A new meta-heuristic method for profit-based unit commitment under competitive environment

This paper proposes a new hybrid meta-heuristic method for profit-based unit commitment (PBUC) that considers units with nonlinear cost function. The proposed method aims at global optimization to carry out profit maximization under competitive environment. The objective of the traditional UC is to minimize operation-cost while satisfying the constraints. However, power system operation needs reformulate tasks that reflect the changes due to the deregulated power systems. As a result, GENCO is interested to determine generation scheduling from a standpoint of maximizing profit under competitive environment. The problem may be formulated as PBUC that corresponds to a nonlinear mixed-integer problem. It is hard to solve due to the complexity. In this paper, a new hybrid meta-heuristic method is proposed to solve PBUC. It makes use of improved TS-EPSO techniques that evaluates solutions with two layers of meta-heuristics. Layer 1 determines the on-off state of generators with Tabu Search (TS) while Layer 2 evaluates output of generators with the evolutionary particle swarm optimization (EPSO). TS is very useful for solving a combinatorial optimization problem efficiently. EPSO has better performance in dealing with an optimization problem with continuous variables. In this paper, TS-EPSO is improved to give more accurate solutions with less CPU time. The proposed method determines a new load curve for maximizing the profit finally. The effectiveness of the proposed method is successfully applied to a sample system.