Optimal Unit Commitment with Ant Colony Algorithm

Abstract

This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony algorithm (ACA) 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. The ACA 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, startup 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 of production of 50 power plant units. UC problem limits the application of traditional optimization techniques to small size system. Recently, meta-heuristic approaches became popular in the effort to overcome shortcomings of traditional optimization techniques. Techniques such as genetic algorithms, evolutionary programming, simulated annealing, and tabu search have been widely investigated to solve the UC problem. These methods can accommodate more complicated constraints and are claimed to produce solutions of improved quality. The ACA is a meta-heuristic technique proposed to find a near optimal solution to the UC problem. The ACA was inspired by the behavior of real ants which are capable of finding the shortest path from food sources to the nest without using visual cues. The technique has been tested successfully on diverse complex combinatorial optimization problems. Also, it has been shown that the ant colony method performs with little variability over problem diversity or random number seed. In addition, the ACA introduces an entirely new solution at each iteration, while other meta-heuristic optimization techniques are based on improvement of the solution or set of solutions obtained from the previous iteration. During the solution process of the UC problem using the ACA, the obtained solution is always feasible. Moreover, the ACA does not require major assumptions and approximations that limit the solution space.