Alocação espacial de geração distribuída em redes de distribuição de energia elétrica utilizando um algoritmo genético híbrido

• Ano de defesa: 2024
• Autor(a) principal: Silva, Carlos Henrique dos Santos
• Orientador(a): Negrete, Lina Paola Garces
• Banca de defesa: Negrete, Lina Paola Garces, Trujillo, Joel David Melo, Flórez, Hugo Andrés Ruiz, Corrêa, Henrique Pires
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/38995/0013000001h4v
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC)
• Departamento: Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RMG)
• País: Brasil
• Palavras-chave em Português: Geração distribuída; Otimização; Algoritmos evolutivos; Sistemas elétricos de distribuição; OpenDSS; QGIS
• Palavras-chave em Inglês: Distributed generation; Optimization; Evolutionary algorithms; Electrical distribution systems
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/13436

This work presents a methodology for optimizing the allocation of Distributed Generation (DG) resources in electrical distribution systems, utilizing evolutionary strategies and considering georeferenced spatial aspects, with an exclusive focus on photovoltaic generation. The methodology is divided into three main stages: definition of the original system, determination of network operating conditions, and application of optimization strategies based on technical criteria, with an emphasis on cost and available area limitations, which are the differentiating factors of this study. In Stage I, the target Electrical Distribution System and its main spatial, technical, and budgetary limitations are defined, and simulations are performed to evaluate the system's performance without the allocation of DG using OpenDSS software. The spatial limitations refer to areas made available by the utility for the construction of photovoltaic plants. In Stage II, reference generation values for each bus are determined, considering the generation value that best reduces the system's total losses when only one DG is allocated, as well as the available area and budget restrictions. In Stage III, two specific optimization strategies are applied and discussed - Hybrid Evolutionary Strategy and Hybrid Genetic Algorithm - to determine the DG allocations that best meet the specified objectives. To apply the proposed methodology, case studies were conducted on two different systems. The first case study used the IEEE 34-Bus System, a reference feeder representing larger and more complex real scenarios. The second case study used a real feeder, with information extracted from the Geographic Database of the Distribution Company (BDGD) provided by ANEEL with data from 2023 and geographically represented using QGIS software. In both studies, the results analysis demonstrated the effectiveness of the proposed optimization strategies, showing significant reductions in system losses and improvements in voltage profile. The results of the case studies show that the Hybrid Genetic Algorithm performed slightly better than the Hybrid Evolutionary Strategy. In the first case study, voltage gains of 7.39%, loss reduction of 14.48%, and load decrease of 9.55% were observed. In the second case study, the allocation of five DG systems resulted in a voltage gain of 1.75%, loss reduction of 14.08%, and load decrease of 8.90%, using 98.47% of the available budget and respecting area limitations. The work concludes that the application of optimization strategies, considering spatial and cost aspects, allows for a satisfactory and efficient solution to the DG allocation problem. The step-structured methodology, together with the adopted optimization strategies, ensures a systematic and rigorous approach, facilitating the replication and validation of results. This methodology contributes to the understanding and implementation of optimal DG allocation strategies, supporting energy utilities in the efficient and sustainable management of their distribution systems.