Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources.
Demand side management
Optimization
Reconfiguration
Renewables
Second Order Cone Programming
Smart distribution network
Two stage optimization
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 Oct 2024
04 Oct 2024
Historique:
received:
01
02
2024
accepted:
23
09
2024
medline:
4
10
2024
pubmed:
4
10
2024
entrez:
3
10
2024
Statut:
epublish
Résumé
The integration of renewable energy sources into smart distribution grids poses substantial challenges in maintaining grid stability, efficiency, and reliability due to their inherent variability and intermittency. This study addresses these challenges by proposing a novel two-level optimization model aimed at enhancing operational efficiency and robustness in smart distribution grids. The model synergistically integrates renewable energy sources, energy storage systems, electric vehicles, and demand-side management through a dynamic reconfiguration approach. It employs a robust optimization framework combined with a two-stage second-order cone optimization model to manage real-time operations and strategic grid reconfiguration. Key findings from simulations on the IEEE 33 and 69-bus networks underscore the model's effectiveness. In the 33-bus system, implementing the demand response program led to a significant reduction in power losses, from 0.64 MW to 0.52 MW, and improved voltage stability, with the minimum voltage increasing from 0.970 to 0.980 p.u. Similarly, in the 69-bus system, power losses decreased from 0.85 MW to 0.79 MW, and voltage stability improved, with the minimum voltage rising from 0.962 to 0.972 p.u. The model also demonstrated reduced energy procurement needs, showcasing its impact on enhancing grid efficiency and reliability. These results highlight the model's potential for advancing smart grid management strategies, offering significant improvements in operational performance and stability under varying demand conditions.
Identifiants
pubmed: 39362938
doi: 10.1038/s41598-024-73928-1
pii: 10.1038/s41598-024-73928-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23041Informations de copyright
© 2024. The Author(s).
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