Riccardo Ramaschi

This paper examines the diverse functionalities of Battery Energy Storage Systems (BESS) in Commercial and Industrial (C&I) settings, particularly when integrated with Photovoltaic (PV) systems (C&I PV-BESS). While these systems provide significant benefits in energy efficiency and sustainability, they also introduce concerns regarding fire safety. To address these risks, this study reviews the associated safety hazards and conducts a comprehensive comparison of major international standards aimed at mitigating fire hazards in C&I PV-BESS installations. Additionally, the paper outlines best practices for system installation and management, focusing on key safety aspects such as cell design, module and rack construction, operational protocols, fire suppression systems, and manufacturing processes. Despite the ongoing challenge of establishing universally adopted fire prevention standards for C&I PV-BESS, this paper proposes practical guidelines to enhance safety on these installations. It also highlights innovative solutions to improve the safe operation and long-term reliability of these integrated systems.

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This paper studies the impact of forecast accuracy on total electric cost of a simulated electric vehicles (EVs) charging station coupled with true solar PV and stationary battery energy storage. The optimal energy management system is based on the rolling horizon approach implemented in with a mixed integer linear program which takes as input the EV load forecast using long short-term memory (LSTM) neural network and persistence approaches and PV production forecast using a physical hybrid artificial neural network. The energy management system is firstly deployed and validated on an existing multi-good microgrid by achieving a discrepancy of state variables below 10% with respect to offline simulations. Then, eight weeks of simulations from each of the four seasons show that the accuracy of the forecast can increase operational costs by 10% equally distributed between the PV and EV forecasts. Finally, the accuracy of the combined PV and EV forecast matters more than single accuracies: LSTM outperforms persistence to predict the EV load (−30% root mean squared error), though when combined with PV forecast it has higher error (+15%) with corresponding higher operational costs (up to 5%).