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WANG Xiaohua, ZHANG Jiancheng, ZHOU Chunxia, et al. Research on Scheduling Controlling Method for Grid-connectedPV Power System[J]. Modern Electric Power, 2015, 32(2): 23-28.
Citation: WANG Xiaohua, ZHANG Jiancheng, ZHOU Chunxia, et al. Research on Scheduling Controlling Method for Grid-connectedPV Power System[J]. Modern Electric Power, 2015, 32(2): 23-28.
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Research on Scheduling Controlling Method for Grid-connectedPV Power System

  • 1.

    Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China;

  • 2.

    China Electric Power Research Institute, Beijing 100085, China

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  • Received Date: July 11, 2014
  • Published Date: April 08, 2015
    Graphical Abstract
    • Abstract
  • Grid-connected photovoltaic system is the main development trend of photovoltaic generation systems. The power output of PV is random and uncontrollable because it is influenced by such factors as sunlight and temperature, large-capacity grid-connected photovoltaic power brings certain difficulties in power grid dispatching. In order to connect large-capacity photovoltaic power with power grid, hybrid energy storage is used to smooth power output for improving the schedulability of grid-connected photovoltaic system. A kind of double-deck energy management method is put forward based on different compensatory period in this paper. Taking the cost of energy storage system and the correlation between actual power supply curve and the power dispatching curve of photovoltaic system as the optimized objectives,an improved technology named Pareto genetic algorithm based on niche is applied to obtain optimized solution. In the end, the capacity of energy storage system is optimally equipped by cases, and the rationality and validity of the proposed energy management strategy is analyzed and verified.
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  • 刘建涛, 曹雷, 马杰,等. 储能技术在光伏并网发电系统中的应用分析[J]. 电网与清洁能源, 2011, 27(7): 6266.
    艾欣, 韩晓男, 孙英云,等. 光伏发电并网及其相关技术发展现状与展望[J]. 现代电力, 2013, 30(1): 17.
    Li Weiya. The affect of future grid-connected photovoltaic power generation on the grid [J]. Solar Energy, 2008(2): 6768.
    陈炜, 艾欣, 吴涛,等. 光伏并网发电系统对电网的影响研究综述[J]. 电力自动化设备, 2013, 33(2): 2633.
    刘建涛, 曹雷, 张建成. 基于储能系统的用户并网光伏发电系统经济性分析[J]. 太阳能学报, 2012, 33(11): 18871892.
    陈维, 沈辉, 邓幼俊,等. 太阳能光伏应用中的储能系统研究[J].蓄电池, 2006(1): 2127.
    鞠洪新, 于金辉. 储能在光伏并网发电系统中的应用[J]. 电子世界, 2009(11): 61.
    谢俊文, 陆继明, 毛承雄,等. 基于变平滑时间常数的电池储能系统优化控制方法[J]. 电力系统自动化, 2013, 37(1): 96102.
    张野, 郭力, 贾宏杰,等. 基于电池荷电状态和可变
    滤波时间常数的储能控制方法[J]. 电力系统自动化, 2012, 36(6):3438.
    鲍雪娜, 强玉尊, 张建成. 联网光伏电站可调度性研究 [J]. 电力科学与工程, 2012, 28(2): 16.
    鲍雪娜, 张建成. 联网光伏电站可调度运行特性分析 [J]. 电网与清洁能源, 2012, 28(8): 5963.
    于芃, 周玮, 孙辉,等. 用于风电功率平抑的混合储能系统及其控制系统设计[J]. 中国电机工程学报, 2011, 31(17):127133.
    蒋平, 熊华川. 混合储能系统平抑风力发电输出功率波动控制方法设计[J]. 电力系统自动化, 2013, 37(1): 122127.
    张国驹, 唐西胜, 齐智平,等. 平抑间歇式电源功率波动的混合储能系统设计[J]. 电力系统自动化, 2011, 35(20): 2428.
    张野, 郭力, 贾宏杰,等. 基于平滑控制的混合储能系统能量管理方法[J]. 电力系统自动化, 2012, 36(16): 3641.
    唐西胜, 武鑫, 齐智平. 超级电容器蓄电池混合储能独立光伏系统研究[J]. 太阳能学报, 2007, 28(2): 178183.
    杨珺, 张建成, 桂勋. 并网风光发电中混合储能系统容量优化配置研究[J]. 电网技术, 2013, 37(5): 12091216.
    江敏, 陈一民. 遗传算法在多目标优化问题中的应用策略[J]. 兰州理工大学学报, 2007, 3(3): 9294.
    朱学军, 攀登, 王安麟,等. 混合变量多目标优化设计的Pareto遗传算法实现[J]. 上海交通大学学报, 2000, 34(3): 411414.
    王晓鹏等. 多目标优化设计的Pareto遗传算法[J]. 系统工程与电子技术, 2003, 25(12): 15581561.
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