Optimal PV-BESS-EV system design for a net-zero energy home considering uncertainties and V2H / H2V operation

Abstract

To reduce greenhouse gas emissions in the residential sector, the concept of a net-zero energy home (NZEH) has been adopted to balance energy generation and consumption through adaptive energy control. However, current NZEH research remains limited by fragmented optimization of photovoltaic (PV)–battery energy storage system (BESS)–electric vehicle (EV) integration, unidirectional energy flow in EVs, and insufficient consideration of real-world uncertainties. This study presents an integrated framework for the optimal design and control strategies of an NZEH incorporating PV, BESS, and EV systems, considering both vehicle-to-home (V2H) and home-to-vehicle (H2V) capabilities. Uncertainties in PV power generation, household load demand, and EV usage are addressed using real-world data combined with probabilistic modeling techniques. This approach includes Monte Carlo simulation, normal distribution, and solar radiation data derived from the PVGIS database. The optimal sizing of PV and BESS is formulated as an optimization problem to achieve net-zero energy while maintaining economic feasibility using a particle swarm optimization technique. The results demonstrate that optimal PV and BESS capacities of 3.6 kW and 11.90 kWh, respectively, can achieve net-zero annual energy under flat-rate pricing, considering uncertainties. Accounting for uncertainties increases the optimal PV capacity by 9.5 %, while the optimal BESS capacity decreases by 13.90%–31.16%, depending on the electricity pricing structure. Furthermore, V2H operation enhances energy cost savings by up to 137 %, particularly in scenarios without BESS. The findings indicate that combining optimized system sizing with uncertainty modeling and EV bidirectional operation significantly enhances the technical performance, economic benefits, and flexibility of NZEH.