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Article

Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean

1
School of Management, Universidad de los Andes, Bogotá 111711, Colombia
2
Centro de Finanzas, IESA, Caracas 1011, Venezuela
3
Idalion Capital Group, Quantitative Trading, 12 Hay Hill, London W1J 8NR, UK
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(3), 281; https://doi.org/10.3390/e23030281
Received: 21 November 2020 / Revised: 10 December 2020 / Accepted: 14 December 2020 / Published: 26 February 2021
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much energy must be sent from each supplier to satisfy each demand. Besides, the energy distribution process may have to satisfy ecological, technological, or economic cost constraints. View Full-Text
Keywords: electricity supply and demand; energy supply diversification; contamination constraints; inverse problems; maximum entropy in the mean electricity supply and demand; energy supply diversification; contamination constraints; inverse problems; maximum entropy in the mean
MDPI and ACS Style

Bautista, R.; Gzyl, H.; ter Horst, E.; Molina, G. Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean. Entropy 2021, 23, 281. https://doi.org/10.3390/e23030281

AMA Style

Bautista R, Gzyl H, ter Horst E, Molina G. Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean. Entropy. 2021; 23(3):281. https://doi.org/10.3390/e23030281

Chicago/Turabian Style

Bautista, Rafael, Henryk Gzyl, Enrique ter Horst, and Germán Molina. 2021. "Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean" Entropy 23, no. 3: 281. https://doi.org/10.3390/e23030281

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