电力系统自动化2017，Vol.41Issue(22)：56-65,10.DOI:10.7500/AEPS20170501001
基于改进模型预测控制的微电网能量管理策略
Energy Management Strategy Based on Improved Model Predictive Control for Microgrid
摘要
Abstract
In a microgrid system,the distributed generator has the characteristics of being intermittent and fluctuating,and the load is variable.The uncertainty of the distributed generator and the load demand will lead to the uncertainty of the microgrid energy optimization results.In order to solve the above problems,an improved model predictive control (MPC) strategy for two-layer microgrid energy optimization with multi time scale is proposed.As some model parameters are fixed in the traditional MPC optimization,it is hard to timely deal with the emergency disturbance in the system;and the single optimization cycle time of traditional MPC is limited,thus it is hard to deal with some complex constraints which are related to time or affect the overall optimization results.An adaptive improved MPC strategy is proposed according to the uncertainty of microgrid and complicated constraints to adapt to the microgrid characteristics of equipment switching flexibility and power generation affected by the outside world.It is shown that the robustness of the system and the accuracy of optimization are guaranteed.The allocation of future energy and load scheduling is optimized according to the day-ahead plan.By taking the results of the day-ahead optimization as reference,the real-time energy optimization is undertaken based on MPC,so as to make the optimization goal better,and improve the accuracy of the results.Finally,the simulation results of MATLAB demonstrate the applicability and accuracy of the proposed method.关键词
微电网(微网)/多时间尺度/改进模型预测控制/能量优化Key words
microgrid/multi-time scale/improved model predictive control (MPC)/energy optimization引用本文复制引用
窦晓波,晓宇,袁晓冬,吴在军,刘晶,胡敏强..基于改进模型预测控制的微电网能量管理策略[J].电力系统自动化,2017,41(22):56-65,10.基金项目
This work is supported by National Key Research and Development Program of China (No.2016YFB0900500) and National Natural Science Foundation of China (No.51777031).国家重点研发计划资助项目(2016YFB0900500) （No.2016YFB0900500）
国家自然科学基金资助项目(51777031).本文研究得到国网江苏省电力公司科技项目(5210EF15001H)的资助,谨此致谢! （51777031）
