Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets
This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to...
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/5/1130 |
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| author | Ali Keyvandarian Ahmed Saif Ronald Pelot |
| author_facet | Ali Keyvandarian Ahmed Saif Ronald Pelot |
| author_sort | Ali Keyvandarian |
| collection | DOAJ |
| description | This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal auto-correlations and cross-correlations among uncertain parameters like energy demand and solar and wind energy supply. These DUSs are compared to static and independent dynamic uncertainty sets based on time series (TS) from the literature. An exact iterative algorithm is developed to solve the problem effectively. A case study of a northern Ontario community evaluates the proposed framework and the solution method using real test data. Simulation reveals a 10.7% increase in capital cost on average but a 36.2% decrease in operational cost, resulting in a 16.4% total cost reduction and an 8.1% improvement in system reliability compared to the nominal model employing point estimates. Furthermore, the proposed VAR- and NN-based DUSs significantly outperform classical static and TS-based dynamic sets, underscoring the necessity of considering cross-correlations in uncertainty quantification. |
| format | Article |
| id | doaj-art-c4f044d1501f4b0183e9984b342232cf |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c4f044d1501f4b0183e9984b342232cf2025-08-20T02:59:14ZengMDPI AGEnergies1996-10732025-02-01185113010.3390/en18051130Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty SetsAli Keyvandarian0Ahmed Saif1Ronald Pelot2Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS B3H 4R2, CanadaDepartment of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS B3H 4R2, CanadaDepartment of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS B3H 4R2, CanadaThis study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal auto-correlations and cross-correlations among uncertain parameters like energy demand and solar and wind energy supply. These DUSs are compared to static and independent dynamic uncertainty sets based on time series (TS) from the literature. An exact iterative algorithm is developed to solve the problem effectively. A case study of a northern Ontario community evaluates the proposed framework and the solution method using real test data. Simulation reveals a 10.7% increase in capital cost on average but a 36.2% decrease in operational cost, resulting in a 16.4% total cost reduction and an 8.1% improvement in system reliability compared to the nominal model employing point estimates. Furthermore, the proposed VAR- and NN-based DUSs significantly outperform classical static and TS-based dynamic sets, underscoring the necessity of considering cross-correlations in uncertainty quantification.https://www.mdpi.com/1996-1073/18/5/1130hybrid renewable energy systemsadaptive robust optimizationdynamic uncertainty setsvector auto-regressive modelsneural networks |
| spellingShingle | Ali Keyvandarian Ahmed Saif Ronald Pelot Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets Energies hybrid renewable energy systems adaptive robust optimization dynamic uncertainty sets vector auto-regressive models neural networks |
| title | Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets |
| title_full | Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets |
| title_fullStr | Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets |
| title_full_unstemmed | Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets |
| title_short | Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets |
| title_sort | robust optimal sizing of a stand alone hybrid renewable energy system using machine learning based uncertainty sets |
| topic | hybrid renewable energy systems adaptive robust optimization dynamic uncertainty sets vector auto-regressive models neural networks |
| url | https://www.mdpi.com/1996-1073/18/5/1130 |
| work_keys_str_mv | AT alikeyvandarian robustoptimalsizingofastandalonehybridrenewableenergysystemusingmachinelearningbaseduncertaintysets AT ahmedsaif robustoptimalsizingofastandalonehybridrenewableenergysystemusingmachinelearningbaseduncertaintysets AT ronaldpelot robustoptimalsizingofastandalonehybridrenewableenergysystemusingmachinelearningbaseduncertaintysets |