Application of the Crow Search Algorithm for Economic Environmental Dispatch

The combined economic and emission dispatch (CEED) problem is a multi-objective non-linear optimization problem with several constraints. Its target is searching for optimum generation outputs of available generating units in a power system to supply the electrical loads and transmission losses at minimum generation costs combined with minimum pollutant emissions. To achieve an optimal solution for this problem, this paper proposes an application of a new meta-heuristic optimizer called crow search algorithm (CSA). CSA is inspired from the intelligent attitude of crows. It is very simple since it has only two adjustable parameters. The CSA is employed and developed in MATLAB for solving the CEED problem. It is applied to four test systems consisting of three thermal generators, the standard IEEE 30-bus model system, ten and forty thermal generators. A comparison between the developed CSA and other optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and hybrid genetic algorithm (HGA) is executed in terms of solution equality and computation efficiency. Simulation results demonstrate clearly the effectiveness of the proposed CSA in solving the CEED problem since its obtained solution is faster and more efficient than that obtained by using other techniques.