Research On Optimization Operation Of Microgrid Based On Genetic Simulated Annealing Particle Swarm Algorithm

China is developing towards green power generation with the increasing awareness of the people to protect the environment.At present,we are facing the pressure of objective facts such as shortage of fossil fuels and environmental pollution.In the field of microgrids,the main purpose of this article is to optimize the operation of the microgrid.First of all,based on the different goals pursued by the microgrid during operation,the lowest cost and the smallest pollution cannot be satisfied at the same time.From the economic and environmental considerations,this paper establishes a comprehensive benefit objective function including photovoltaic power generation,wind power generation,micro gas turbines,fuel cells,waste incineration power generation and the micro-grid model of the power plant,that simultaneously considers the cost of micro-grid fuel,maintenance and management costs,depreciation costs,interaction costs with the public grid,and pollutant disposal costs.Secondly,based on the prototype of the traditional particle swarm optimization algorithm(PSO),this paper improves the inertial weight iteration method,the convergence speed and convergence rationality.Firstly this paper combines the simulated annealing algorithm(SA)to avoid the traditional particle swarm optimization algorithm which is easy to fall into the defect of local optimal solution;then combined with genetic algorithm(GA),the three major elements of selection operator,crossover operator and mutation operator are used reasonably,and the global search of probability meaning is effectively obtained to be more suitable for the way microgrid works.Finally,based on the established comprehensive benefit objective function,this paper uses an improved genetic simulated annealing particle swarm optimization algorithm to obtain a more suitable operation mode for microgrid.The genetic simulated annealing particle swarm optimization algorithm,simulated annealing particle swarm optimization algorithm and traditional particle swarm optimization algorithm are tested in pairs.The results verified that the improved genetic simulated annealing particle swarm optimization algorithm is more scientific and feasible.