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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17839 |
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| _version_ | 1866912289259520000 |
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| author | García-Muñoz, Fernando Duran-Mateluna, Cristian |
| author_facet | García-Muñoz, Fernando Duran-Mateluna, Cristian |
| contents | This study introduces adaptive robust optimization (ARO) and adaptive robust stochastic optimization (ARSO) approaches to address long- and short-term uncertainties in the optimal sizing and placement of distributed energy resources in distribution networks. ARO models uncertainty using a Budget of Uncertainty (BoU), while ARSO distinguishes long-term (LT) demand (via BoU) and short-term (ST) photovoltaics generation (via scenarios). Adapted Benders cutting plane algorithms are presented to tackle the tri-level optimization challenges. The experiments consider a modified version of the IEEE 33 bus system to test these two approaches and also compare them with traditional robust and stochastic optimization models. The results indicate that distinguishing between LT and ST uncertainties using a hybrid formulation such ARSO yields a solution closer to the optimal solution under perfect information than ARO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties García-Muñoz, Fernando Duran-Mateluna, Cristian Optimization and Control This study introduces adaptive robust optimization (ARO) and adaptive robust stochastic optimization (ARSO) approaches to address long- and short-term uncertainties in the optimal sizing and placement of distributed energy resources in distribution networks. ARO models uncertainty using a Budget of Uncertainty (BoU), while ARSO distinguishes long-term (LT) demand (via BoU) and short-term (ST) photovoltaics generation (via scenarios). Adapted Benders cutting plane algorithms are presented to tackle the tri-level optimization challenges. The experiments consider a modified version of the IEEE 33 bus system to test these two approaches and also compare them with traditional robust and stochastic optimization models. The results indicate that distinguishing between LT and ST uncertainties using a hybrid formulation such ARSO yields a solution closer to the optimal solution under perfect information than ARO. |
| title | Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.17839 |