Solving power system unit commitment with wind farms using multi-objective quantum-inspired binary particle swarm optimization

To reduce air pollutant emissions, it is essential to develop energy-saving and emission-reducing in power generation scheduling. A double-objective function considering both costs and emissions is proposed, and to consider the randomness and uncontrollability of wind power, interval forecasting information of wind power with a certain probability is used to determine the required spinning reserve capacity. Simultaneously, the paper presents a new method to solve it. The proposed method uses a new Multi-objective Quantum-inspired Binary particle swarm optimization (QBPSO) for the unit on/off problem and the primal-dual interior point method for load economic dispatch problem. Based on the QBPSO, the article introduces the Pareto optimal concept and the external archive to it. The paper also adopts the heuristic adjusted regulations to ensure the whole algorithm to search the optimal particle in the feasible region. The proposed method is applied to power systems which are composed of 10-units with 24-h demand horizon and a certain proportion of wind farms. The simulation results prove the validity of the model and algorithm. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4798487]