基于滑动平均和模型预测控制的风储平抑策略
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摘要
风电出力的随机波动给电网带来了大量的负面影响,电池储能系统(Battery Energy StorageSystem,BESS)的接入可以有效平抑风功率波动,提高风电出力稳定性。为了提高储能利用效率,BESS的荷电状态(SOC)需最大限度控制在一定区域内,以便拥有足够裕量进行下一时刻的充放电动作,从而带来更好的平滑效果。为此,提出了一种由滑动平均滤波法和模型预测控制法协调运行的控制策略。该策略利用风电出力预测曲线,综合考虑了BESS出力、BESS的SOC以及风储联合输出功率平滑效果来实现对BESS优化控制。仿真验证表明该策略与传统低通滤波相比,不仅平滑效果更为理想,而且BESS的SOC限制在一定区间内,从而降低了BESS的最大充放电深度,节约了储能投资成本。
The random fluctuations of wind power outputs have brought a lot of negative impacts in the power grid.Using storage batteries to smooth the wind power fluctuation can optimize wind power output characteristics and improve the stability of the output power in the wind farm.Controlling the state of charge(SOC) of energy storage battery in a defined interval can extend the service life of the storage battery and bring a better smoothing effect because of sufficient capacity.So,a new control strategy consisting of the moving average filter method and model predictive control act was proposed.It is based on the wind power prediction curve,with consideration of the BESS output,the SOC of BESS and the combined output power of wind storage to achieve the optimization of BESS.Simulation results show that the new strategy not only does have a good stabilizing effect but also extends the storage battery life to save storage investment costs because the SOC of the energy storage battery is limited to a set interval when it compared with the traditional first-order low-pass filter algorithm.
The random fluctuations of wind power outputs have brought a lot of negative impacts in the power grid.Using storage batteries to smooth the wind power fluctuation can optimize wind power output characteristics and improve the stability of the output power in the wind farm.Controlling the state of charge(SOC) of energy storage battery in a defined interval can extend the service life of the storage battery and bring a better smoothing effect because of sufficient capacity.So,a new control strategy consisting of the moving average filter method and model predictive control act was proposed.It is based on the wind power prediction curve,with consideration of the BESS output,the SOC of BESS and the combined output power of wind storage to achieve the optimization of BESS.Simulation results show that the new strategy not only does have a good stabilizing effect but also extends the storage battery life to save storage investment costs because the SOC of the energy storage battery is limited to a set interval when it compared with the traditional first-order low-pass filter algorithm.
引文
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[2]Hodge B M,Ela E G,Milligan M.Characterizing and Modeling Wind Power Forecast Errors from Operational Systems for Use in Wind Integration Planning Studies[J].Wind Engineering,2013,36(5):509-524.
[3]章竹耀,肖欣,郭晓丽,等.基于储能电池的光伏功率波动平抑策略[J].电力建设,2016,37(8):90-95.Zhang Z Y,Xiao X,Guo X L,et al.Restraining Strategy of Photovoltaic Power Fluctuation Based on Energy Storage Battery[J].Electric Power Construction,2016,37(8):90-95.
[4]Khosravi A,Nahavandi S.An Optimized Mean Variance Estimation Method for Uncertainty Quantification of Wind Power Forecasts[J].International Journal of Electrical Power&Energy Systems,2014,61(5):446-454.
[5]Han X J,Chen F,Cui X W,et al.A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology[J].Energies,2012,5(5):1593-1612.
[6]Jiang Q,Hong H.Wavelet-based Capacity Configuration and Coordinated Control of Hybrid Energy Storage System for Smoothing Out Wind Power Fluctuations[J].IEEE Transactions on Power Systems,2013,28(2):1363-1372.
[7]Bortolini Marco,Gamberi Mauro,Alessandro.Technical and Economic Design of Photovoltaic and Battery Energy Storage System[J].Energy Conversion and Management,2014,86(2):81-92.
[8]邓凯锋,王耀南,刘东奇.基于小波变换的卡尔曼滤波动力电池SOC估算[J].控制工程,2015,22(3):398-403.Deng K F,Wang Y N,Liu D Q.Kalman Filter for HEV's Battery SOC Estimation Based on Wavelet Transform[J].Control Engineering of China,2015,22(3):398-403.
[9]唐西胜,苗福丰,齐智平,等.风力发电的调频技术研究综述[J].中国电机工程学报,2014,34(25):4304-4314.Tang X S,Miao F F,Qi Z P,et al.Survey on Frequency Control of Wind Power[J].Proceedings of the CSEE,2014,34(25):4304-4314.
[10]Kakigano H,Miura Y,Ise T,et al.DC micro-grid for super high quality distribution-system configuration and control of distributed generations and energy storage devices[C].Power Electronics Specialists Conference.Jeju,Korea:IEEE,2006:1-7.
[11]赵兴勇,杨涛,王灵梅.基于复合储能的微电网运行方式切换控制策略[J].高电压技术,2015,41(7):2142-2147.Zhao X Y,Yang T,Wang L M.Control Strategy for Operation Modes Transfer of Micro-grid Based on Hybrid Energy Storage System[J].High Voltage Engineering,2015,41(7):2142-2147.
[12]章竹耀,郭晓丽,张新松,等.储能电池平抑风功率波动策略[J].电力系统保护与控制,2017,45(3):62-68.Zhang Z Y,Guo X L,Zhang X S,et al.Strategy of Smoothing Wind Power Fluctuation Based on Storage Battery[J].Power System Protection and Control,2017,45(3):62-68.