Genetic Algorithm-based Cost Optimization Model for Power Economic Dispatch Problem

In any power generation and distribution system, a continuous balance must be maintained between electrical generation and varying load demand, while the system frequency, voltage levels, and security must all be kept constant and the cost of generation maintained at minimal level. Numerous classical techniques such as Lagrange, linear programming, non-linear programming and quadratic programming-based methods have been proposed for attaining these objectives. The attendant weaknesses to these methods include economic dispatch problem-induced non-optimal power flow and cost increment. Classical approach-based solution to the economic dispatch problem suffered some limitations, which include restriction to the local minima while the cost functions show non-convex or piecewise discontinuity in the functional space. Furthermore, treatments of operational constraints are very difficult using the classical approach. This paper reports on the formulation of a Genetic Algorithm (GA)-based model as a solution to the problems of economic power dispatch. The model considered GA as numerical optimization algorithms based on the principle inspired from the genetic and evolution mechanisms observed in natural systems and population of living being. The implementation of the model produced an application whose performance evaluation on power demand and transmission loss of three power generating systems and three Nigerian Thermal Power Plants showed superior performances of the new model over some existing ones.