Study On Energy Optimization Management Method Of Intelligent Photovoltaic Microgrid

Since the beginning of the 21st century,photovoltaic micro-grid with energy autonomy management and controlhas been developed rapidly as one of these main forms of renewable energy utilization.But,due to these characters of single power,large output fluctuation,and limited storage capacity of photovoltaic microgrid,the smaller power supply or load fluctuation will have a great influence on its optimal operation schedule.Therefore,it is of great theoretical and engineering significance to establish a suitable optimal scheduling model and its solving method.In this paper,the typical photovoltaic microgrid structures have been biefly introduced first.At the same time,the historical and current development of microgrid energy optimation scheduling of three aspects in terms of dispatching strategy,dispatching model and solving method is showed.Based on this,in this paper,we do further research in muti-objective optimation scheduling method considering demand side management,stochastic optimal scheduling considering forecast error,and muti-time-scale stochastic optimization dispatch problem.The main work includes the following three parts:(1)In the issue of muti-objective optimation scheduling method based on photovoltaic microgrid considering demand side management.Firstly,based on the brief analysis of operation states the typical configuration,focusing on the influence of the economic performance with and without considering electric vehicle,a multi-objective optimal scheduling model of photovaltaic microgrid with and without considering electric vehicle charging are proposed respectively,considering the constraints such as power balance,energy storage system charge state,load transferable time range,electric vehicle charging time and so on,and the non-dominated sorting genetic algorithm-II(NSGA-II)based solving strategy is proposed.Finally,the feasibility and effectiveness of this method is validated through simulation results,and that for general cases of considering the demand side management and the electric vehicle charging for significantly improving the operational efficiency of the system also be proved likewise.(2)in the issue of stochastic optimal scheduling considering forecast error,focusing on the influence of forecasting error,using the probability density distribution function to simulate the uncertain factors in the operation of system,considering the price mechanism of real-time electricity and real-time spinning reserve and other constraints,a optimal scheduling model of grid-connected photovoltaic microgrid based on chance constrained programming theory is proposed.In which,random variables and chance constrained are included,which made it difficult to solve directly.But the hybrid algorithm combining Monte Carlo simulation and genetic algorithms can be used to solve it,which provides an effective method for the economic dispatch problem with uncertainty.(3)In the issue of muti-time-scale stochastic optimization dispatch problem,considering the deviation of the scheduling and real-time scheduling due to the forecast error of renewable energy and load,a multi-time scale stochastic optimization scheduling model considering the demand side management is proposed to reduce the power fluctuations caused by the forecast error.Among them,the day-ahead economic dispatch model based on the theory of chance constrained programming is established first,and then the real-time power adjustment strategy is utilised to modify the scheduling scheme in real time.Simulation results show that this method can effectively compensate the power fluctuations in the actual operation of system,ensure the safety and reliability of the system,and improve the feasibility of the method.