Unit commitment considering effect of load and wind power uncertainty

With the annual capacity growth of wind power integration, the stochastic wind power makes it increasingly difficult to optimize traditional unit commitment with fixed load and wind power percentage. Considering load and wind powder uncertainty, the multiple scenario model of load and wind power was established using scenario reduction techniques. To explore effect of load and wind power uncertainty, the positive and negative spinning reserve needs of the unit commitment were determined based on the maximum variation ranges of load and wind power under different scenarios. Considering effect of different loads and wind powers under different scenarios on the unit dispatch optimization, taking the weighted sum of mean and variance of generating cost under all scenarios as the objective function, a model for unit dispatch optimization that considers load and wind power uncertainty was established. This model was solved using improved particle swarm optimization (PSO) algorithm. PSO-oriented dynamic adjustment of unit output range was proposed in order to improve the convergence performance of the PSO algorithm during iteration. The accuracy and validity of the proposed model and algorithm were verified by a case study based on a typical 10-unit commitment.