Planeamiento de la expansión de los sistemas eléctricos de transmisión utilizando el modelo AC resuelto mediante algoritmos evolutivos

Abstract

The Transmission Expansion Planning (TEP) problem consists of determining all the changes needed in the transmission system infrastructure (i.e. additions and reinforcements) in order to allow the balance between the projected demand and the power supply, at minimum investment along the planning horizon. The TEP is a large scale, mixed-integer, non-linear and non-convex problem. Due to the complexity of the problem requires greater research in optimization techniques that seek a solution of the problem in an efficient manner. Therefore, more optimization techniques must be explored to solve the TEP problem in an efficient way. In this research work, the focus was on the study of the optimization heuristic and metaheuristics based on Evolutionary Algorithms (EA) applied to the problem of static TEP, using the AC model. The algorithms used were: Evolutionary Programming (EP), Evolutionary Strategy (EE) and Modified Evolutionary Strategy (MEE). The performance of the algorithms were evaluated in a gradual manner for different scenarios of the Garver test system. Then the better performance algorithm was applied to solve the TEP problem using the IEEE 24-bus system. Its final performance was compared with other heuristic algorithms available in the reference literature.