GA-PSO Algorithm Based Day-ahead Economic Schedule for Isolated Microgrid

As to the problem that the global optimal solution of the day-ahead schedule is complex and hard to convergence for isolated microgrid that includes many types of power supplies and loads, the particle swarm optimization combined with genetic algorithm (GA-PSO) is adopted to solve this mixed-integer nonlinear problem. Initially, the day-ahead scheduling objective function and the scheduling models of micro turbine, battery and all kinds of loads are built, in which the battery discharge depth model is modified to describe the charging and discharging cost of battery more reasonably. Then loads are directly scheduled according to the compensation contract. By considering the operation cost of microgrid, the objective function is built to maximize the operation benefits of the microgrid on the premise of guaranteeing reliability. Then a new GA-PSO method is proposed, and it is verified through a microgrid example. The result shows that the day-ahead schedule problem can be solved effectively by using the proposed algorithm. In addition, this method acquires a quick convergence as to scheduling optimization of microgrid that includes multiple periods and multiple hybrid units.