Application of hybrid meta-heuristic method to unit commitment in power systems

In this paper, a new hybrid meta-heuristic method is proposed to solve the unit commitment (UC) problem in power systems effectively. The proposed method focuses on the global optimization in a sense that a generation company need carry out the cost reduction under competitive environment. The objective of UC is to minimize operation-cost while satisfying the constraints. The unit commitment problem is hard to solve due to the complexity. The problem formulation may be written as a nonlinear mixed-integer problem that consists of two kinds of variables. Namely, one is a discrete variable to determine on-off state of generators while the other is a continuous one to evaluate output of generators. This paper proposes a new hybrid meta-heuristic method that makes use of TS-EPSO techniques and evaluates solutions with two layers. 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 one of meta-heuristics that introduces the adaptive memory function into the hill-climbing method of local search to escape from a local minimum. It is very effective for solving a combinatorial optimization problem efficiently. EPSO improves the performance of particle swarm optimization (PSO) of swarm intelligence though the evolutionary strategy for tuning the parameters. EPSO has better performance in dealing with an optimization problem with continuous variables. The effectiveness of the proposed method is successfully applied to a sample system.