Mathematical model to control the Argentine Energy System during the COVID 19 pandemic

Authors

Keywords:

electric power systems, Mathematical Modelling, COVID-19

Abstract

The novel coronavirus disease (COVID-19) has infected million people worldwide, causing more than 700,000 deaths since late 2019. The fight against this pandemic requires a joint effort among different areas as medicine, industry, technology and science. The normal performance of essential services is necessary in the context of facing this disease. The normal electricity supply ensures a correct health response, and it helps with the movements that mitigate the infections, as the social confinement. In consequence, this paper presents a mathematical model that analyzes the behavior of the Argentine Electric System, when the effects that the COVID-19 causes in the population are considered. The model reaches feasible and accurate solutions with low computational times. Test scenarios are based on reports of official agencies and predictions. The obtained results allow identifying critical areas of the system and developing corrective actions. In addition, this helps prepare the system to continue its operations under the worst-case scenarios that may arise from the disease.

Author Biographies

Gonzalo Exequiel Alvarez, CONICET

Gonzalo Exequiel Alvarez Ingeniero Electromecánico, graduado en la UTN – FRP (Argentina) en 2011.  Se graduó con el título de Doctor en Ingeniería-Mención Industrial en 2019, en la UTN–FRSF. Actualmente trabaja en CONICET, con  sede en INGAR. Sus áreas de especialización incluyen la optimización de sistemas de energía eléctrica, sistemas de acumulación de energía y procesos industriales

Juan Leonardo Sarli, INGAR-CONICET

Juan Leonardo Sarli recibió el título de Ingeniero en Sistemas de Información, de la UTN-FRSF (Argentina) en 2014. Actualmente se desempeña como analista de procesos de negocio, en una empresa de la región. Recibió su título de Doctor en Ingeniería mención Sistemas de Información de la UTN-FRSF en 2019. Su línea de investigación aborda la modelado e implementación de procesos de negocio en cadenas de suministro.

 

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Published

2020-09-16

Issue

Section

Special Issue on Fighting against COVID-19
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