Towards an Open Energy Management System for Integrated Energy Storage and Electric Vehicle Fast Charging Station

Abstract

SINTEF’s solar energy infrastructure, which integrates photovoltaic (PV) panels, meteorological instruments, inverters, and data pipelines, enables real-time data acquisition and visualization. Building on this infrastructure, we developed AI-based algorithms for forecasting PV power output and analyzing the performance of the system and various PV panel types. The potential of this system is significantly enhanced by integrating battery storage, electric vehicle (EV) charging stations, external energy market pricing, advanced meteorological forecasting, and demand forecasting. These integrations support the development of a comprehensive open energy management system (EMS) that facilitates localized energy production and adaptive demand response. In this paper, we outline the key elements of an open EMS that includes PV, batteries, and EV charging station. We describe a prototype and discuss further developments needed in this area. Our approach leverages machine learning to optimize energy flow decisions, with the inclusion of rule-based models to guide and explain these decisions. This work addresses the gap in applying theoretical open EMS models to practical residential and commercial settings, aiming to provide a dynamic platform where forecasting and optimization methods are refined and implemented in real-world scenarios.