Neural Computing and Applications
This paper presents an implementation of an effective cuckoo search algorithm (ECSA) for solving hydrothermal scheduling (ST-FH-HTS) problems considering transmission power losses, nonconvex fuel cost function of thermal units, and transmission grid constraints such as the voltage of load buses, voltage of generator buses, capacity of transmission lines. The ECSA method has been developed based on the conventional cuckoo search algorithm (CCSA) which is a recently developed meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other birds of other species for solving optimization problems. In the ECSA method, new eggs generated via Lévy flights are replaced partially, and the newly generated eggs are then evaluated and ranked at once. Moreover, there is a boundary by the best solution technique proposed for replacing the invalid dimension in order to improve convergence rate and performance. The performance of ECSA has been investigated via comparisons with other methods by testing on eleven systems. In addition, the ECSA and other popular meta-heuristic algorithms such as CCSA, conventional particle swarm optimization, conventional differential evolution, and conventional Bat algorithm have been tested on a large-scale system with 50 thermal units and four hydrounits considering constraints from an IEEE 118-bus transmission line grid. The result comparisons from ECSA with other methods for 12 test systems have revealed that ECSA method is very efficient for solving ST-FH-HTS problems. Therefore, the ECSA can be a favorable method for solving the ST-FH-HTS problems.