Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina

In the continuous fight against Bellman's Curse of Dimensionality, this work presents the first steps towards learning the Optimal Operation Policy of the electricity generation system of Uruguay, Brazil, Paraguay and Argentina with the infrastructures projected for the year 2030. The Operation...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America) S. 1 - 6
Hauptverfasser: Chaer, Ruben, Ramirez, Ignacio, Camacho, Vanina, Caporale, Ximena, Casaravilla, Gonzalo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 20.10.2022
Schlagworte:
Approximate Stochastic Dynamic Programming
Machine Learning
Optimal operation of hydrothermal systems
Reinforcement Learning
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the continuous fight against Bellman's Curse of Dimensionality, this work presents the first steps towards learning the Optimal Operation Policy of the electricity generation system of Uruguay, Brazil, Paraguay and Argentina with the infrastructures projected for the year 2030. The Operation Policy under consideration involves 76 state variables: one associated to the surface temperature anomaly of the Pacific Ocean in the N34 area, and 75 related to the hydroelectric reservoirs. The proposed methodology includes the design and training of two alternate neural network architectures combined with modern techniques devised for variance reduction and exploration, which were key to the success achieved.
DOI:10.1109/IEEEPESGTDLatinAmeri53482.2022.10037786