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

arXiv: https://arxiv.org/abs/2509.19054 · HTML: https://mlsanity.com/paper/2509.19054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons](https://mlsanity.com/paper/2604.15360/) (2026)

## Abstract (arXiv)

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.