Investing in low-carbon technologies, including light-duty vehicles, is a strategy to decarbonize the residential sector and private mobility. This work aims to assess the parameters driving the economic convenience of battery electric vehicles (BEVs) for a household, and what are the economic implications of BEV adoption on the total cost of the residential energy system in case of house renovation. An archetypal household energy system has been modelled for the Italian context, where strong residential energy efficiency incentives have been put in place in recent years. Adopting a least-cost-oriented energy system optimization model, 33’600 residential energy system configurations have been analysed through an extensive sensitivity analysis carried out focusing on crucial input parameters, classified as behavioural (annual travelled distance, expected ownership time of the car), energy-related (electricity and heating demand, house location, PV installed capacity), and economic (grid electricity price, gasoline prices and incentives on BEV purchase). Results show that integration with PV installation is the parameter most strongly correlated with BEV adoption, followed by annual travelled distance and ownership time. Moreover, results suggest that an increase in electricity prices has a lower impact on disincentivizing BEVs adoption compared to how much an equivalent increase in gasoline prices disincentivizes ICEVs adoption. Valuable insights reveal that, within the range of the Italian average gasoline price, BEV-based energy systems remain competitive. This holds even with a high electricity price, provided a minimum of 3 kW photovoltaic capacity is installed. In light of the ongoing energy crisis in Europe, these findings are promising for BEV adoption, particularly if accompanied by BEV incentive policies.

Hybrid microgrids represent a cost-effective and viable option to ensure access to energy in rural areas located far from the main grid. Nonetheless, the sizing of rural microgrids is complicated by the lack of models capable of accounting for the evolution of the energy demand over time, which is likely to occur in such contexts as a result of the modification of users' lifestyles. To tackle this issue, the present study aims at developing a novel, long-term optimisation model formulation, capable of accounting for load evolution and performing suitable investment decisions for capacity expansion along the time horizon. Multiple scenarios of load evolution are considered to evaluate the beneficial effects of this novel approach, through the coupling of the model with a tool for stochastic load profiles generation. The results show how this implementation brings lower Net Present Cost to the project and improved correspondence between actual electricity demand and microgrid sizing. Finally, a sensitivity analysis evaluates the robustness of the approach with respect to input data variability and the Loss of Load parameter.