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Multi-level optimization applied to distribution systems planning including non-utility-owned distributed energy resources

Grant number: 22/12947-1
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): July 01, 2023
Effective date (End): June 30, 2025
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal Investigator:John Fredy Franco Baquero
Grantee:Mario Andres Mejia Alzate
Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil

Abstract

Distribution systems are being restructured, modified, and adapted to operate in the smart grid environment, allowing the integration of distributed energy resources such as distributed renewable generation, energy storage systems, electric vehicle charging stations, and so on. These technologies provide several operational and environmental benefits to distribution networks, but they also present several challenges that must be addressed carefully in order to avoid system operational issues, which can vary depending on the location, size, and type of distributed energy resource installed. Furthermore, the installation of these resources in the network allows the emergence of new agents in the energy market, which may have different objectives and/or conflict with the concessionaire's objectives, making proper planning of the size and location of these resources difficult. As a result, utilities require specialized tools to assist them in making decisions in the face of these new challenges. The current research project proposes to use bi-level optimization to address the problem of distribution system planning that includes distributed energy resources that are not owned by the utility. The bi-level optimization allows to hierarchically model problems involving multiple agents and finds a balanced solution that optimizes each agent's objectives. Thus, the objectives of the utility will be considered at the upper level, while the objectives of the owners of distributed energy resources will be considered at the lower level. To represent the planning problem, mathematical optimization models will be developed to be solved using commercial solvers, as well as variable neighborhood search metaheuristics. Furthermore, the formulation of the problem will be expanded to account for the uncertainties associated with the planning problem. The proposed methods will be used to solve systems with varying degrees of complexity from the literature, and the results will be compared to existing methods.

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