Multiple Objective Optimizations for Energy Management System under Uncertainties

Recently, micro-grid gains more and more concerns, because it is flexible and environmentally friendly. Optimization of the distributed generators operation in micro-grid is a complicated and challenging task, a multi objective optimal model was designed to cut off the operation cost, improve the economic benefits and reduce the emission. However, the randomness of the renewable energy generation and load demand makes the decision process much more complicated. Chance constrained programming (CCP) was employed to deal with these uncertainties. Besides, the satisfaction degree of the decision was taken into consideration to coordinate the conflicts among different targets. Through the weighted satisfaction degree and coordinate degree, the multi-objective programming can be transformed into single-objective programming. To gain the solution of the optimization problem, genetic algorithm was utilized to search for the optimal strategy. To verify the validity of the proposed model, an energy management system of micro-grid with five types distributed generators was taken as the case study. The results indicate the effectiveness of the proposed method.