Ant colony search algorithm for unit commitment

In this paper, the ant colony search algorithm (ACSA) is proposed to solve the thermal unit commitment problem. ACSA is a new cooperative agents approach, which is inspired by the observation of the behaviors of real ant colonies on the topic of ant trial formation and foraging methods. In the ACSA, a set of cooperating agents called "ants" cooperates to find good solution for unit commitment problem of thermal units. The merits of ACSA are parallel search and optimization capabilities. The problem is decomposed in two sub-problems. The unit commitment sub-problem is solved by the ant colony search algorithm method and the economic dispatch sub-problem is solved by the lambda-iteration method. The unit commitment problem is formulated as the minimization of the performance index, which is the sum of objectives (fuel cost, start-up cost) and some constraints (power balance, generation limits, spinning reserve, minimum up time and minimum down time). This proposed approach is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA) on the 10 unit system.