Bayesian Inference with MILP Dispatch Models for the Probabilistic Prediction of Power Plant Dispatch

To model future energy systems and their markets, it is important to understand the underlying dynamics of their components correctly. Common fundamental market models simulate power plant dispatch and rely on expert guesses for their parametrization. This is a significant source of uncertainty regarding the model results. We propose a method to predict the dispatch of power plants probabilistically, based only on historic open data. We use published operational data of the power plant as well as electricity and fuel market prices. These are fed into a fundamental dispatch model as part of a Monte Carlo simulation to obtain probabilistic estimates of the dispatch parameters. The resulting parameter distributions are used to probabilistically predict power plants' dispatch. We apply this method to three German natural gas combined cycle power plants. The results show that our approach increases the prediction accuracy in comparison to deterministic parameter estimation, and additionally yields further information on the certainty of the dispatch predictions.