Optimization strategies for decision-makers to increase the distributed energy resources penetration for a greener future.

• Ano de defesa: 2021
• Autor(a) principal: Taheri, Seyed Iman
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Algoritmos de otimização; Distributed energy resources; Distributed Generation; Distribution network; Energia (Gerenciamento); Energy management; Multiobjective; Optimization algorithm; Recursos energéticos; Redes de distribuição de energia elétrica; Single objective
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3143/tde-17092021-104358/

Integrating distributed energy resources (DERs), specifically renewable distributed generation (DG), in the distribution system leads to energy management problems. These problems for electric utilities are varied based on different former improvements in distribution systems and their requirements. Therefore, this thesis presents different energy management scenarios and solutions for covering a wide range of optimization problems in the distribution systems related to integrating DERs.All these scenarios are in one direction under the \'placement of DER in the distribution system.\' This thesis analyses the strategies, including four energy management optimization problems. The first and second scenarios are the single objective and multiobjective placement of DER in the distribution system. The thesis improves the first two scenarios by a multiobjective placement and sizing by considering the distribution system hosting capacity and the former installed DER (i.e., third scenario). Finally, the fourth scenario presents the optimal dispatching of DERs and microgrids for virtual power plant (VPP) Scheduling. These scenarios include the allocation of DG, such as fuel cells, wind turbines, and solar cells, in a distribution system to guide future energy management. In these scenarios, the applied optimization algorithms are the improved teaching-learningbased optimization (TLBO) algorithm with presenting two new modifications and two new hybrid optimization algorithms. The first and second scenarios use further modifications of TLBO for DER placement in the single objective and multiobjective problems. The third scenario applies a hybrid optimization algorithm combining TLBO and honey bee mating optimization (HBMO) algorithm. The hybrid algorithm of the fourth scenario is a combination of grid search algorithm and derivative-free optimization algorithm. TLBO, HBMO, grid search, and derivative-free optimization algorithms have been reported as the best optimization algorithms in this area. Thus, this thesis, by modifying and combining those algorithms, tries to design a better optimization algorithm matched to each scenario. Moreover, an IEEE standard distribution system, a 70-bus radial distribution system, is used to implement the proposed optimization algorithms based on the power system manager scenario and objective functions. The results show the importance of the proposed algorithms to optimize objective functions. The proposed algorithms observed superiority presents the best accuracy and velocity in achieving the optimal solution among the other optimization algorithms tested. The proposed algorithms are new optimization algorithms obtained by modifying the original version of the TLBO or combine two optimization algorithms. TLBO modification is in teacher and learner phases with adding the proper optimization techniques such as quasi-opposition-based learning technique and mutation method. With the best optimal solutions, the electric utility can have an economic, technical, and environmental improvement in the power system by DERs integrations. Besides, prosumers can have full benefits from renewable generators.