| Abstract: | This paper presents opposition-based differential evolution to determine the optimal hourly schedule of powergeneration in a hydrothermal system. Differential evolution (DE) is a population-based stochastic parallel searchevolutionary algorithm. Opposition-based differential evolution has been used here to improve the effectivenessand quality of the solution. The proposed opposition-based differential evolution (ODE) employs opposition-basedlearning (OBL) for population initialization and also for generation jumping. The effectiveness of the proposedmethod has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermalmulti-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valvepoint loading. The results of the proposed approach are compared with those obtained by other evolutionarymethods. It is found that the proposed opposition-based differential evolution based approach is able to providebetter solution |
