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Unit commitment using the ant colony search algorithm

The paper presents an ant colony search algorithm (ACSA)-based approach to solve the unit commitment (UC) problem. This ACSA algorithm is a relatively new meta-heuristic for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as a constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, distributed computation avoids early convergence, and the greedy heuristic helps find adequate solutions in the early stages of the search process. The ACSA was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. The UC problem solved using the proposed approach is subject to real power balance, real power operating limits of generating units, spinning reserve, start up cost, and minimum up and down time constraints. The proposed approach determines the search space of multi-stage scheduling followed by considering the unit transition related constraints during the process of state transition. The paper describes the proposed approach and presents test results on a 10-unit test system that demonstrates its effectiveness in solving the UC problem.