Research On Economic Dispatching Of Microgrid System Including Renewable Energy And Electric Vehicles

With the launch of the national "dual-carbon" action plan of carbon peaking in 2030 and carbon neutrality in 2060,the proportion of wind power and photovoltaic power generation in the future power supply structure will continue to expand.However,large-scale wind power and photovoltaic power plants will have a greater impact on the existing grid system after being connected to the grid;at the same time,with the vigorous promotion of electric vehicles in China and the explosive growth of the industry,the future charging demand and the resulting energy consumption demand will continue to rise.The disorderly access of large-scale electric vehicles is a huge challenge to the grid.Based on the above two considerations,combined with the uncertainty of photovoltaic power generation and electric vehicle charging and discharging behavior,this paper applies the adjustable robust optimization scheduling algorithm to the work area microgrid system.By constructing an effective electric vehicle charging and discharging strategy,it can be used as a flexible energy storage unit to participate in scheduling to maximize the utility of peak shaving and valley filling,thereby promoting the coordinated development of electric vehicles and the renewable energy industry.The main research work of this paper includes the following four aspects:1.According to the concept of microgrid,it is divided into island microgrid and grid-connected microgrid,and then discussed the problems of multi-microgrid connection and real-time optimization.Distributed power,as the main component of the microgrid system,expounds its impact on the microgrid system,and summarizes the scheduling strategy of the microgrid system including electric vehicles and the hydrogen refueling station microgrid system.In addition,the application of stochastic optimization,robust optimization and fuzzy optimization in different scenarios is compared with the optimization methods for uncertain problems.2.Detailed derivation of the relevant basic theories of the article model.From Soyster’s robust optimization theory to Bertsimas’s adjustable robust optimization theory based on Soyster’s method,the process of processing uncertain variables is reproduced.Robust optimization means that uncertain variables fluctuate in the entire range,and the solution results are guaranteed to be valid for all situations.Adjustable robust optimization is to flexibly adjust the size of the interval to reduce the robustness of the model to improve economy.3.Propose a double layer’s scheduling model for photovoltaic microgrid.The upper layer models the photovoltaic output and the charging behavior of electric vehicles in the form of intervals,considering practical constraints such as power balance,and minimizing the costs of microgrid system operation-maintenance and environmental governance as the objective function to obtain a robust economic dispatch model.Through duality theory,this complex optimization problem with multiple variables is transformed into a conventional deterministic problem that is easy to solve.The lower layer uses the particle swarm algorithm to complete the total power distribution of electric vehicles,and displays the real-time state of charge of each electric vehicle.4.considering the V2G function of electric vehicles,electric vehicles participate in dispatch as an energy storage unit with dual attributes of charging and discharging.To improve the versatility of the model,a grouping strategy is proposed to manage all electric vehicles in groups.By introducing interval adjustment parameters,adjusting the photovoltaic output interval and the charging/discharging power interval of electric vehicles,the robustness of the model is changed.The robustness of the model is quantified by the probability that the spinning reserve constraint is violated,so as to coordinate the relationship between model robustness and economy,and provide a theoretical basis for decision-makers.