Unit Commitment solution using an optimized genetic system

This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed.