrical Measurement&Instrumentation,2016,53(19):93-99.
[8]胡福年,张访,葛苗苗,等.考虑峰谷分时电价和时序特性的分布式电源选址定容[J].电测与仪表,2016,53(13):112-117.Hu Funian,Zhang Fang,Ge Miaomiao,et al. Optimal site selection and capacity determination of distributed generations considering time of use price and timing characteristics[J]. Electrical Measurement&Instrumentation,2016,53(13):112-117.
[9]赵永强,王春芳,周钊正,等.小型光伏电站成本回收问题的研究[J].太阳能学报,2017,38(6):1584-1591.Zhao Yongqiang,Wang Chunfang,Zhou Zhaozheng,et al. Cost Recovery Study Of Small-Sized PV Generation Plant[J]. Acta Energiae Solaris Sinica,2017,38(6):1584-1591.
[10]Xing W U,Liu T,Xingyuan L I,et al. Optimal Configuration of PMU Based on Data Compatibility of WAMS/SCADA and Improved FCM Clustering Algorithm[J]. Power System Technology,2014,202(3):756-761.
[11]张利. NSGA2算法及其在电力系统稳定器参数优化中的应用[D].西南交通大学,2013.Zhang Li. NSGA2 Algorithm And ITS Application In Optimizing Power System Stablizers Parameters[D]. Southwest Jiaotong University,2013.
[12]戴光明,王茂才.多目标优化算法及在卫星星座设计中的应用[M].中国地质大学出版社,2009.