Battery Management Based on Genetic Algorithm Optimization

Energy storage system can improve the capacity of renewable energy resources in the micro-grid like solar energy,wind energy and so forth. Conventional researches consider only the standard deviation of the interactive power between micro-grid and distribution network. Analyzing the charging and discharging strategy of the battery to get the lowest cost and the highest profit from the micro-grid,it is important to consider the battery capacity,the initial state of charge and the optimization effect,then the most economic model of the interaction power is established in the maximum power tracking of the output of the wind turbine. Genetic algorithm,an efficient global optimization search algorithm,based on natural selection and genetic theory,has the advantages of fast convergence,simple calculation,versatility and so on. Finally,an algorithm example on the battery state and power balance constraints optimizes battery scheduling to minimize the interactive power cost by the genetic algorithm. Thus the interactive power economy can be improved greatly on the basis of keeping the interaction power volatility at a certain value.The result shows that this research can play a guiding role in making a more comprehensive storage battery strategy of charging and discharging in the future.