Algoritmo genético especializado aplicado ao planejamento da expansão de sistemas de distribuição de energia elétrica

• Autor(a) principal: Camargo, Vera Lúcia Vieira de [UNESP]
• Data de Publicação: 2014
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/111131
• topic: Sistemas de energia eletrica - Planejamento; Algoritmos genéticos; Programação não-linear; Genetic algorithms

In this paper, it was proposed the development of a solution technique to solve the expansion planning problem of electricity distribution system (PEDS) modeled as a problem of mixed integer nonlinear programming (MINP) mono-objective and multi-stage (dynamic), with the goal to find an expansion plan from electricity distribution system with minimum investment costs and operation subject to physical and operational constraints and restrictions that establish the limits of continuity indicators DIC, FIC, DEC and FEC . The model objective function is equal to the costs present value net with construction and/ or reconductoring of circuits with construction and/or substations expansion with annual resistive losses and with substations operation. In order to achieve the objective proposed a specialized genetic algorithm adapted by Chu-Beasley was developed in conjunction with specialized heuristics techniques to solve the problem of PEDS. To assess the feasibility and flexibility of proposed literature, systems which they were tested organized in three stages with different goals: the first it was performed the static planning considering only the system operating constraints (systems 23 and 136 bus); the second it was performed the dynamic multistage planning considering the same restrictions from the previous stage (systems 54 and 417 bus) and the third it was performed the dynamic multistage planning considering as much operating restrictions as related to reliability of system (system 27 bus). According to the results, the algorithm was efficient and versatile because of the systems that were possible to reach a comparison, the algorithm was able to find best result (for system 54 bus) equal (for systems 23 and 136 bus) and next to system of 417 bus