Scholarship 24/20600-7 - Otimização, Planejamento - BV FAPESP
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Expansion Planning for Modern and Resilient Electricity Distribution Systems Considering Local Energy Markets

Grant number: 24/20600-7
Support Opportunities:Scholarships in Brazil - Doctorate
Start date: April 01, 2025
End date: March 31, 2029
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal Investigator:John Fredy Franco Baquero
Grantee:Matheus Holzbach
Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil

Abstract

The growing integration of distributed energy resources (DERs) into electricity distribution systems (EDSs), driven by the global energy transition, and the increased frequency of extreme weather events, challenges the traditional planning and operating models. In turn, local energy markets (LEMs), by encouraging the participation of distributed generation and promoting local energy autonomy, offer opportunities to increase the resilience of electricity systems and reduce energy costs. However, the implementation of LEMs presents challenges in terms of the need for more sophisticated energy management mechanisms and the adequacy of grid infrastructure resources. In this context, it is proposed to develop optimization methods for planning the expansion of EDSs to minimize costs, guaranteeing resilience in facing extreme events and the efficient integration of LEMs. Through stochastic and robust programming techniques, uncertainty associated with generation and demand is modeled through different operating scenarios, in order to provide economic solutions that offer the required level of robustness. The benefits expected from this integration include reduced energy costs for consumers, increased penetration of DERs and improved quality of service. The results are expected to contribute to improving the efficiency and sustainability of electricity systems, as well as promoting the transition to a more decentralized and resilient energy model.

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