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http://hdl.handle.net/2183/33634 Gestión de energía en comunidades energéticas mediante Blockchain y MPC estocástico y distribuido
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Sivianes, Manuel
Velarde, Pablo
Zafra, Ascensión
Bordons, Carlos
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Bibliographic citation
Sivianes, M., Velarde, P., Zafra-Cabeza, A, Bordons, C. 2023. Gestión de energía en comunidades energéticas mediante Blockchain y MPC estocástico y distribuido. XLIV Jornadas de Automática 370-375. https://doi.org/10.17979/spudc.9788497498609.370
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Abstract
[Resumen] Este trabajo presenta una plataforma de gestión de energía distribuida que utiliza un contrato inteligente implementado en una red Blockchain para optimizar el rendimiento de una comunidad energética ante perturbaciones estocásticas, como la variabilidad en la radiación solar y las fluctuaciones en la demanda de energía por parte de los agentes. Estas perturbaciones se modelan mediante distribuciones de probabilidad y se abordan mediante un esquema de control predictivo distribuido basado en restricciones estocásticas. El rendimiento del algoritmo propuesto se evalúa a través de diversas simulaciones.
[Abstract] This paper presents a distributed energy management platform that utilizes a smart contract implemented on a Blockchain network to optimize the performance of an energy community in the face of stochastic disturbances, such as variability in solar radiation and fluctuations in energy demand from the agents. These disturbances are modeled using probability distributions and are addressed through a distributed predictive control scheme based on chance-constraints. The performance of the proposed algorithm is evaluated through various simulations.
[Abstract] This paper presents a distributed energy management platform that utilizes a smart contract implemented on a Blockchain network to optimize the performance of an energy community in the face of stochastic disturbances, such as variability in solar radiation and fluctuations in energy demand from the agents. These disturbances are modeled using probability distributions and are addressed through a distributed predictive control scheme based on chance-constraints. The performance of the proposed algorithm is evaluated through various simulations.
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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/


