Unit Connuitment Solution Methodology Using Genetic Algorithm

Solution methodology of unit commitment using genetic algorithms (Gas) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem-specific operators are proposed for the satisfaction of time-dependent constraints. Problem formulation, representation, and the simulation results for a 10-generator-scheduling problem are presented.