Hybrid adaptive robust-stochastic optimization model for the design of a photovoltaic–battery energy storage system
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...
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| Format: | master thesis |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/443185 |
| Online Access: | https://hdl.handle.net/2117/443185 |
| Access Level: | Open access |
| Keyword: | Renewable energy sources Energy storage Photovoltaic power generation Stochastic robust optimization, Uncertainty, Battery degradation, System design, Photovoltaic panel, Battery energy storage Energies renovables Energia--Emmagatzematge Energia solar fotovoltaica Àrees temàtiques de la UPC::Energies::Energia solar fotovoltaica |
| Summary: | 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. 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. Furthermore, the analysis shows that for BoU<4, the cost of additional conservatism sharply increases, while BoU≥4 provides a stable robustness–cost trade-off. |
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