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Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios

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Date
2024-10-15
Author
Martínez Velázquez, Miguel
Mateo Domingo, Carlos
Gómez San Román, Tomás
Alonso Santos, Beatriz
Frías Marín, Pablo
Estado
info:eu-repo/semantics/publishedVersion
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Abstract
 
 
Energy storage systems can be leveraged in electricity distribution network planning as mitigation alternatives to traditional grid reinforcements if they are strategically installed and operated to reduce congestion and voltage limit violations. This paper examines the technical and economic viability of distributed battery energy storage systems owned by the system operator as an alternative to distribution network reinforcements. The case study analyzes the installation of battery energy storage systems in a real 500-bus Spanish medium voltage grid under sustained load growth scenarios. The results show that, in general, dedicated battery energy storage systems are only a cost-efficient alternative in distribution system planning under very specific conditions, such as when low load growth rates are expected. Nevertheless, they are only required for peak shaving a few days per year. For the analyzed case study, the recoverable portion of their total cost through deferral of distribution system upgrades is higher than the fraction of cycles required for peak shaving under all sustained load growth scenarios. Therefore, it is also explored if mobile battery energy storage systems, capital grants, and revenue stacking can enable battery energy storage systems to become an efficient distribution system planning alternative.
 
URI
https:doi.org10.1016j.est.2024.113404
http://hdl.handle.net/11531/100638
Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios
Tipo de Actividad
Artículos en revistas
ISSN
2352-152X
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Distribution network planning; Energy storage systems; Reinforcements; Genetic algorithms
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