Um Estudo Comparativo do Desempenho de Algoritmos para a Reconfiguração de Rede Elétrica

Smart grids (SGs) have as one of their basic proposals to incorporate intelligence into the electric grid through computing and communication technologies aiming at greater efficiency and effectiveness in their operation and control. Power loss, quality, and failures are inherent in the generation process, transmission, and distribution of electricity and, in the context of SGs, should be minimized to ensure greater resilience and system efficiency. Dynamic and efficient distribution network reconfiguration is an example of an SG functionality. The reconfiguration process consists of adjusting or changing the topology of the distribution network from the opening and closing of switches to minimize technical losses, optimize operating parameters, and restore power supply in contingency situations. The nature of the network reconfiguration problem is combinatorial, complex, and non-linear. Aiming to minimize convergence time in search of a solution in medium and large topologies, heuristic and optimization techniques are an alternative. This dissertation proposes a new genetic algorithm, GAEnhanced (Genetic Algorithm Enhanced), to solve network reconfiguration and make a comparative study of performance aspects of this algorithm in relation to other solutions and algorithmic strategies used. The main goal is to evaluate the algorithm implementation strategies for dynamic reconfiguration and on-the-fly distribution networks from a broader perspective, in addition to proposing a new solution with the GAEnhanced algorithm. A simulator (DNRSim) with basic functionalities for implementation and tests of network reconfiguration algorithms for the Smart Grid was developed within the scope of this dissertation. The comparative study of the performance of the GAEnhanced algorithm and other solutions with the DNRSim uses the IEEE models for system tests (14-bus, 30-bus, 57-bus, 118-bus, and 330-bus). The comparative study results illustrate the different ways to efficiently compute network reconfiguration solutions (scalability, time, and quality) and demonstrate the feasibility of using the GAEnhanced algorithm in the context of Smart Grids in a perspective of deploying more autonomic and intelligent solutions.