A Method for the Characterization of the Energy Demand Aggregate Based on Electricity Data Provided by AMI Systems and Metering in Substations

Oscar A. Bustos-Brinez, Javier E. Duarte, Alvaro Zambrano-Pinto, Fabio A. González and Javier Rosero-Garcia ()
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Oscar A. Bustos-Brinez: MindLab Research Group, Department of Systems and Industrial Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Javier E. Duarte: EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Alvaro Zambrano-Pinto: EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Fabio A. González: MindLab Research Group, Department of Systems and Industrial Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Javier Rosero-Garcia: EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia

Energies, 2023, vol. 17, issue 1, 1-23

Abstract: This paper presents a methodology developed to perform the processing, analysis, and characterization of AMI measurement data from the substations of three network operators of the Colombian electrical grid. This methodology includes the analysis of the data, which presents the sources of information used by the model, along with the preprocessing and exploratory analysis of the substations data. It also includes the formulation of the data reconstruction method, which uses a constrained optimization model to characterize the substations, based on the different behaviors of the end users of the Colombian electrical grid. In addition to the proposed methodology, the results of its application to the data provided by the operators are provided. These results show the capacity of the proposed methodology to adequately identify the most common behaviors of the users in a given area and characterize most of the energy demand profiles of each substation.

Keywords: customer data analysis; optimization modeling; electrical grid management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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