An Improved Ant Colony Search Algorithm for Unit Commitment Application

This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony search algorithm (ACSA) is a meta-heuristic technique for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, while the greedy heuristic helps find adequate solutions in the early stages of the search process, and finally distributed computation avoids early convergence. The ACSA was inspired by the behavior of real ants that are capable of finding the shortest path from food sources to the nest without using visual cues. The constraints used in the solution of the UC problem using this approach are: real power balance, real power operating limits of generating units, spinning reserve, start up cost, and minimum up and down time constraints. The approach determines the units schedule followed by the consideration of unit transition related constraints. The proposed approach is expected to yield a better operational cost for the UC problem and use less computational resources compared to the traditional ACSA