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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| Veröffentlicht in: | 2023 IEEE PES GTD International Conference and Exposition (GTD) S. 139 - 143 |
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| Sprache: | Englisch |
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01.05.2023
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Casaravilla, Gonzalo Ramirez, Ignacio Chaer, Ruben |
| Author_xml | – sequence: 1 givenname: Ruben surname: Chaer fullname: Chaer, Ruben email: rchaer@simsee.org organization: Universidad de la República and Electricity Market Administration,Facultad de Ingeniería,Montevideo,Uruguay – sequence: 2 givenname: Ignacio surname: Ramirez fullname: Ramirez, Ignacio email: nacho@fing.edu.uy organization: Universidad de la República,Facultad de Ingeniería,Montevideo,Uruguay – sequence: 3 givenname: Gonzalo surname: Casaravilla fullname: Casaravilla, Gonzalo email: gcp@fing.edu.uy organization: Universidad de la República,Facultad de Ingeniería,Montevideo,Uruguay |
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| Snippet | 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... |
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| SubjectTerms | Approximate Stochastic Dynamic Programming Costs Heuristic algorithms Lakes Power system dynamics Reinforcement learning Reinforcement Machine Learning Renewable Energies Solar energy Stochastic processes |
| Title | Machine Learning Applied to the Operation of Fully Renewable Energy Systems |
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