Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators
The growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/109 |
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| author | Edward Alejandro Ortiz Josimar Tello-Maita David Celeita Agustin Marulanda Guerra |
| author_facet | Edward Alejandro Ortiz Josimar Tello-Maita David Celeita Agustin Marulanda Guerra |
| author_sort | Edward Alejandro Ortiz |
| collection | DOAJ |
| description | The growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability of their grids by identifying effective solutions. One promising approach to achieving this is through the deployment of battery energy storage systems, which can rapidly inject power to mitigate the impacts of network disturbances or outages. This study investigates the use of advanced genetic algorithms as a practical methodology for the optimal siting of batteries in modern distribution networks. By incorporating historical data on demand and network failures, the algorithm generates statistical models that inform the optimization process. The model integrates both the technical and economic aspects of battery systems to identify locations that minimize reliability indices such as SAIDI and SAIFI, while also reducing investment costs. Tested on a real distribution system comprising 1837 nodes, the proposed approach demonstrates the ability of genetic optimization to deliver efficient solutions compared with traditional methods, providing a high likelihood of identifying strategic battery locations that respond to variable demand, system failures, and technical constraints. |
| format | Article |
| id | doaj-art-7146438edd5a4117ac339f92c6cd2a33 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-7146438edd5a4117ac339f92c6cd2a332025-01-10T13:17:07ZengMDPI AGEnergies1996-10732024-12-0118110910.3390/en18010109Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System OperatorsEdward Alejandro Ortiz0Josimar Tello-Maita1David Celeita2Agustin Marulanda Guerra3Electrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, ColombiaElectrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, ColombiaFaculty of Engineering, Universidad de la Sabana, Chía 111321, ColombiaElectrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, ColombiaThe growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability of their grids by identifying effective solutions. One promising approach to achieving this is through the deployment of battery energy storage systems, which can rapidly inject power to mitigate the impacts of network disturbances or outages. This study investigates the use of advanced genetic algorithms as a practical methodology for the optimal siting of batteries in modern distribution networks. By incorporating historical data on demand and network failures, the algorithm generates statistical models that inform the optimization process. The model integrates both the technical and economic aspects of battery systems to identify locations that minimize reliability indices such as SAIDI and SAIFI, while also reducing investment costs. Tested on a real distribution system comprising 1837 nodes, the proposed approach demonstrates the ability of genetic optimization to deliver efficient solutions compared with traditional methods, providing a high likelihood of identifying strategic battery locations that respond to variable demand, system failures, and technical constraints.https://www.mdpi.com/1996-1073/18/1/109genetic optimizationbattery energy storage system (BESS)distribution power systemsfailure modelingenergy storagereliability |
| spellingShingle | Edward Alejandro Ortiz Josimar Tello-Maita David Celeita Agustin Marulanda Guerra Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators Energies genetic optimization battery energy storage system (BESS) distribution power systems failure modeling energy storage reliability |
| title | Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators |
| title_full | Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators |
| title_fullStr | Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators |
| title_full_unstemmed | Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators |
| title_short | Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators |
| title_sort | advanced genetic algorithms for optimal battery siting a practical methodology for distribution system operators |
| topic | genetic optimization battery energy storage system (BESS) distribution power systems failure modeling energy storage reliability |
| url | https://www.mdpi.com/1996-1073/18/1/109 |
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