Hybrid Adaptive Robust Stochastic Optimization Model for the Design of a Photovoltaic Battery Energy Storage System
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915509300101120 |
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| author | Pous, Alba Lun Mora Garcia-Muñoz, Fernando Jorquera-Bravo, Natalia Aranguiz, Ricardo Olivos, Valentina Bugueño |
| author_facet | Pous, Alba Lun Mora Garcia-Muñoz, Fernando Jorquera-Bravo, Natalia Aranguiz, Ricardo Olivos, Valentina Bugueño |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Adaptive Robust Stochastic Optimization Model for the Design of a Photovoltaic Battery Energy Storage System Pous, Alba Lun Mora Garcia-Muñoz, Fernando Jorquera-Bravo, Natalia Aranguiz, Ricardo Olivos, Valentina Bugueño Optimization and Control 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. |
| title | Hybrid Adaptive Robust Stochastic Optimization Model for the Design of a Photovoltaic Battery Energy Storage System |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.19054 |