Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration
Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requir...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/15/4466 |
| _version_ | 1797525671751188480 |
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| author | Maël Riou Florian Dupriez-Robin Dominique Grondin Christophe Le Loup Michel Benne Quoc T. Tran |
| author_facet | Maël Riou Florian Dupriez-Robin Dominique Grondin Christophe Le Loup Michel Benne Quoc T. Tran |
| author_sort | Maël Riou |
| collection | DOAJ |
| description | Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage. |
| first_indexed | 2024-03-10T09:16:35Z |
| format | Article |
| id | doaj.art-b96df77d0ce24dd0834fcab0b265e5ca |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T09:16:35Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b96df77d0ce24dd0834fcab0b265e5ca2023-11-22T05:33:20ZengMDPI AGEnergies1996-10732021-07-011415446610.3390/en14154466Multi-Objective Optimization of Autonomous Microgrids with Reliability ConsiderationMaël Riou0Florian Dupriez-Robin1Dominique Grondin2Christophe Le Loup3Michel Benne4Quoc T. Tran5Entech Smart Energies, 29000 Quimper, FranceFrance Energies Marines, 29280 Plouzané, FranceENERGY Lab—LE2P (FRH2 CNRS), 97744 Saint-Denis, FranceEntech Smart Energies, 29000 Quimper, FranceENERGY Lab—LE2P (FRH2 CNRS), 97744 Saint-Denis, FranceCEA Tech, 44340 Nantes, FranceMicrogrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.https://www.mdpi.com/1996-1073/14/15/4466microgridoff-gridreliabilitysizinggenetic algorithm |
| spellingShingle | Maël Riou Florian Dupriez-Robin Dominique Grondin Christophe Le Loup Michel Benne Quoc T. Tran Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration Energies microgrid off-grid reliability sizing genetic algorithm |
| title | Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration |
| title_full | Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration |
| title_fullStr | Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration |
| title_full_unstemmed | Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration |
| title_short | Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration |
| title_sort | multi objective optimization of autonomous microgrids with reliability consideration |
| topic | microgrid off-grid reliability sizing genetic algorithm |
| url | https://www.mdpi.com/1996-1073/14/15/4466 |
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