Scenario-Based Predictive Optimization of PV-BESS Systems for Enhanced V2G Flexibility Under Uncertainties

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

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.

A Support Fraction-Based PV-BESS Sizing Design for EV Charging Stations Using Hybrid GA-LP

With the rapid growth of electrified transportation, the design of charging infrastructure and station-level energy management has become increasingly important for meeting growing power and energy demands efficiently and cost-effectively. To address this challenge, this study presents an optimal sizing framework for photovoltaic (PV) and battery energy storage system (BESS) integrated EV charging stations, using an actual battery electric bus (BEB) charging station as the case study. This work formulates the load support fraction as a planning parameter, where different load support fractions (10 to 100)% are evaluated using an annualized-cost-based NPV metric, defined as the present value of annualized net savings to quantify the economic benefits and achieve optimal PV-BESS sizing design that is most profitable over the lifetime, considering seasonal variability. A hybrid bi-level optimization approach is proposed, where the outer Genetic Algorithm (GA) searches for the best PV-BESS size combinations and the inner Linear Programming (LP) model achieves optimal hourly dispatch for each GA candidate, enabling effective energy management. The case study results from a real-world battery electric bus (BEB) charging station operated by Utah Transit Authority (UTA) in Ogden, UT, USA, demonstrate that a 40% load support fraction is optimal and robust to seasonal variations, providing the best balance between the capital costs and long-term savings, and yielding 21.2% lower annual cost compared to a charging station design without PV-BESS and 42.5% higher NPV compared to a fully PV-BESS powered design.

Techno-economic optimization of a hybrid microgrid with integrated backup storage and vehicle-to-grid functionality

This study presents the techno-economic optimization of a hybrid backup system integrated within an off-grid microgrid framework with electric vehicle (EV) grid-interaction capability. A real-world case study from a remote region in Egypt is used to evaluate system performance under realistic operating conditions. The optimization problem is formulated to minimize the net present cost (NPC) while ensuring system reliability using a penalty-based loss of power supply probability (LPSP). The system integrates photovoltaic (PV), wind turbines (WT), battery energy storage systems (BESS), hydrogen energy storage systems (HESS), and EVs. The novelty of this work lies in the development of a coordinated multi-storage energy management strategy that integrates BESS, HESS, and constrained EV participation within a unified optimization framework. The results show that the BESS-only configuration achieves the lowest cost (NPC ≈ $20.33 billion), while the PV/WT/BESS/HESS configuration results in the highest cost (NPC ≈ $25.48 billion). The proposed PV/WT/BESS/HESS/EV configuration provides a balanced solution with an NPC of approximately $22.79 billion while maintaining near-zero LPSP. EV integration enhances system flexibility, reducing the required BESS capacity by 44.8% relative to the HESS-only configuration, while also lowering reliance on hydrogen-based long-term storage, thereby mitigating the high capital costs associated with extended-duration storage components. These findings demonstrate that coordinated multi-storage management significantly improves the techno-economic viability and operational resilience of large-scale off-grid microgrids in remote regions.

MILP-based optimization of renewable energy integration in maritime ports using demand response and energy storage

A Two Stage Optimal Power Flow Coordination for PVs, Batteries, and EVs in Unbalanced Low Voltage Distribution Networks

The increasing penetration of photovoltaic systems, battery storage and electric vehicles in low-voltage distribution networks poses significant operational challenges, including voltage regulation, thermal overload, and energy curtailment. This paper proposes an uncertainty-aware two-stage coordination framework. The first stage employs a LinDist3Flow-based optimisation with Monte Carlo scenario generation to determine day-ahead charging and discharging schedules for batteries and electric vehicles while accounting for uncertainties in load demand, solar irradiance, temperature, and electric vehicle availability. The second stage uses an exact nonlinear alternating current optimal power flow formulation to optimise inverters’ active and reactive power set-points in near real-time operation. The framework is evaluated on a modified IEEE European low-voltage test feeder comprising 165 loads with 100% single-phase photovoltaic penetration. Four operational approaches are compared: local Volt-Watt/Volt-Var control, direct application of scheduled set-points without real-time correction, real-time optimal power flow with fixed time-of-use schedules, and the proposed two-stage framework. The results demonstrate that the proposed approach achieves the lowest total curtailment while maintaining voltage and thermal compliance, reducing the total curtailed energy by approximately 5% compared to the optimal power flow in real-time with fixed schedules. These findings highlight the importance of integrating uncertainty-aware scheduling with real-time coordination in future low-voltage networks with a high penetration of consumer energy resources.

Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III.

This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.