Economic Power Optimization for Microgrids Considering Operational Benefits
The off-grid microgrid power mainly generated by wind and light has the characteristics of randomness and un-controllability,which leads to poor operation efficiency.In order to improve the operation efficiency of off-grid microgrid,an improved particle swarm optimization(PSO)algorithm is adopted.The convergence speed and precision of the algorithm are improved by introducing the inertia weight and the learning factor of linear change after chaos disturbance.With the lowest operation and maintenance cost,pollution control and fuel cost as the objective function,a microgrid model of wind,light,firewood and storage was built to verify the proposed algorithm.The simulation results show that the total cost of the microgrid system optimized by the proposed algorithm is effectively saved,and the effectiveness of the proposed algorithm for off-grid microgrid power optimization is verified.
off-grid microgridparticle swarm algorithmoperation and maintenance costpollution control costfuel cost
万方数据
维普
