Impact of forecast on control methods for customer-sited battery storage

Abstract

This work explores the effect of forecast on model-based methods for control of energy storage. There are high-value applications of energy storage that require advanced control. Model Predictive Control (MPC), an optimization-based technique is a widely used example of model-based control. Depending on the application, MPC requires forecast of variables like demand, renewable generation, and electricity prices. We explore the performance of MPC using different forecast techniques, with varying levels of error. We analyse the deterioration of system performance when forecast error increases, for a customer-sited storage system under different settings.