Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty

The penetration of wind energy sources to power systems has significantly increased in recent years. With variable and uncertain wind power output, the payment and market-clearing price (MCP) may vary in different cases. In this paper, a methodology to quantitatively model the payment cost minimization (PCM) considering the effects of wind power from a probabilistic viewpoint is presented. The autoregressive moving average (ARMA) method with normal distribution of wind forecast error is used to model a time series of wind speed. Based on the wind turbine power curve, the probability distribution of wind power output can be obtained. Then, Monte Carlo simulation (MCS) is used to produce random samples of wind speed, and the genetic algorithm is applied to solve PCM for each sample. The proposed methodology and its solution are verified with simulation studies of two sample systems. The probabilistic distribution results can give consumers an overview of how much they should pay in a probabilistic sense. Further, the simulation results can serve as a lookup table to provide useful input for more refined unit commitment, and also provide a benchmark for future research works on PCM considering wind power.