A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids
This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues...
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MDPI AG
2018-03-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/3/455 |
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| author | Guido Carpinelli Fabio Mottola Daniela Proto Angela Russo Pietro Varilone |
| author_facet | Guido Carpinelli Fabio Mottola Daniela Proto Angela Russo Pietro Varilone |
| author_sort | Guido Carpinelli |
| collection | DOAJ |
| description | This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries’ lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution. |
| first_indexed | 2024-12-10T07:07:34Z |
| format | Article |
| id | doaj.art-8d65a95b6e754b49b0f84aced49bc0c1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T07:07:34Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8d65a95b6e754b49b0f84aced49bc0c12022-12-22T01:58:08ZengMDPI AGApplied Sciences2076-34172018-03-018345510.3390/app8030455app8030455A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage MicrogridsGuido Carpinelli0Fabio Mottola1Daniela Proto2Angela Russo3Pietro Varilone4University of Naples Federico II, via Claudio 21, 80125 Naples, ItalyUniversity of Naples Parthenope, Centro Direzionale di Napoli, Isola C/4, 80143 Naples, ItalyUniversity of Naples Federico II, via Claudio 21, 80125 Naples, ItalyPolitecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, ItalyUniversity of Cassino and Southern Lazio, Via G. Di Biasio 43, 03043 Cassino, ItalyThis paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries’ lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution.http://www.mdpi.com/2076-3417/8/3/455distributed energy storage systemsoptimization methodgenetic algorithmdistribution networks |
| spellingShingle | Guido Carpinelli Fabio Mottola Daniela Proto Angela Russo Pietro Varilone A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids Applied Sciences distributed energy storage systems optimization method genetic algorithm distribution networks |
| title | A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids |
| title_full | A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids |
| title_fullStr | A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids |
| title_full_unstemmed | A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids |
| title_short | A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids |
| title_sort | hybrid method for optimal siting and sizing of battery energy storage systems in unbalanced low voltage microgrids |
| topic | distributed energy storage systems optimization method genetic algorithm distribution networks |
| url | http://www.mdpi.com/2076-3417/8/3/455 |
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