Solution of unit commitment problem using gravitational search algorithm

Abstract

In this article, gravitational search algorithm (GSA) is proposed to solve thermal unit commitment (UC) problem. The objective of UC is to determine the optimal generation of the committed units to meet the load demand and spinning reserve at each time interval, such that the overall cost of generation is minimized, while satisfying different operational constraints. GSA is a new cooperative agents’ approach, which is inspired by the observation of the behaviors of all the masses present in the universe due to gravitation force. The proposed method is implemented and tested using MATLAB programming. The tests are carried out using six systems having 10, 20, 40, 60, 80 and 100 units during a scheduling period of 24h. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, simulated annealing (SA), genetic algorithm (GA), evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO), improved PSO (IPSO), hybrid PSO (HPSO), binary coded PSO (BCPSO), quantum-inspired evolutionary algorithm (QEA), improved quantum-inspired evolutionary algorithm (IQEA), Muller method, quadratic model (QM), iterative linear algorithm (ILA) and binary real coded firefly algorithm (BRCFF).