IEEE Power & Energy Magazine - Spanish - November/December 2020 - 53

Una base fundamental de las AEG es la capacidad de
modelar con precisión los bloques de construcción de las
celdas y sus interacciones con el resto de los sistemas, de
manera que se puedan aplicar métodos de control, optimización y previsión en el funcionamiento. El NREL también
está construyendo herramientas informáticas que pueden
simular a la vez múltiples tecnologías en la red para diseñar y evaluar estos algoritmos de control y optimización
escalables y distribuidos usando computación de alto rendimiento. Además, el NREL ha dado los pasos preliminares
para demostrar estos algoritmos en tiempo real con dispositivos del mundo real, primero en el laboratorio y ahora en
los hogares inteligentes. Se necesitará trabajo adicional en
controles, optimización, análisis de datos, sistemas complejos y ciberseguridad para implementar la AEG en toda la red
de los Estados Unidos.
Sobre la base de las técnicas de optimización distribuida que se han desarrollado, es preciso reflexionar más
sobre el diseño de futuros mecanismos de mercado para
contabilizar sistemáticamente los pagos y recompensas
por el intercambio de energía y la prestación de servicios
auxiliares entre celdas y dispositivos autónomos. Será
necesario ampliar considerablemente las ideas de la energía transactiva para dar cabida a la arquitectura propuesta
con millones de dispositivos controlables en el sistema y
permitir un análisis matemático riguroso de la estabilidad
y la optimización del sistema.

Agradecimientos
Este trabajo fue escrito en parte por el Laboratorio Nacional
de Energías Renovables (NREL), operado por Alliance for
Sustainable Energy, LLC, para el Departamento de Energía
(DOE, por sus siglas en inglés) de los Estados Unidos bajo el
contrato DE-AC36-08GO28308. Este trabajo fue respaldado
en parte por el programa de investigación y desarrollo dirigido por el laboratorio del NREL. Las opiniones expresadas
en el artículo no representan necesariamente las opiniones
del DOE o del Gobierno de los EE. UU. El Gobierno de los
EE. UU. conserva, y el editor, mediante su aceptación del
artículo para su publicación, reconoce que el Gobierno de
EE. UU. conserva una licencia mundial no exclusiva, pagada,
irrevocable, para publicar o reproducir la forma publicada
de este artículo, o permite que otros lo hagan, para fines del
gobierno de los Estados Unidos.

Lecturas complementarias
B. Kroposki, E. Dall'Anese, A. Bernstein, Y. Zhang y B. Hodge, " Autonomous energy grids " (Redes de energía autónomas), en Proc. 51st Hawaii Int. Conf. System Sciences, enero
de 2018, págs. 2700-2709. doi: 10.24251/HICSS.2018.341.

noviembre/diciembre 2020	

A. Bernstein, E. Dall'Anese y A. Simonetto, " Online
primal-dual methods with measurement feedback for time-varying convex optimization " (Método primal-dual en
línea con retroalimentación de la medición para la optimización convexa variable en el tiempo), IEEE Trans. Signal
Process., vol. 67, n.° 8, págs. 1978-1991, 2019. doi: 10.1109/
TSP.2019.2896112.
A. Bernstein y E. Dall'Anese, " Real-time feedback-based optimization of distribution grids: A unified approach "
(Optimización de las redes de distribución basada en la retroalimentación en tiempo real: un enfoque unificado), IEEE
Trans. Control Netw. Syst., vol. 6, n.° 3, págs.  1197-1209,
Septiembre de 2019. doi: 10.1109/TCNS.2019.2929648.
X. Zhou, Z. Liu, C. Zhao y L. Chen, " Accelerated voltage regulation in multi-phase distribution networks based
on hierarchical distributed algorithm " (Regulación acelerada de la tensión en redes de distribución multifásica basada
en un algoritmo distribuido jerárquicamente), IEEE Trans.
Power Syst., vol. 35, n.° 3, págs. 2047-2058, mayo de 2020.
doi: 10.1109/TPWRS.2019.2948978.
X. Zhou, E. Dall'Anese y L. Chen, " Online stochastic
optimization of networked distributed energy resources "
(Optimización estocástica en línea de las fuentes de energía distribuida en red), IEEE Trans. Autom. Control, vol.
65, n.° 6, págs. 2387-2401, julio de 2019. doi: 10.1109/
TAC.2019.2927925.
M. Colombino, E. Dall'Anese y A. Bernstein, " Online
optimization as a feedback controller: Stability and tracking " (Optimización en línea como un controlador de retroalimentación: estabilidad y seguimiento), IEEE Trans. Control Netw. Syst., vol. 7, n.° 1, págs. 422-432, marzo de 2020.
doi: 10.1109/TCNS.2019.2906916.

Biografías
Benjamin Kroposki pertenece al Laboratorio Nacional de
Energías Renovables, Golden, Colorado.
Andrey Bernstein pertenece al Laboratorio Nacional de
Energías Renovables, Golden, Colorado.
Jennifer King pertenece al Laboratorio Nacional de
Energías Renovables, Golden, Colorado.
Deepthi Vaidhynathan pertenece al Laboratorio Nacional de Energías Renovables, Golden, Colorado.
Xinyang Zhou pertenece al Laboratorio Nacional de
Energías Renovables, Golden, Colorado.
Chin-Yao Chang pertenece al Laboratorio Nacional de
Energías Renovables, Golden, Colorado.
Emiliano Dall'Anese pertenece a la Universidad de Colorado, Boulder.
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IEEE Power & Energy Magazine - Spanish - November/December 2020

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IEEE Power & Energy Magazine - Spanish - November/December 2020 - Cover3
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