Coincident peak prediction for capacity and transmission charge reduction

Meeting the ever-growing needs of the power grid requires constant infrastructure enhancement. There are two important aspects for a grid’s ability to ensure continuous and reliable electricity delivery to consumers: capacity, the maximum amount the system can handle, and transmission, the infrastructure necessary to deliver electricity across the network. The capacity and transmission costs are allocated to the end-users according to the cost causation principle. These charges are computed based on the customer’s demand on coincident peak (CP) events, time intervals when the system-wide electric load is highest. We tackle the problem of predicting CP events based on publicly available data of actual and forecast loads in different jurisdictions. We propose two different algorithms depending upon the availability of forecasts. In both cases, we generate Monte Carlo scenarios from the models, and derive estimators for predicting CP-day and exact CP-hour events. We back-test the prediction performance of our algorithms on two case studies. Our analysis has crucial practical implications for load curtailment through Battery Energy Storage System (BESS) solutions.