Unit Commitment by Evolving Ant Colony Optimization

Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on the systems with number of generating units in the range of 10 to 60. The test results are encouraging and compared with those obtained by other methods.