APPLYING PRINCIPAL COMPONENT ANALYSIS FOR AGGREGATION OF ENVIRONMENTAL VARIABLES AFFECTING THE OPERATIONS OF ELETRICITY DISTRIBUTORS IN BRAZIL

Vol 56, 2024 - 308482
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Abstract

The National Electric Energy Agency (ANEEL) in Brazil regulates electricity tariffs using benchmarking methods like Data Envelopment Analysis (DEA) to assess the efficiency of operating costs among distributors. Recognizing that operational efficiency varies with geographic conditions, this study suggests incorporating environmental and operational factors into the DEA to avoid distortions. Principal Component Analysis (PCA) is introduced to integrate these environmental influences into the evaluation. By analyzing 21 environmental variables, PCA reduced them to four main components that significantly affect operating costs: Territoriality, Climatic Factor, Wildfires, and Accessibility/Infrastructure. These components enable adjustments to operating costs, ensuring a more level playing field by simulating similar environmental conditions across all companies. Applying this approach in a second-stage DEA model can enhance the accuracy and fairness of regulatory benchmarking and simplify result analysis by reducing the number of variables considered.

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Institutions
  • 1 UFMG
Track
  • 7. DEA – Data envelopment analysis
Keywords
Principal Component Analysis (PCA)
Operations of electricity distributor
Unsupervised analysis
Machine learning