Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Ant Colony Optimization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Generation Scheduling problem by Intelligent Genetic Algorithm
Computers and Electrical Engineering
Hi-index | 0.00 |
The optimum economic operation and planning of electric power generation systems occupies a crucial position in the electric power industry. Unit commitment (UC) is an important function in generation resource management. Moreover, it is nowadays critical to incorporate reliability analysis of the power system into its design of operation strategy. For this purpose, equipment malfunction or failure should be considered in unit commitment. In this paper, first the model for UC considering generator outages is formulated, where the reliability requirement is incorporated into the spinning reserve constraint in the optimization design. Then, a mixed binary- and real-coded particle swarm optimization (PSO) is developed to locate the optimum generation combination. A 10-generator test power system is used to verify the effectiveness of the proposed approach along the 24-h planning horizon. A comparative study is conducted to examine the impact of reliability constraint on the optimal solution obtained. Furthermore, comparison is made between the proposed method and other methods including both analytical and meta-heuristic algorithms.