Design optimization of standalone photovoltaic systems for enhanced energy density

This study proposes a comprehensive optimization framework for determining the optimal configuration of a photovoltaic (PV) system to minimize project lifetime costs while increasing the energy density and reducing the risks associated with the energy supply. Our research employed a mixed-integer nonlinear programming (MINLP) approach to evaluate a standalone hydrogen-based storage system and grid-connected PV systems, accounting for seasonal variability and energy reliability metrics such as loss of power supply probability (LPSP). The model was optimized in two phases. The first was a two-stage optimization procedure that employed the relax-and-fix (R&F) heuristic to find an initial feasible solution and the branch-and-bound (B&B) algorithm to achieve optimality. The second phase incorporated risk-based decisions by quantifying energy density variability using the expected conditional value at risk (CVaR) to optimize energy density. The model optimized the configuration decisions regarding the number of PV panels, tilt angle, spacing, and hydrogen storage system capacity. A sensitivity analysis was implemented to investigate various operational scenarios, such as integrated PV-hydrogen storage systems, grid-connected systems, and seasonal-specific design configurations for winter, spring, summer, and fall. The results indicated that seasonal designs deviated from the optimum performance owing to production-demand mismatches, whereas a year-round grid-connected system emerged as the most cost-effective and reliable, offering a low levelized cost of energy (LCOE) and efficient energy export. In conclusion, our study emphasizes the importance of a comprehensive system design, where annual optimization mitigates seasonal fluctuations and enhances energy stability by optimizing the PV configuration, capacity planning, and risk management strategies to ensure consistent performance and economic viability throughout the year.