Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms

Abstract

In this paper, Cuckoo search algorithm (CSA) is suggested for determining optimal operation parameters of the combined wind turbine and hydrothermal system (CWHTS) in order to minimize total fuel cost of all operating thermal power plants while all constraints of plants and system are exactly satisfied. In addition to CSA, Particle swarm optimization (PSO), PSO with constriction factor and inertia weight factor (FCIWPSO) and Social Ski-Driver (SSD) are also implemented for comparisons. The CWHTS is optimally scheduled over twenty-four one-hour interval and total cost of producing power energy is employed for comparison. Via numerical results and graphical results, it indicates CSA can reach much better results than other ones in terms of lower total cost, higher success rate and faster search process. Consequently, the conclusion is confirmed that CSA is a very efficient method for the problem of determining optimal operation parameters of CWHTS.

Designing a power system stabilizer using a hybrid algorithm by genetics and bacteria for the multi-machine power system

“…The application of intelligent meta-heuristic methods for controlling complex and nonlinear systems has been widely used in various fields of science and technology in the last decade [46]- [54]. Sometimes, such problems can have a very complex objective function or model constraints, depending on their actual and practical nature.…”