IEEE Power & Energy - Spanish - May/June 2018 - 15

rentes: datos de demanda de medidores
inteligentes domésticos, datos de unida-
des de medición de fasores (PMU, por
sus siglas en inglés) y predicciones de
energía eólica probabilísticas.
dado que las PMU están transfor-
mando la manera en que vemos las re-
des de transmisión de potencia, algunas
empresas de distribución de energía
han comenzado a investigar aplicacio-
nes de PMU a nivel de distribución,
también conocidas como micro PMU
(μPMU). Una μPMU típica lee 120 ve-
ces/segundo, es decir, 108,000 más rápi-
do que un medidor inteligente típico que
lee una vez cada 15 minutos. la forma
de traducir estos datos de frecuencia
ultra alta en funcionamientos de dis-
tribución y planificación mejorados se
convierte en un desafío emergente tan-
to para la industria como para el mun-
do académico. en el segundo artículo,
"Sincrofasores en la distribución", Mohse-
nian-rad y otros presentan una serie de
ejemplos del mundo real basados en los

datos de μPMU y eventos recopilados
de varios alimentadores de prueba de
12.47 kV en riverside, California. los
autores comienzan con cinco ejemplos
que ilustran las aplicaciones del análi-
sis descriptivo y predictivo en el diag-
nóstico defectuoso de la distribución y
el monitoreo de redes. luego, usan dos
ejemplos adicionales para motivar las
aplicaciones mejoradas de los datos de
μPMU y pedir soluciones escalables.
Aunque existe amplio apoyo para un
futuro con baja emisión de carbono, la
transición hacia una economía con baja
emisión de carbono presenta un desafío
enorme. el reino Unido, por ejemplo,
está enfrentando una disminución en el
subsidio para la generación renovable y
una reducción en la capacidad de la red.
Ambos factores están limitando la ace-
leración de la transición. li y otros, en
el tercer artículo, "Análisis de 'big data'
para el intercambio flexible de energía"
presentan dos esquemas de intercam-
bio para explotar la flexibilidad en un

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sistema energético: 1) la flexibilidad y
la energía compartidas mediante la co-
mercialización de energía entre pares
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acceso compartido a la red. en dicho
sistema de intercambio flexible de ener-
gía, los usuarios y los participantes del
mercado requieren información integral
para el conocimiento de la situación; los
precios tienen que establecerse en tiem-
po real; la flexibilidad en la demanda
necesita coincidir con los recursos ener-
géticos variables a través de un amplio
análisis de datos. Por lo tanto, el análisis
de "big data" pasa a ser un facilitador
clave de estos esquemas de intercambio.
en esta década, la industria de la
energía ha estado muy dedicada a la
gestión y análisis de datos de nuevas
fuentes, como los medidores inteligen-
tes y las PMU. Mientras tanto, no se ha
prestado demasiada atención a los datos
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está afectando muchas facetas de la in-
dustria de la energía, como la demanda

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 Advanced PMU-based Applications software portfolio
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Valley Authority, and United Kingdom Power Networks (England), among others. BSI's
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http://www.bigwood-systems.com

Table of Contents for the Digital Edition of IEEE Power & Energy - Spanish - May/June 2018

Contenidos
IEEE Power & Energy - Spanish - May/June 2018 - Cover1
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IEEE Power & Energy - Spanish - May/June 2018 - Contenidos
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IEEE Power & Energy - Spanish - May/June 2018 - Cover3
IEEE Power & Energy - Spanish - May/June 2018 - Cover4
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