Evolving ant colony optimization based unit commitment

Authors:
K. Vaisakh;L. R. Srinivas
Affiliations:
Department of Electrical Engineering, AU College of Engineering, Andhra University, Visakhapatnam 530003, AP, India;Department of Electrical and Electronics Engineering, S.R.K.R. Engineering College, Bhimavaram 534204, AP, India
Venue:
Applied Soft Computing
Year:
2011

Citing 4
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Abstract

Ant colony optimization (ACO) was inspired by the observation of natural behavior of real ants' pheromone trail formation and foraging. Ant colony optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes 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, startup cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on two different systems. The test results are encouraging and compared with those obtained by other methods.