Electricity generation scheduling with large-scale wind farms using particle swarm optimization

Large-scale integration of wind power in the electricity system presents some planning and operational difficulties, which are mainly due to the intermittent and difficult nature of wind prediction process. Therefore it is considered as an unreliable energy source.This paper presents a new approach for solving the generation scheduling (GS) problem. It will consider the reserve requirement, load balance and wind power availability constraints. The particle swarm optimization (PSO) method is suggested to deal with the equality and inequality constraints in the GS problem. The proposed PSO is applied to a 12-unit test system (including 10 conventional thermal generating units and 2 wind farms) to determine the acceleration constants of proposed PSO and the global variant-based passive congregation PSO (GPAC). Employing these constants which correspond to the best total cost function, the performance of proposed PSO and GPAC are determined, through comparison of their results for three specific test systems. Evaluation of the solution for these test systems demonstrates that near optimal schedules are obtained with application of proposed PSO.