| Resumo: | This doctoral thesis proposes a stochastic planning method to build multiobjective optimization models for planning and managing virtual plants (VPP) with distributed generation, simulating a set of features based on optimization algorithms to minimize total costs. These algorithms are based on the modeling of the equipment demanded by the VPP, comparing them with the implantation techniques and strategies that already exist in the world. In addition, this thesis presents a VPP model in the IBM ILOG CPLEX Optimization Studio environment to plan a number of distributed generation units (DG). Once a VPP concept was designed by simulation, comparisons were made between the different algorithms developed for the same functionality, highlighting the advantages and disadvantages of each one and evaluating the algorithms based on economic viability strategies. The final results are substantiated by a case study of a VPP composed of micro and mini energy producers and the necessary goals for each functionality are established.
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