Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station

Hydrogen energy storage system (HESS) has attracted tremendous interest due to its low emissions and high storage efficiency. In this article, the HESS is considered as an essential tool in hydrogen-integrated transportation and power systems to alleviate EV charging demand forecast error in a fast-charging station (FCS) and to solve voltage deviation problem due to the huge uptake of fast chargers on the utility grid. First, the wavelet transform (WT) method and long short-term memory (LSTM) neural network are combined to precisely predict the nonstationary traffic flow (TF). Then, a queueing theory-based model is developed to convert the predicted TF to the expected EV charging demand in FCS by considering charging service limitations and driver behaviors. Third, the charging demand prediction error is used to schedule the components in a HESS by considering their inherent properties and operational limits. As a result, the HESS configuration can be determined by analyzing the tradeoff between the investment cost and the monetary penalty due to charging demand forecast error and voltage deviation. The proposed solution is validated through a case study with mathematical justifications and simulation results.

Automated summary

This article focuses on the application of hydrogen energy storage system (HESS) in hydrogen-integrated transportation and power systems. The combination of wavelet transform and long short-term memory neural network is used to accurately predict the nonstationary traffic flow. A queueing theory-based model is then developed to convert the predicted traffic flow to charging demand, considering the limitations of charging service and driver behaviors. The HESS components are scheduled based on the charging demand prediction error, taking into account their properties and operational limits, to address the charging demand forecast error and voltage deviation.