Coincident Peak Prediction for Capacity and Transmission Charge Reduction

TL;DR

This paper presents a novel approach for predicting coincident peak events in power grids using load data and Monte Carlo methods, aiming to reduce capacity and transmission costs.

Contribution

It introduces a scenario-based Monte Carlo prediction framework for CP events, incorporating adaptive thresholds and practical load management strategies.

Findings

Effective CP event prediction improves grid cost management.

Adaptive threshold strategies enhance prediction accuracy.

Load curtailment with BESS can reduce peak-related costs.

Abstract

Meeting the ever-growing needs of the power grid requires constant infrastructure enhancement. There are two important aspects for a grid 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. These capacity and transmission costs are then allocated to the end-users according to the cost causation principle. These charges are computed based on the customer 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 actual load and forecast data on the load of different jurisdictions. In particular, we identify two main use cases depending on the availability of a forecast. Our approach generates scenarios and formulates Monte-Carlo estimators for predicting CP-day and exact CP-hour events. Finally, we backtest the prediction performance of strategies with adaptive threshold for the prediction task. This analysis enables us to derive practical implications for load curtailment through Battery Energy Storage System (BESS) solutions.