Hybrid Adaptive Robust Stochastic Optimization Model for the Design of a Photovoltaic Battery Energy Storage System

Abstract

Future energy projections and their inherent uncertainty play a key role in the design of photovoltaic-battery energy storage systems (PV-BESS) for household use. In this study, both stochastic and robust optimization techniques are simultaneously integrated into a Hybrid Adaptive Robust-Stochastic Optimization (HARSO) model. Uncertainty in future PV generation is addressed using a stochastic approach, while uncertainty in power demand is handled through robust optimization. To solve the tri-level structure emerging from the hybrid approach, a Column-and-Constraint Generation (CCG) algorithm is implemented. The model also accounts for battery degradation by considering multiple commercially available battery chemistries, enabling a more realistic evaluation of long-term system costs and performance. To demonstrate its applicability, the model is applied to a case study involving the optimal design of a PV-BESS system for a household in Spain. The empirical analysis includes both first-life (FL) and second-life (SL) batteries with different chemistries, providing a comprehensive evaluation of design alternatives under uncertainty. Results indicate that the optimal solution is highly dependent on the level of robustness considered, leading to a shift in design strategy. Under less conservative settings, robustness is achieved by increasing battery capacity, while higher levels of conservatism favor expanding PV capacity to meet demand.