Fuzzy unit commitment using the Ant Colony Search Algorithm

Abstract

Solving the deterministic unit commitment (UC) model may yield the optimal commitment for each unit for a certain condition of the power system. Changing the system condition due to a sudden change or uncertainties may render the obtained solution infeasible or inapplicable to the system under study. This paper presents a fuzzy model to handle uncertainties associated with the UC problem and introduces a methodology based on the Ant Colony Search Algorithm (ACSA) to find a near-optimal solution to the problem. The ACSA is a meta-heuristic technique for solving hard combinatorial optimization problems, a class of problems which the UC problem belongs to. 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 proposed approach uses a fuzzy comparison technique and generates a fuzzy range of the cost that reflects problem uncertainties. Test results on a 10-unit system demonstrate the viability of the proposed technique to solve the UC problem considering uncertainties.