一种考虑经济调度的风电场储能控制策略
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摘要
随着风电的电力系统高比例接入,其输出功率的随机波动性将对系统的经济调度带来影响,为此,从风电场协调经济调度出发,研究利用储能平抑风电场功率波动的容量设计与控制策略。首先设计风电场基于电池和超级电容器的混合储能系统及考虑两种储能装置功率调节特性的风功率波动平滑控制策略;然后利用风电场并网电力系统及其经济调度控制,基于储能系统分组轮换控制提出一种储能面向风功率波动平抑的荷电状态管理与容量设计方法。在此基础上,针对风电功率波动的随机特性,采用人工神经网络设计储能系统动态跟踪风电场输出功率波动的控制策略。该控制策略通过利用风电场输出功率预测误差进行神经元权重在线调整,不仅使风电功率波动平抑具有良好的控制效果,而且提高了控制器对控制输入随机波动的鲁棒响应。最后利用改进的含风电场的3机11节点电力系统仿真,验证所提储能系统荷电状态管理和容量设计、平抑风功率波动控制策略的有效性。
With the grid-connection proportion of wind generators increasing, the random fluctuation of wind power will take an impact on the economic dispatching of power system. To address this, from the perspective of wind power plant coordinating economic dispatching, the two key issues of both the capacity design and control of the energy storage used for smoothing the wind power fluctuation are studied. First, a hybrid energy storage system based on both the battery storage and the super capacitor storage, the smoothing control strategy of wind power fluctuation considering the regulation characteristics of the two kinds of energy storage devices are respectively designed as well. Then, based on wind farm grid-connected power system and its economic dispatching control, using the group rotation method to implement the hybrid energy storage system smoothing the fluctuation of wind power, the methods of both the capacity design and the management of the state of charging of each hybrid energy storage group are presented. Moreover, taking account into the random characteristics of wind power fluctuation, the control strategy making energy storage system to dynamically track the output power fluctuation of wind farm is designed by using the artificial neutral network. Since the proposed control strategy using the deviation between the actual output power and the predicted output power of the wind power plant to adjust the weight of each neuron on line, this not only takes good control effect on the wind power fluctuation smoothing, but also improves the robust response of the controller to the random fluctuation of control input. Finally, an improved 3-machine 11-node wind power system is built. At the same time, the feasibility of the proposed method for both managing the state of charging of each energy storage group and designing the capacity, the effectiveness of the proposed adaptive neural network control strategy are respectively validated by simulation.
With the grid-connection proportion of wind generators increasing, the random fluctuation of wind power will take an impact on the economic dispatching of power system. To address this, from the perspective of wind power plant coordinating economic dispatching, the two key issues of both the capacity design and control of the energy storage used for smoothing the wind power fluctuation are studied. First, a hybrid energy storage system based on both the battery storage and the super capacitor storage, the smoothing control strategy of wind power fluctuation considering the regulation characteristics of the two kinds of energy storage devices are respectively designed as well. Then, based on wind farm grid-connected power system and its economic dispatching control, using the group rotation method to implement the hybrid energy storage system smoothing the fluctuation of wind power, the methods of both the capacity design and the management of the state of charging of each hybrid energy storage group are presented. Moreover, taking account into the random characteristics of wind power fluctuation, the control strategy making energy storage system to dynamically track the output power fluctuation of wind farm is designed by using the artificial neutral network. Since the proposed control strategy using the deviation between the actual output power and the predicted output power of the wind power plant to adjust the weight of each neuron on line, this not only takes good control effect on the wind power fluctuation smoothing, but also improves the robust response of the controller to the random fluctuation of control input. Finally, an improved 3-machine 11-node wind power system is built. At the same time, the feasibility of the proposed method for both managing the state of charging of each energy storage group and designing the capacity, the effectiveness of the proposed adaptive neural network control strategy are respectively validated by simulation.
引文
[1] 国家能源局.2015年风电产业发展情况[R].北京:国家能源局,2016.
[2] ALBADI M H,El-SAADANY E F.Overview of wind power intermittency impacts on power systems[J].Electric Power Systems Research,2010,80(6):627-632.
[3] 林卫星,文劲宇,艾小猛,等.风功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38-46.LIN Weixing,WEN Jinyu,AI Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46.
[4] 熊传平,张晓华.考虑大规模风电接入的系统静态电压稳定性分析[J].电力系统保护与控制,2012,40(21):133-137.XIONG Chuanping,ZHANG Xiaohua.Analysis on static voltage stability of system with large-scale wind power integration[J].Power System Protection and Control,2012,40(21):133-137.
[5] 何成明,王洪涛,韦仲康,等.风电场与AGC机组分布式协同实时控制[J].中国电机工程学报,2015,35(2):302-309.HE Chengming,WANG Hongtao,WEI Zhongkang,et al.Distributed coordinated real-time control of wind farm and AGC units[J].Proceedings of the CSEE,2015,35(2):302-309.
[6] VENAYAGAMOORTHY G K,ROHRIG K,ERLICH I.Short-term wind power forecasting and intelligent predictive control based on data analytics[J].IEEE Power & Energy Magazine,2012,10(5):70-78.
[7] 王洪涛,何成明,房光华,等.计及风电预测误差带的调度计划渐进优化模型[J].电力系统自动化,2011,35(22):131-135.WANG Hongtao,HE Chengming,FANG Guanghua,et al.A gradual optimization model of dispatching schedule taking account of wind power predication error bands[J].Automation of Electric Power Systems,2011,35(22):131-135.
[8] KUNDUR P.Power system stability and control[M].New York:McGraw-hill,1994.
[9] DUNN B,KAMATH H,TARASCON J M.Electrical energy storage for the grid:a battery of choices[J].Science,2011,334(6058):928-935.
[10] 程时杰,李刚,孙海顺,等.储能技术在电气工程领域中的应用与展望[J].电网与清洁能源,2009,25(2):1-8.CHENG Shijie,LI Gang,SUN Haishun,et al.Application and prospect of energy storage in electrical engineering[J].Power System and Clean Energy,2009,25(2):1-8.
[11] 李允超,宋华伟,马洪涛,等.储能技术发展现状研究[J].发电与空调,2017,38(4):56-61.LI Yunchao,SONG Huawei,MA Hongtao,et al.Research on the development of energy storage technology[J].Power Generation & Air Condition,2017,38(4):56-61.
[12] 丁明,林根德,陈自年,等.一种适用于混合储能系统的控制策略[J].中国电机工程学报,2012,32(7):1-6.DING Ming,LIN Gende,CHEN Zinian,et al.A control strategy for hybrid energy storage systems[J].Proceedings of the CSEE,2012,32(7):1-6.
[13] 张新松,顾菊平,袁越,等.基于电池储能系统的风功率波动平抑策略[J].中国电机工程学报,2014,34(28):4753-4759.ZHANG Xinsong,GU Juping,YUAN Yue,et al.Strategy of smoothing wind power fluctuation based on battery energy storage system[J].Proceedings of the CSEE,2014,34(28):4753-4759.
[14] TELEKE S,BARAN M E,BHATTACHARYA S,et al.Optimal control of battery energy storage for wind farm dispatching[J].IEEE Transactions on Energy Conversation,2010,25(3):787-794.
[15] 张惠生,赵兴勇,王凯武,等.基于风光互补微电网的复合储能控制策略[J].广东电力,2017,30(2):48-53,96.ZHANG Huisheng,ZHAO Xingyong,WANG Kaiwu,et al.Control strategy for hybrid energy storage based on wind/photovoltaic complementary micro-grid system[J].Guangdong Electric Power,2017,30(2):48-53,96.
[16] 杜平,万玉良,米增强,等.储能型风电场黑启动火电机组过程中的功率协调控制策略[J].电力科学与工程,2017,33(6):34-41.DU Ping,WAN Yuliang,MI Zengqiang,et al.The coordinated power control strategy of storage-based wind farm during black-start process of thermal power unit[J].Electric Power Science and Engineering,2017,33(6):34-41.
[17] OHKI Y.Recent progress in energy storage by a NAS battery system[J].IEEE Electrical Insulation Magazine,2010,26(3):55-56.
[18] YUAN Yue,ZHANG Xinsong,JU Ping,et al.Applications of battery energy storage system for wind power dispatchability purpose[J].Electric Power Systems Research,2012,93(12):54-60.
[19] 张红艳,张建坡.电池储能接入系统拓扑及其控制策略[J].电力科学与工程,2018,34(11):14-19.ZHANG Hongyan,ZHANG Jianpo.Research on PCS topologies and control of battery energy storage systems[J].Electric Power Science and Engineering,2018,34(11):14-19.
[20] 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):37-41.ZHANG Ye,GUO Li,JIA Hongjie,et al.An energy management method of hybrid energy storage system based on smoothing control[J].Automation of Electric Power Systems,2012,36(16):37-41.
[21] 刘佳,孙春顺,杨江涛,等.平抑风电出力波动的混合储能系统优化策略[J].电力科学与工程,2018,34(2):38-43.LIU Jia,SUN Chunshun,YANG Jiangtao,et al.Optimization strategy for smoothing wind power output fluctuation with hybrid energy storage system[J].Electric Power Science and Engineering,2018,34(2):38-43.
[22] 蒋哲,韩学山,王孟夏.伴随 AGC 机组控制过程的潮流模型与算法[J].电网技术,2010,34(1):68-72.JIANG Zhe,HAN Xueshan,WANG Mengxia.Model and algorithm for power flow accompanying with automatic generation control process[J].Power System Technology,2010,34(1):68-72.
[23] MUDUMBAI R,DASGUPTA S,BRIAN B.Distributed control for optimal economic dispatch of a network of heterogeneous generators[J].IEEE Transactions on Power Systems,2012,27(4):1750-1760.
[24] BANAKAR H,LUO C l,BOON-TECK O.Impacts of wind power minute-to-minute variations on power system operation[J].IEEE Transactions on Power Systems,2008,23(1):150-160.
[25] DIVYA K C,?STERGAARD J.Battery energy storage technology for power systems-an overview[J].Electric Power Systems Research,2009,79(4):511-520.
[26] 刘瑞叶,黄磊.基于动态神经网络的风电场输出功率预测[J].电力系统自动化,2012,36(11):20-23.LIU Ruiye,HUANG Lei.Wind power forecasting based on dynamic neural networks[J].Automation of Electric Power Systems,2012,36(11):20-23.
[27] RAHMAN M A,JOHN S.On-line adaptive artificial neural network based vector control of permanent magnet synchronous motor[J].IEEE Transactions on Energy Conversion,1998,13(3):311-318.
[28] WANG P,BILLINTON R.Reliability benefit analysis of adding WT to a distribution system[J].IEEE Transactions on Energy Conversion,2001,16(2):134-139.
[2] ALBADI M H,El-SAADANY E F.Overview of wind power intermittency impacts on power systems[J].Electric Power Systems Research,2010,80(6):627-632.
[3] 林卫星,文劲宇,艾小猛,等.风功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38-46.LIN Weixing,WEN Jinyu,AI Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46.
[4] 熊传平,张晓华.考虑大规模风电接入的系统静态电压稳定性分析[J].电力系统保护与控制,2012,40(21):133-137.XIONG Chuanping,ZHANG Xiaohua.Analysis on static voltage stability of system with large-scale wind power integration[J].Power System Protection and Control,2012,40(21):133-137.
[5] 何成明,王洪涛,韦仲康,等.风电场与AGC机组分布式协同实时控制[J].中国电机工程学报,2015,35(2):302-309.HE Chengming,WANG Hongtao,WEI Zhongkang,et al.Distributed coordinated real-time control of wind farm and AGC units[J].Proceedings of the CSEE,2015,35(2):302-309.
[6] VENAYAGAMOORTHY G K,ROHRIG K,ERLICH I.Short-term wind power forecasting and intelligent predictive control based on data analytics[J].IEEE Power & Energy Magazine,2012,10(5):70-78.
[7] 王洪涛,何成明,房光华,等.计及风电预测误差带的调度计划渐进优化模型[J].电力系统自动化,2011,35(22):131-135.WANG Hongtao,HE Chengming,FANG Guanghua,et al.A gradual optimization model of dispatching schedule taking account of wind power predication error bands[J].Automation of Electric Power Systems,2011,35(22):131-135.
[8] KUNDUR P.Power system stability and control[M].New York:McGraw-hill,1994.
[9] DUNN B,KAMATH H,TARASCON J M.Electrical energy storage for the grid:a battery of choices[J].Science,2011,334(6058):928-935.
[10] 程时杰,李刚,孙海顺,等.储能技术在电气工程领域中的应用与展望[J].电网与清洁能源,2009,25(2):1-8.CHENG Shijie,LI Gang,SUN Haishun,et al.Application and prospect of energy storage in electrical engineering[J].Power System and Clean Energy,2009,25(2):1-8.
[11] 李允超,宋华伟,马洪涛,等.储能技术发展现状研究[J].发电与空调,2017,38(4):56-61.LI Yunchao,SONG Huawei,MA Hongtao,et al.Research on the development of energy storage technology[J].Power Generation & Air Condition,2017,38(4):56-61.
[12] 丁明,林根德,陈自年,等.一种适用于混合储能系统的控制策略[J].中国电机工程学报,2012,32(7):1-6.DING Ming,LIN Gende,CHEN Zinian,et al.A control strategy for hybrid energy storage systems[J].Proceedings of the CSEE,2012,32(7):1-6.
[13] 张新松,顾菊平,袁越,等.基于电池储能系统的风功率波动平抑策略[J].中国电机工程学报,2014,34(28):4753-4759.ZHANG Xinsong,GU Juping,YUAN Yue,et al.Strategy of smoothing wind power fluctuation based on battery energy storage system[J].Proceedings of the CSEE,2014,34(28):4753-4759.
[14] TELEKE S,BARAN M E,BHATTACHARYA S,et al.Optimal control of battery energy storage for wind farm dispatching[J].IEEE Transactions on Energy Conversation,2010,25(3):787-794.
[15] 张惠生,赵兴勇,王凯武,等.基于风光互补微电网的复合储能控制策略[J].广东电力,2017,30(2):48-53,96.ZHANG Huisheng,ZHAO Xingyong,WANG Kaiwu,et al.Control strategy for hybrid energy storage based on wind/photovoltaic complementary micro-grid system[J].Guangdong Electric Power,2017,30(2):48-53,96.
[16] 杜平,万玉良,米增强,等.储能型风电场黑启动火电机组过程中的功率协调控制策略[J].电力科学与工程,2017,33(6):34-41.DU Ping,WAN Yuliang,MI Zengqiang,et al.The coordinated power control strategy of storage-based wind farm during black-start process of thermal power unit[J].Electric Power Science and Engineering,2017,33(6):34-41.
[17] OHKI Y.Recent progress in energy storage by a NAS battery system[J].IEEE Electrical Insulation Magazine,2010,26(3):55-56.
[18] YUAN Yue,ZHANG Xinsong,JU Ping,et al.Applications of battery energy storage system for wind power dispatchability purpose[J].Electric Power Systems Research,2012,93(12):54-60.
[19] 张红艳,张建坡.电池储能接入系统拓扑及其控制策略[J].电力科学与工程,2018,34(11):14-19.ZHANG Hongyan,ZHANG Jianpo.Research on PCS topologies and control of battery energy storage systems[J].Electric Power Science and Engineering,2018,34(11):14-19.
[20] 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):37-41.ZHANG Ye,GUO Li,JIA Hongjie,et al.An energy management method of hybrid energy storage system based on smoothing control[J].Automation of Electric Power Systems,2012,36(16):37-41.
[21] 刘佳,孙春顺,杨江涛,等.平抑风电出力波动的混合储能系统优化策略[J].电力科学与工程,2018,34(2):38-43.LIU Jia,SUN Chunshun,YANG Jiangtao,et al.Optimization strategy for smoothing wind power output fluctuation with hybrid energy storage system[J].Electric Power Science and Engineering,2018,34(2):38-43.
[22] 蒋哲,韩学山,王孟夏.伴随 AGC 机组控制过程的潮流模型与算法[J].电网技术,2010,34(1):68-72.JIANG Zhe,HAN Xueshan,WANG Mengxia.Model and algorithm for power flow accompanying with automatic generation control process[J].Power System Technology,2010,34(1):68-72.
[23] MUDUMBAI R,DASGUPTA S,BRIAN B.Distributed control for optimal economic dispatch of a network of heterogeneous generators[J].IEEE Transactions on Power Systems,2012,27(4):1750-1760.
[24] BANAKAR H,LUO C l,BOON-TECK O.Impacts of wind power minute-to-minute variations on power system operation[J].IEEE Transactions on Power Systems,2008,23(1):150-160.
[25] DIVYA K C,?STERGAARD J.Battery energy storage technology for power systems-an overview[J].Electric Power Systems Research,2009,79(4):511-520.
[26] 刘瑞叶,黄磊.基于动态神经网络的风电场输出功率预测[J].电力系统自动化,2012,36(11):20-23.LIU Ruiye,HUANG Lei.Wind power forecasting based on dynamic neural networks[J].Automation of Electric Power Systems,2012,36(11):20-23.
[27] RAHMAN M A,JOHN S.On-line adaptive artificial neural network based vector control of permanent magnet synchronous motor[J].IEEE Transactions on Energy Conversion,1998,13(3):311-318.
[28] WANG P,BILLINTON R.Reliability benefit analysis of adding WT to a distribution system[J].IEEE Transactions on Energy Conversion,2001,16(2):134-139.