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This work proposes a methodology and a practical tool for the study of long-term network planning under uncertainties. In this approach the major external uncertainties during the planning horizon are modeled as macroscenarios at different future time instants. On the other hand, the random nature of actual operating conditions is taken into account by using a probabilistic model of microscenarios based on past statistics. Massive Monte-Carlo simulations are used to generate and simulate a large number of scenarios and store the detailed results in a relational database. Data mining techniques are then applied to extract information from the database so as to rank scenarios and network reinforcements according to different criteria.