Multi-objective Robust Optimization Dispatch Of Power System Considering Classification Demand Response

This study is aimed at multiple complex uncertainties involving renewable energy and load,multiple economic and environmental optimization objectives,and demand response mechanisms.The power system including wind power and photovoltaic power generation is the research object.The engineering optimization method is used to study the power system operation optimization method,which lays a theoretical foundation for the establishment of a high-efficiency and robust power system operation method system for complex operating conditions.The specific research contents and research results are as follows:Firstly,according to the user's electrical characteristics,the load is divided into important loads,the load can be transferred,and the load can be controlled.For different attributes,the classification considers the demand response.Secondly,the random distribution characteristics of various uncertain factors are fully considered.A new set of uncertainties based on categorical probability and chance constraints is used to accurately describe the robustness of the scheduling scheme.Then,the robustness is incorporated into the collaborative optimization goal,and an environmental,economic,and robust multi-objective optimal scheduling model is established.Considering the mutual constraint relationship between economy,environmental protection and robustness,the pre-set subjectivity of robustness is eliminated.Next,the complex environmental economic robust multi-objective optimal scheduling model is proposed.The parallel multi-objective differential evolution algorithm is used to solve the model,and the decomposition strategy of the target task,the parallel allocation strategy of each computing engine(kernel)and the mutual communication strategy between each computing kernel are designed.Finally,the study designs a space-based virtual the shortest compromise method for the ideal solution,the solution yields the most satisfying constraints excellent compromise.The results of the example analysis verify the effectiveness and superiority of the proposed method.The research results can be extended to other areas such as smart grid planning and microgrid optimization operation.