Integrated sizing and management of residential energy systems for electric and hydrogen vehicle charging

Abstract

The rising adoption of electric (EV) and hydrogen (HV) vehicles has intensified the need for effective energy management strategies to minimize grid dependence and carbon emissions. Yet, there is a need to systematically examine multiple EV/HV charging scenarios under real household loads, especially in systems integrating photovoltaic (PV) panels, battery storage, and the grid. This study addresses this gap by employing a dynamic framework that captures hourly variations in vehicle arrival times and states of charge (SoC). To address this gap, a simulation model was developed in TRNSYS and integrated with a Python-based controller to evaluate the energy performance, economic feasibility, and CO2 emissions of a residential building in Gaithersburg, Maryland, USA. The study considered 70 household parameters, including lighting, heating, cooling, and vehicle charging. Five distinct scenarios were analyzed, ranging from direct reliance on PV and the grid without storage to configurations incorporating multiple battery packs for greater self-sufficiency. One scenario achieved nearly 90 % self-sufficiency, while a balanced configuration, optimized using a genetic algorithm that considered both emissions and cost, reduced CO2 emissions by approximately 24 % compared to a grid-dependent baseline. These findings underscore the importance of proper storage sizing in enhancing energy efficiency. A well-planned integration of solar generation, battery storage, and grid support can significantly cut annual CO2 emissions, accommodate seasonal variations, and lessen reliance on conventional power sources. The results provide practical insights for designing future residential systems that effectively integrate renewable energy and vehicle charging in a sustainable and cost-efficient manner.