Application of a data minig based technique for the evaluation of transmission expansion plans

This paper describes a methodology for the study of long-term network planning under uncertainties. In this approach the major external uncertainties during the planning horizon are modelled as macro-scenarios at different future time instants. The random nature of actual operating conditions is taken into account by using a probabilistic model of micro-scenarios based on past statistics. MonteCarlo simulations are used to generate and simulate a specified number of scenarios. Data mining techniques are then applied to the simulations results collected in a database, so as to extract information and to rank scenarios and network reinforcements according to different performance criteria. The paper describes the application of this approach on a real transmission planning problem faced by the Belgian transmission system operator.