Improved PSO Algorithm based Optimal Operation in Power Systems Integrating Solar and Wind Energy Sources

This paper proposes an improved particle swarm optimization (PSO) algorithm for optimal operation of a power system including the solar and wind energy sources. The algorithm is to minimize operating costs of the hybrid power system. The proposed PSO algorithm is one of the standard PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO algorithm to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. Additionally, the inertia weight which is considered as a trade-off factor for the local and global search abilities of the PSO algorithm is also linearly decreased instead of a fixed constant value to improve its performance. The improved PSO algorithm based optimal operation of the hybrid power system with and without solar and wind powers is considered on a standard IEEE 30-bus 6-generator 41transmission line test power system. The numerical results demonstrate the capabilities of the proposed algorithm to generate optimal solutions of a hybrid power system considering the solar and wind energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to optimize the operating costs of the hybrid power system.