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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2023 IEEE PES GTD International Conference and Exposition (GTD) s. 139 - 143
Hlavní autoři:	Chaer, Ruben, Ramirez, Ignacio, Casaravilla, Gonzalo
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.05.2023
Témata:	Approximate Stochastic Dynamic Programming Costs Heuristic algorithms Lakes Power system dynamics Reinforcement learning Reinforcement Machine Learning Renewable Energies Solar energy Stochastic processes
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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 spatial differences, together with parameter regularization and dynamic exploring techniques. The proposed strategy was applied to a case of what could be the power system of Uruguay by 2050 based strongly in hydro, wind and solar energies, including three lakes, four groups of battery banks, and the basin runoff of the two main Uruguayan rivers. The generation in the year 2022 in Uruguay was 43% hydraulic, 40% wind plus solar, 7% biomass and 10% based on fossil fuels. This composition prints a very relevant stochastic component that makes it difficult to apply machine learning techniques without the kind of algorihms proposed in this work.
DOI:	10.1109/GTD49768.2023.00053