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Application of Data Mining Techniques to Identify Structural. Congestion Problems under Uncertainty

E.F. Sánchez-Úbeda, J. Peco, P. Raymont, T. Gómez, S. Bañales, A.L. Hernández

This paper proposes a novel methodology to identify congestion problems under both «traditional» and «new» uncertainties such as generation costs, location and size of new generators, retirement of old ones, generation patterns, etc. The methodology allows not only identifying the transmission paths and corridors which will have congestion problems, but also the scenarios producing these critical situations. Thus, it can be used not only to simplify the study of new investments (reinforcement of existing lines), but also to facilitate the evaluation of hedging strategies and the design of proactive policies to avoid the detected congestion.


Palabras clave: Transmission planning, congestion management, uncertainty, data mining, artificial intelligence techniques, automatic learning, decision trees.

IEEE Power Tech. Conference, Vol. I, POM4-323, ISBN: 0-7803-7139-9, Oporto, Norte (Portugal). 10-13 Septiembre 2001

Fecha de publicación: septiembre 2001.



Cita:
E.F. Sánchez-Úbeda, J. Peco, P. Raymont, T. Gómez, S. Bañales, A.L. Hernández, Application of Data Mining Techniques to Identify Structural. Congestion Problems under Uncertainty, IEEE Power Tech. Conference, Vol. I, POM4-323, ISBN: 0-7803-7139-9. Oporto, Portugal, 10-13 Septiembre 2001


    Líneas de investigación:
  • *Predicción y Análisis de Datos

IIT-01-028A

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