PNNL

Abstract

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are to accurately determine the peak load days and hours, and quantify and reduce uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate i) the probability of the next operation day containing the peak hour of the month and ii) the probability of an hour to be the peak hour of the day. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the two hours with the highest probabilities.