Evaluating the effectiveness of storage control in reducing peak demand on low-voltage feeders

Abstract

Uptake of low carbon technologies could likely lead to increased demand in distribution networks and consequently could impose additional stress on the networks. Battery energy storage systems (BESS) are identified as a feasible alternative to traditional network reinforcement. This study analyses two BESS scheduling algorithms (model predictive control (MPC) and fixed schedule) supplied with forecasts from five methods for predicting demand on 100 low-voltage feeders. Results show that forecasting feeders with higher mean daily demand produce lower mean absolute errors and better peak demand reduction. MPC with simple error improves peak reduction over fixed schedule for feeders with lower mean daily demand.