Energy management systems for forecasted demand error compensation using hybrid energy storage system in nanogrid

Abstract

This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. The power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange.