Quantum-Enhanced Solar Cell Efficiency: A Theoretical and Empirical Framework for Photovoltaic Storage Degradation Optimization

Abstract

The integration of photovoltaic (PV) and energy storage systems (ESSs) in modern smart grids has significantly increased the issue of component degradation, which in turn has negatively impacted the system's efficiency, operational expenses, and long-term reliability. However, the stochastic and nonlinear nature of PV module and battery degradation are not well captured by conventional classical optimization methods, which leads to suboptimal dispatch strategies and premature system aging. In this work, an empirical study of a quantum enhanced degradation pathway optimization framework that combines hybrid quantum-classical computing methods to maximize energy dispatch efficiency and extend system lifetime. The three-layer hierarchical optimization framework is realized by combining quantum-assisted Monte Carlo simulations on a D-Wave Advantage quantum annealer with a reinforcement learning-based classical engine and utilizes the quantum annealer to dynamically adjust its operational strategies in real time. The framework was validated with a simulated 5 MW PV array and a 2.5 MWh lithium-ion battery storage system, with a 5-year operational time horizon. Results show a 25% decrease in battery degradation-induced wear, a PV module lifespan increase of around 2.5 years, and more than 47% increase in energy dispatch efficiency compared to classical mixed-integer linear programming baselines. The quantum-assisted model demonstrates statistically significant performance gain in several degradation scenarios, temperature profiles, and load demand conditions, through sensitivity analyses.