基于FMPC的储能系统跟踪光伏发电计划控制策略
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摘要
为了最大限度地提高跟踪光伏发电计划的能力,文章提出一种适用于储能系统的模糊模型预测控制(FMPC)充放电控制策略。基于模型预测控制(MPC)方法,建立了以储能系统剩余容量偏离理想值、并网功率与发电计划偏差最小为目标的控制模型,通过引入模糊控制(Fuzzy Control),即时调节目标函数中的权重系数以获得最佳跟踪效果。以光伏电站实际运行数据进行仿真分析,结果表明,与传统MPC和普通控制策略相比,所提出的基于FMPC的储能系统控制方法具有独特的灵活性和适应性。
In order to maximize the ability to improve the tracking PV power schedule output, a fuzzy model predictive control(FMPC) charge and discharge control strategy suitable for energy storage systems(ESS) was proposed. Firstly, based on the model predictive control(MPC)method, a control model was established, which aimed to minimize the deviate from the ideal value of the remaining capacity of the energy storage system and the deviation between the grid power and the generation plan. Secondly, the weight coefficient in the objective function can be adjusted instantly to obtain the best tracking effects by introducing Fuzzy Control. The actual operation data of PV was used for simulation analysis, the results show that compared with the traditional MPC and common control strategies, the proposed BESS control method based on FMPC had unique flexibility and adaptability.
In order to maximize the ability to improve the tracking PV power schedule output, a fuzzy model predictive control(FMPC) charge and discharge control strategy suitable for energy storage systems(ESS) was proposed. Firstly, based on the model predictive control(MPC)method, a control model was established, which aimed to minimize the deviate from the ideal value of the remaining capacity of the energy storage system and the deviation between the grid power and the generation plan. Secondly, the weight coefficient in the objective function can be adjusted instantly to obtain the best tracking effects by introducing Fuzzy Control. The actual operation data of PV was used for simulation analysis, the results show that compared with the traditional MPC and common control strategies, the proposed BESS control method based on FMPC had unique flexibility and adaptability.
引文
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[12]杨德友,温佳鑫,陈家荣,等.用于提高风电场可调度性的储能系统预测控制策略[J].高电压技术,2017,43(3):1043-1048.
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[15]肖浩,裴玮,孔力.基于模型预测控制的微电网多时间尺度协调优化调度[J].电力系统自动化,2016,40(18):7-14.
[2]陈晓云,马瑞军.三相光伏并网逆变器状态空间模型与矢量控制[J].新疆大学学报(自然科学版),2013,30(4):482-489.
[3]Xiangjun L,Yao L,Hui D.Optimal control and management of a large-scale battery energy storage system to mitigate fluctuation and intermittence of renewable generations[J].Journal of Modern Power Systems&Clean Energy,2016,4(4):593-603.
[4]张颖,季宇,唐云峰,等.基于MPC含分布式光伏配电网有功功率-无功功率协调控制[J].电力系统自动化,2017,41(21):140-146.
[5]刘艳芬,白恺,张彩萍,等.风储电站实时跟踪发电计划控制策略研究[J].可再生能源,2015,33(3):363-368.
[6]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.
[7]姚波,樊艳芳.光储联合系统中储能电站的能量管理[J].可再生能源,2017,35(2):232-239.
[8]田春光,田利,李德鑫,等.基于混合储能系统跟踪光伏发电输出功率的控制策略[J].电工技术学报,2016,31(14):75-83.
[9]杨婷婷,李相俊,齐磊,等.基于机会约束规划的储能系统跟踪光伏发电计划出力控制方法[J].电力建设,2016,37(8):115-121.
[10]戚永志,刘玉田.风光储联合系统输出功率滚动优化与实时控制[J].电工技术学报,2014,29(8):265-273.
[11]闫鹤鸣,李相俊,麻秀范,等.基于超短期风电预测功率的储能系统跟踪风电计划出力控制方法[J].电网技术,2015,39(2):432-439.
[12]杨德友,温佳鑫,陈家荣,等.用于提高风电场可调度性的储能系统预测控制策略[J].高电压技术,2017,43(3):1043-1048.
[13]席裕庚,李德伟,林姝.模型预测控制---现状与挑战[J].自动化学报,2013,39(3):222-236.
[14]Khalid M,Savkin A V.A model predictive control approach to the problem of wind power smoothing with controlled battery storage[J].Renewable Energy,2010,35(7):1520-1526.
[15]肖浩,裴玮,孔力.基于模型预测控制的微电网多时间尺度协调优化调度[J].电力系统自动化,2016,40(18):7-14.