Otimização robusta da confiabilidade de microrredes no modo de operação ilhado - CHAGAS, Elton Amorim (2018)
The introduction of information technologies and automation in distribution networks and the demand for renewable energy resources has driven to smart grids paradigm. One of the main catalyst for the smart grids implementation are the microgrids. That is, distribution networks with the ability of operating interconnected to utility grid or autonomous, i.e., without connection with the utility network and forming an islanded subsystem. This research work presents a methodology to improve the reliability of a microgrid operating in islanded mode through the pre-dispatch optimization of the distributed generators connected to the microgrid. The improvement of reliability is based on the minimization of the probability of frequency and nodal voltages violations. In order to make the study more realistic, a distributed slack bus probabilistic power flow was implemented, which takes into account the uncertainties inherent to the operation and planning of the microgrid (generators unavailability, intermittence of renewable energy resources, and load forecasting errors) and the lack of a slack bus in an islanded distribution system. In the probabilistic flow, two analytical techniques were used together: the Point Estimation, to obtain the moments of the microgrid state variables and the Gaussian Sum Expansion, to obtain the probability distributions of these variables. This method was validated through the implementation of a probabilistic flow based on the Monte Carlo Simulation. In order to optimize the pre-dispatch of the microgrid, two optimization meta-heuristics were used: the Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). The results obtained with the application of the two meta-heuristics showed that the GSA presents solutions with better quality and lower computational cost than PSO. Therefore, the GSA is more effective for the solution of the problem of microgrid pre-dispatch optmization.