A Genetic Algorithm-Based Cost Optimization Model To Solve Power Economic Dispatch Problem

Abstract

In an electric power system, Economic Dispatch Problem is the optimal allocation of the load demand among the running generators while satisfying the power balance equations and the unit’s operating limits. A continuous balance must be maintained between electrical generation and varying load demand, while the system frequency, voltage levels, and security also must be kept constant. Furthermore, it is desirable that the cost of such generation be minimal. Numerous classical techniques such as Lagrange based methods, linear programming, non-linear programming and quadratic programming methods have been proposed for in the literature. The solution of the economic dispatch problem using the classical approach presents some limitations in its implementation. One of such limitations is that there exists the possibility for this approach to be caught at the local minima when the cost functions are non-convex or piecewise discontinuous in the functional space. Furthermore, treatments of operational constraints are very difficult using the classical approach. This thesis explored the application of Genetic Algorithm (GA) to solve the problem of economic power dispatch in order to circumvent the above stated limitations. Genetic algorithms are numerical optimization algorithms based on the principle inspired from the genetic and evolution mechanisms observed in natural systems and population of living being. The GA method is implemented using C++ in order to be able to verify the performance of the GA approach in practical applications. Case studies of three generators are considered for two different power demands. Economic dispatch without transmission losses were considered in both case studies. Results showed a significant improvement with the method of genetic algorithm over the classical method.