Solving economic dispatch problem using particle swarm optimization by an evolutionary technique for initializing particles

Abstract

One of the important optimization problems regarding power system issues is to determine and provide an economic condition for generation units based on the generation and transmission constraints, which is called Economic Dispatch (ED). The nonlinearity of the present problems makes conventional mathematic methods unable to propose a fast and robust solution, especially when the power system contains high number of generation units. In the present paper, an evolutionary modified Particle Swarm Optimization (PSO) is used to find fast and efficient solutions for different power systems with different generation unit numbers. The proposed algorithm is capable of solving the constraint ED problem, determining the exact output power of all the generation units. In such a way, proposed algorithm minimizes the total cost function of the generation units. To model the fuel costs of generation units, a piecewise quadratic function is used and B coefficient method is used to represent the transmission losses. The acceleration coefficients are adjusted intelligently and a novel algorithm is proposed for allocating the initial power values to the generation units. The feasibility of the proposed PSO based algorithm is demonstrated for four power system test cases consisting of 3, 6, 15, and 40 generation units. The obtained results are compared to existing results based on previous PSO implementing and Genetic Algorithm (GA). The results reveal that the proposed algorithm is capable of reaching a higher quality solution including mathematical simplicity, fast convergence, and robustness to cope with the non-linearities of economic load dispatch problem.