Thermal Unit Commitment Strategy with Solar and Wind Energy Systems Using Genetic Algorithm Operated Particle Swarm Optimization

This paper presents a methodology for solving unit commitment problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process of the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, mutation are applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best with particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods.