Enhancing Processing Time for Uncertainty Cost Quantification: Demonstration in a Scheduling Approach for Energy Management Systems

Luis Carlos Pérez Guzmán, Gina Idárraga-Ospina () and Sergio Raúl Rivera Rodríguez ()
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Luis Carlos Pérez Guzmán: Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, San Nicolas de los Garza 66451, NL, Mexico
Gina Idárraga-Ospina: Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, San Nicolas de los Garza 66451, NL, Mexico
Sergio Raúl Rivera Rodríguez: Electrical and Electronics Engineering Department, Universidad Nacional de Colombia, Bogota 111321, Colombia

Sustainability, 2025, vol. 17, issue 15, 1-19

Abstract: This paper calculates the expected cost of uncertainty in solar and wind energy using the uncertainty cost function (UCF), with a primary focus on computational processing time. The comparison of processing time for the uncertainty cost quantification (UCQ) is conducted through three methods: the Monte Carlo simulation method (MC), numerical integration method, and analytical method. The MC simulation relies on random simulations, while numerical integration employs established numerical formulations. These methods are commonly used for solving cost optimization problems in power systems. However, the analytical method is a less conventional approach. The analytical method for calculating uncertainty costs is closely related to the UCF, as it relies on a mathematical representation of the impact of uncertainty on costs, which is modeled through the UCF. A multi-objective approach was employed for scheduling an energy management system, that is to say, thermal–wind–solar energy systems, proposing a simplified method for modeling controllable renewable generation through UCF with an analytical method, instead of the complex probability distributions typically used in traditional methods. This simplification reduces complexity and computational processing time in optimization problems, offering greater accuracy in approximating real distributions and adaptability to various scenarios. The simulations performed yielded positive results in improving cost estimation and computational efficiency, making it a promising tool for enhancing economic distribution and grid operability.

Keywords: analytical method; Monte Carlo; multi-objective; optimization; solar energy; uncertainty cost functions; uncertainty cost quantification; wind energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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