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Due to the large size of electric power systems, there is a very high computational burden when obtaining the optimum network by using classical optimization techniques. Several authors have used heuristics and/or sensitivities in order to guide the search of optimal network investments. This paper proposes an automatic learning approach in order to decide whether a network change will improve the overall costs or not. More specifically, decision trees methods are used to identify a set of simple and reliable rules which combine criteria based on both heuristics and sensitivities. These decision trees are integrated in a subtransmission planning tool, improving dramatically both the "optimality" of the resultant network and the computational time. |
