Stochastic Economic-Emission Load Dispatch Using Particle Swarm Optimization

Abstract

The ELD problem in a power system is to determine the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying all practical constraints. Since optimum economic dispatch is not environmentally the best solution so it is required to reduce the pollution or emissions as well. Hence the classical economic dispatch problem is modified to economic-emission dispatch problem. Further, there are many inaccuracies and uncertainties in the input information which lead to deviations from optimal operation and cause an increase in the cost over the optimal value, therefore stochastic model is formed to solve the practical problem. Here, an attempt has been made to solve the stochastic economic-emission load dispatch problem using Particle swarm Optimization. Recently evolution search techniques are used to solve ELD problem. The technique used in this thesis is Particle Swarm Optimization (PSO). There are number of applications of PSO. It has emerged as a useful tool for engineering optimization. In PSO, the behavior of each individual is affected by the best local and the best global individual to help it fly through a hyperspace. Moreover, an individual can learn from its past experiences to adjust its flying speed and direction. In this thesis, six unit test system has been considered for economic and emission dispatch. Deviations due to unsatisfied load demand are considered as another objective function. Weighting method is used to calculate the minimum and maximum values for different objective functions. Constraints are handled as another objective while calculating the membership function. PSO search method is used to obtain the best optimal solution using fuzzy cardinal priority ranking.