Model Predictive Control Based Microgrid Energy Dispatch Under Uncertainties

The global energy crisis and environmental problems are continuously promoting the transformation and upgrading of energy utilization.In China,Building an Energy Interconnection System(EIS)is considered a strategic way to achieve clean,low-carbon,safe and efficient Energy.As the cell of building the EIS,the microgrid can realize the coordination of the multiple energy between regions,dispatch of active load,and utilization of local renewable energy.This research focuses on the uncertainties of microgrid energy dispatch.The main constribution of this desertation are listed as follows:(1)An integrated energy system management model is set up which ensures stable operation of the system and optimizes the compresensive economic revenue,and a model predictive control based microgrid optimal scheduling approach is proposed.The optimal scheduling model fully takes into account the renewable energy,energy storage system and distributed controllable resources,and the time-of-use electricity price is also considered.The model can adjust the output of power generation equipment and energy storage system timely to realize the system power balance and economic optimal performance at different time.(2)A stochastic model predictive control strategy based on scenario optimization is proposed to realize the economic scheduling of microgrid.Considering the fully use of wind power output,the stochastic of renewable energy is described by the method of scenario generation and scenario reduction.Under the condition that the energy storage life is not affected,a certain relaxation probability is introduced into the charging and discharging power of the energy storage battery and the SOC constraint to enhance the ability of wind power suppression.The number of scenario optimization is given based on the constraint relaxation probability threshold under a certain degree of confidence.MATLAB/CPLEX is used to solve the problem,which effectively improves the operation economy of the microgrid and the ability to suppress wind power fluctuations.(3)The application scenario of microgrid is further expanded,and a CHP microgrid with stochastic access of electric vehicles is established,in which the schedulability of electric vehicles is described by the method of scenario optimization,and the battery loss model of electric vehicles is analyzed.Taking microgrids of office buildings and residential areas as examples,the optimal scheduling of EV-CHP microgrid is realized by coordinated dispatching of thermal energy and electric energy.The system can make the best use of renewable energy and improve the comprehensive utilization efficiency of energy.At the same time,EVs can adopt different response strategies for different operating conditions of the microgrid to achieve the purpose of load shifting.(4)In order to further study the schedulability of uncertain microgrid,an adaptively stochastic model predictive control strategy is proposed.An improved adaptive update strategy is proposed to better track the dynamic performance of the system,and to adjust the control quantity timely through the adaptive update strategy,so that the system can better achieve the expected performance.Compared with the robust model predictive control method based on scenario optimization,and the effects of different adaptive update strategies and parameter optimization are analyzed.It is proved that the proposed method can effectively improve the schedulability of the system and the adaptability of the model,so as to reduce the conservatism of the system,increase the feasible region and improve the elasticity of the microgrid.