A Genetic Algorithm-based Hybrid Optimization Approach for Microgrid Energy Management

This paper proposes a novel Meta-heuristic based hybrid optimization method for Microgrid energy management system. First, microgrid energy management problem is modeled as a mixed integer nonlinear programming with the consideration of quadratic fuel cost of distributed generators and their startup/shut-down states. In order to obtain a favorable solution, a hybrid solution procedure combined quadratic programming and genetic algorithm is proposed to solve the problem. Then, the proposed method is verified via numerical simulation. Through the comparison of optimization result with IBM ILOG CPLEX Optimizer, simulation shows that the proposed algorithm has the advantage of finding better scheduling solution which leads to less operating cost.