Abstract: Unit commitment is a typical issue involving large-scale complicated nonlinear optimization. The difficulty of solving unit commitment increases nonlinearly with the increase of system scale. Effectively solving this problem has always been a hotspot and difficulty in power system research. In this paper, a Binary Harmony Search Particle Swarm Optimization (BHSPSO) algorithm is proposed for unit commitment problem. Firstly, the information sharing mechanism of particle swarm optimization is incorporated into the process of learning the harmony memory of the harmony search algorithm. And then the heuristic intelligent strategy is used to deal with the complex constraints of the time series. The spinning reserve constraints are repaired according to the priority of the unit, and an "on-off-on" repair strategy is designed to deal with the constraints of minimum power-off time and power-on time, effectively improving the quality of the results obtained. The BHSPSO algorithm is applied to standard calculation examples of six systems with 10, 20, 40, 60, 80 and 100 units. The simulation results show that the proposed algorithm has advantages of simplicity, fast convergence and strong robustness. This research offers a new approach for efficient solution of unit commitment optimization problem.

Key words: economic running of power house, unit commitment, binary harmony search, particle swarm optimization, repair strategy