Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration

As the application of wind power energy is rapidly developing, it is very important to analyze the effects of wind power fluctuation on power system operation. In this paper, a model of thermal unit commitment problem with wind power integration is established and chance constrained programming is applied to simulate the effects of wind power fluctuation. Meanwhile, a combination of quantum-inspired binary gravitational search algorithm and chance constrained programming is proposed to solve the thermal unit commitment problem with wind power integration. In order to reduce the searching time and avoid the premature convergence, a priority list of thermal units and a local mutation adjustment strategy are utilized during the optimization process. The priority list of thermal units is based on the weight between average full-load cost and maximal power output. Then, a stochastic simulation technique is used to deal with the probabilistic constraints. In addition, heuristic search strategies are used to handle deterministic constraints of thermal units. Furthermore, the impacts of different confidence levels and different prediction errors of wind fluctuation on system operation are analyzed respectively. The feasibility and effectiveness of the proposed method are verified by the test system with wind power integration, and the results are compared with those using binary gravitational search algorithm and binary particle swarm optimization. The simulation results demonstrate that the proposed quantum-inspired binary gravitational search algorithm has a higher efficiency in solving thermal unit commitment problem with wind power integration.