Machine Learning Applied to the Operation of Fully Renewable Energy Systems

This work presents a novel learning algorithm for the operation policy of power systems trying to minimize the cost of fulfilling the energy demand. The algorithm improves upon the classical reinforcement learning methods by controlling the sampling variance in the estimation of the future cost spat...

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Bibliographic Details

Published in:	2023 IEEE PES GTD International Conference and Exposition (GTD) pp. 139 - 143
Main Authors:	Chaer, Ruben, Ramirez, Ignacio, Casaravilla, Gonzalo
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.05.2023
Subjects:	Approximate Stochastic Dynamic Programming Costs Heuristic algorithms Lakes Power system dynamics Reinforcement learning Reinforcement Machine Learning Renewable Energies Solar energy Stochastic processes
Online Access:	Get full text
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