结合风功率预测及储能能量状态的模糊控制策略平滑风电出力
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摘要
在风电并网处加入储能系统,可以有效地平滑风力发电系统的并网功率,满足电网规定的波动范围。而基于储能荷电能量反馈的储能控制策略,可以在保证并网功率要求的同时,尽量避免储能电池过度充/放。在此基础上,提出一种基于相空间重构–随机森林风功率预测模型和储能荷电能量反馈的模糊控制策略。基于预测未来风功率变化评估功率波动水平,并结合储能当前荷电状态,利用模糊控制器调节储能系统出力。在保证风电平滑前提下,减少储能电池进入平抑能力死区时间,维持储能系统平抑波动水平。最后,通过将仿真算例结果和传统方法对比,验证了所提控制策略的优越性,即可以在相同储能配置比例下达到更低的功率波动指标要求和更少的储能死区时间。
By adding energy storage system at the point of common coupling(PCC), the output power of wind power generation system can be effectively smoothed to meet the fluctuation range specified by the power grid. The energy storage control strategy based on SOE(state of energy) feedback is used to avoid overcharging and over-discharging of the energy storage system to ensure its working under normal SOE range. Based on this, this paper proposes a wind power prediction model and an energy storage fuzzy feedback control strategy based on phase space reconstruction-random forest. Based on the predicted future wind power change, the power fluctuation level is evaluated. And combined with current SOE, fuzzy controller is used to adjust the energy storage system output under the premise of ensuring wind power smoothing, and the time for the energy storage battery to enter stabilization capability is maintained under the premise of ensuring wind power smoothing, thereby maintaining the level of energy storage system. Finally, simulation results are compared with those obtained with traditional methods to verify superiority of the control strategy proposed in this paper. It can achieve required lower power fluctuation indexes and less energy storage dead time under the same energy storage configuration ratio.
By adding energy storage system at the point of common coupling(PCC), the output power of wind power generation system can be effectively smoothed to meet the fluctuation range specified by the power grid. The energy storage control strategy based on SOE(state of energy) feedback is used to avoid overcharging and over-discharging of the energy storage system to ensure its working under normal SOE range. Based on this, this paper proposes a wind power prediction model and an energy storage fuzzy feedback control strategy based on phase space reconstruction-random forest. Based on the predicted future wind power change, the power fluctuation level is evaluated. And combined with current SOE, fuzzy controller is used to adjust the energy storage system output under the premise of ensuring wind power smoothing, and the time for the energy storage battery to enter stabilization capability is maintained under the premise of ensuring wind power smoothing, thereby maintaining the level of energy storage system. Finally, simulation results are compared with those obtained with traditional methods to verify superiority of the control strategy proposed in this paper. It can achieve required lower power fluctuation indexes and less energy storage dead time under the same energy storage configuration ratio.
引文
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[21]何川,刘天琪,胡晓通,等.基于超短期风电预测和混合储能的风电爬坡优化控制[J].电网技术,2017,41(3):782-790.He Chuan,Liu Tianqi,Hu Xiaotong,et al.Optimal control of wind ramp based on very short-term wind forecast and hybrid ESS[J].Power System Technology,2017,41(3):782-790(in Chinese).
[22]贾鹏飞,李卫国,高兴军,等.平抑风电功率波动的电池储能系统模糊控制方法[J].现代电力,2014,31(3):7-11.Jia Pengfei,Li Weiguo,Gao Xingjun,et al.Fuzzy logic control of battery energy storage system for smoothing wind power fluctuation[J].Modern Electric Power,2014,31(3):7-11(in Chinese).
[23]蒋小平,彭朝阳,魏立彬,等.基于模糊控制的混合储能平抑风电功率波动[J].电力系统保护与控制,2016,44(17):126-132.Jiang Xiaoping,Peng Chaoyang,Wei Libin,et al.Hybrid energy storage for smoothing wind power fluctuations based on fuzzy control[J].Power System Protection and Control,2016,44(17):126-132(in Chinese).
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[25]陆振波,蔡志明,姜可宇.基于改进的C-C方法的相空间重构参数选择[J].系统仿真学报,2007,19(11):2527-2529+2538.Lu Zhenbo,Cai Zhiming,Jiang Keyu.Determination of embedding parameters for phase space reconstruction based on improved C-Cmethod[J].Journal of System Simulation,2007,19(11):2527-2529+2538(in Chinese).
[26]王德文,孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报,2015,35(3):527-537.Wang Dewen,Sun Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of the CSEE,2015,35(3):527-537(in Chinese).
[27]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.Wu Xiaoyu,He Jinghan,Zhang Pei,et al.Power system short-term load forecasting based on improved random forest with grey relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55(in Chinese).
[2]刘春燕,晁勤,魏丽丽.基于实证数据和模糊控制的多时间尺度风储耦合实时滚动平抑波动[J].电力自动化设备,2015,35(2):35-41.Liu Chunyan,Chao Qin,Wei Lili.Wind-storage coupling based on actual data and fuzzy control in multiple time scales for real-time rolling smoothing of fluctuation[J].Electric Power Automation Equipment,2015,35(2):35-41(in Chinese).
[3]罗毅,千雨乐.基于相空间重构和小波神经网络的光伏阵列发电量预测[J].太阳能学报,2014,35(6):992-997.Luo Yi,Qian Yule.Power generation forecast of PV array based on phase space reconstruction and wavelet neural network[J].Acta Energiae Solaris Sinica,2014,35(6):992-997(in Chinese).
[4]陶佳,张弘,朱国荣,等.基于优化相空间重构技术的风电场发电功率预测研究[J].中国电机工程学报,2011,31(28):9-14.Tao Jia,Zhang Hong,Zhu Guorong,et al.Wind power prediction based on technology of advanced phase space reconstruction[J].Proceedings of the CSEE,2011,31(28):9-14(in Chinese).
[5]江岳春,张丙江,邢方方,等.基于混沌时间序列GA-VNN模型的超短期风功率多步预测[J].电网技术,2015,39(8):2160-2166.Jiang Yuechun,Zhang Bingjiang,Xing Fangfang,et al.Supershoryterm multi-step prediction of wind power based on GA-VNNmodel of chaotic time series[J].Power System Technology,2015,39(8):2160-2166(in Chinese).
[6]谷悦,唐巍,屈瑞谦.基于BP神经网络群结构的风电场短期风速预测[J].农村电气化,2013(1):50-52.
[7]凌武能,杭乃善,李如琦.基于云支持向量机模型的短期风电功率预测[J].电力自动化设备,2013,33(7):34-38.Ling Wuneng,Hang Naishan,Li Ruqi.Short-term wind power forecasting based on cloud SVM model[J].Electric Power Automation Equipment,2013,33(7):34-38(in Chinese).
[8]严欢,卢继平,覃俏云,等.基于多属性决策和支持向量机的风电功率非线性组合预测[J].电力系统自动化,2013,37(10):29-34.Yan Huan,Lu Jiping,Tan Xiaoyun,et al.A nonleaner combined model for wind power forecasting based on multi-attribute decisionmaking and support vector machine[J].Automation of Electric Power Systems,2013,37(10):29-34(in Chinese).
[9]胡梦月,胡志坚,仉梦林,等.基于改进AdaBoost.RT和KELM的风功率预测方法研究[J].电网技术,2017,37(2):536-542.Hu Mengyue,Hu Zhijian,Zhang Menglin,et al.Research on wind power forecasting method based on improved AdaBoost.RT and KELM algorithm[J].Power System Technology,2017,37(2):536-542(in Chinese).
[10]张川,杨雷,牛童阳,等.平抑风电出力波动储能技术比较及分析[J].电力系统保护与控制,2015,43(7):149-154.Zhang Chuan,Yang Lei,Niu Tongyang,et al.Comparison and analysis of energy storage technology to balance fluctuation of wind power output[J].Power System Protection and Control,2015,43(7):149-154(in Chinese).
[11]雷珽,欧阳曾恺,李征,等.平抑风能波动的储能电池SOC与滤波协调控制策略[J].电力自动化设备,2015,35(7):126-131.Lei Ting,Ouyang Zengkai,Li Zheng,et al.Coordinated control of battery SOC maintaining and filtering for wind power fluctuation smoothing[J].Electric Power Automation Equipment,2015,35(7):126-131(in Chinese).
[12]杨锡运,曹超,李相俊,等.基于模糊经验模态分解的电池储能系统平滑风电出力控制策略[J].电力建设,2016,37(8):134-140.Yang Xiyun,Cao Chao,Li Xiangjun,et al.Control strategy of smoothing wind power output using battery energy storage system based on fuzzy empirical mode decomposition[J].Electric Power Construction,2016,37(8):134-140(in Chinese).
[13]陈科彬,邱晓燕,刘波.风电场混合储能的小波包-SOC分区功率控制[J].仪器仪表学报,2017,38(8):2078-2085.Chen Kebin,Qiu Xiaoyan,Liu bo.Wavelet packet-SOC partitioned power control of hybrid energy storage for wind farm[J].Chinese Journal of Scientific Instrument,2017,38(8):2078-2085(in Chinese).
[14]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):34-38.Zhang Ye,Guo Li,Jia Hongjie,et al.A new battery energy storage system control method based on SOC and variable filter time constant[J].Automation of Electric Power Systems,2012,36(6):34-38(in Chinese).
[15]郭云鹏,王小蕾,文福拴,等.用于平抑风电功率波动的电池储能系统控制策略[J].电力建设,2018,39(6):125-130.Guo Yunpeng,Wang Xiaolei,Wen Fushuan,et al.Control strategies of battery energy storage systems for mitigating fluctuations of wind power outputs[J].Electric Power Construction,2018,39(6):125-130(in Chinese).
[16]邵丽华,章竹耀,张春龙,等.储能电池荷电状态与平抑风电出力波动协调运行策略[J].电力建设,2017,38(1):84-88.Shao Lihua,Zhang Zhuyao,Zhang Chunlong,et al.Coordinated operation strategy of storage battery SOC and smoothing wind power fluctuation[J].Electric Power Construction,2017,38(1):84-88(in Chinese).
[17]娄素华,吴耀武,崔艳昭,等.电池储能平抑短期风电功率波动运行策略[J].电力系统自动化,2014,38(2):17-22.Lou Suhua,Wu Yaowu,Cui Yanzhao,et al.Operation strategy of battery energy storage system for smoothing short-term wind power fluctuation[J].Automation of Electric Power Systems,2014,38(2):17-22(in Chinese).
[18]王境彪,李江,胡立强,等.基于双目标时变交集的电池储能改善风电场出力控制[J].电力系统保护与控制,2016,44(18):31-40.Wang Jingbiao,Li Jiang,Hu Liqiang,et al.A control of BESS to improve the wind farm output based on time-varying intersection of dual targets[J].Power System Protection and Control,2016,44(18):31-40(in Chinese).
[19]柴炜,曹云峰,李征,等.基于状态量预测的风储联合并网储能优化控制方法[J].电力系统自动化,2015,39(2):13-20.Chai Wei,Cao Yunfeng,Li Zheng,et al.An optimal energy storage control scheme for wind power and energy storage system based on state forecast[J].Automation of Electric Power Systems,2015,39(2):13-20(in Chinese).
[20]荣飞,李旺,黄守道.一种新型的基于风速预测的风力发电系统蓄电池充放电控制策略[J].电力自动化设备,2018,38(4):157-168.Rong Fei,Li Wang,Huang Shoudao.A new charging/discharging control strategy for battery in wind power generation system based on wind speed prediction[J].Electric Power Automation Equipment,2018,38(4):157-168(in Chinese).
[21]何川,刘天琪,胡晓通,等.基于超短期风电预测和混合储能的风电爬坡优化控制[J].电网技术,2017,41(3):782-790.He Chuan,Liu Tianqi,Hu Xiaotong,et al.Optimal control of wind ramp based on very short-term wind forecast and hybrid ESS[J].Power System Technology,2017,41(3):782-790(in Chinese).
[22]贾鹏飞,李卫国,高兴军,等.平抑风电功率波动的电池储能系统模糊控制方法[J].现代电力,2014,31(3):7-11.Jia Pengfei,Li Weiguo,Gao Xingjun,et al.Fuzzy logic control of battery energy storage system for smoothing wind power fluctuation[J].Modern Electric Power,2014,31(3):7-11(in Chinese).
[23]蒋小平,彭朝阳,魏立彬,等.基于模糊控制的混合储能平抑风电功率波动[J].电力系统保护与控制,2016,44(17):126-132.Jiang Xiaoping,Peng Chaoyang,Wei Libin,et al.Hybrid energy storage for smoothing wind power fluctuations based on fuzzy control[J].Power System Protection and Control,2016,44(17):126-132(in Chinese).
[24]中国电力科学研究院.GB/T 19963-2011风电场接入电力系统技术规定[S].北京:全国电力监管标准化技术委员会,中国标准出版社,2011.
[25]陆振波,蔡志明,姜可宇.基于改进的C-C方法的相空间重构参数选择[J].系统仿真学报,2007,19(11):2527-2529+2538.Lu Zhenbo,Cai Zhiming,Jiang Keyu.Determination of embedding parameters for phase space reconstruction based on improved C-Cmethod[J].Journal of System Simulation,2007,19(11):2527-2529+2538(in Chinese).
[26]王德文,孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报,2015,35(3):527-537.Wang Dewen,Sun Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of the CSEE,2015,35(3):527-537(in Chinese).
[27]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.Wu Xiaoyu,He Jinghan,Zhang Pei,et al.Power system short-term load forecasting based on improved random forest with grey relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55(in Chinese).
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