Unit commitment by a tabu-search-based hybrid-optimisation technique

The paper introduces an application of hybrid-particle-swarm optimisation and sequential-quadratic-programming technique (hybrid PSO-SQP) guiding the tabu search (TS) method for solving the unit-commitment (UC) problem. The UC problem is divided into a combinatorial subproblem in unit-status variables and a nonlinear-programming subproblem in unit-power-output variables. TS a powerful heuristic method that uses the history of the search. It has proved to be a successful strategy for combinatorial-optimisation problems. The central idea of the TS method is the use of adaptive memory, which prevents convergence to local optima, by driving the search to different parts of the search space. The TS method with an improved random-perturbation-of-current-solution scheme is proposed to solve the combinatorial subproblem. A simple scheme for generating the initial feasible commitment schedule for the TS method is also proposed. The nonlinear programming subproblem is solved using the hybrid PSO-SQP technique. A practical system, Neyveli thermal-power-station (NTPS), India, and several example systems were studied to validate the robustness and effectiveness of the proposed technique for the UC problem.