Unit Commitment optimization using improved Genetic Algorithm

This paper shows an investigation for solving the Thermal Unit Commitment (UC) problem by utilizing of Genetic Algorithm advantages. A Parallel Structure was developed to handle the infeasibility problem in a structured and improved Genetic Algorithm (GA) which provides an effective search and therefore greater economy. In addition, this proposed method could help us to obtain better performance by using both computational methods and classification of unit characteristics. Typical constraints such as; unit maximum/minimum MW limit, system power balance, minimum up and down times, start up and shut-down ramps, have been considered. A number of important UC control parameters have been identified accordingly. This method was developed and tested by using C# program. Tests have been performed on 10 and 20 units systems over a scheduling period of 24 hours. The final results were compared with those obtained genetic schemes in other same research.