Maximizing the Financial Return of Non-Technical Loss Management in Power Distribution Systems

Despite many studies have been published on non-technical loss management, there is a lack of solutions focused in the financial return of utilities' actions. This paper aims to contribute to fill up this gap, presenting a new approach that maximizes the financial return of utilities when selec...

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Bibliographic Details
Published in:IEEE transactions on power systems 2022-03, Vol.37 (2), p.1634-1641
Main Authors: Barros, Rafael Mendonca Rocha, da Costa, Edson Guedes, Araujo, Jalberth Fernandes
Format: Article
Language:English
Subjects:
Algorithms
Boosting
Classification algorithms
Consumers
Electric power distribution
Energy recovery
financial return
Forestry
Inspection
Inspections
machine learning
Mathematical analysis
maximization
non-technical losses
Optimization
Power distribution
power distribution systems
Predictive models
rotation Forest
Utilities
XGBoost
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Summary:Despite many studies have been published on non-technical loss management, there is a lack of solutions focused in the financial return of utilities' actions. This paper aims to contribute to fill up this gap, presenting a new approach that maximizes the financial return of utilities when selecting consumers for field inspections. The predicted return of an inspection is calculated considering its operational cost versus the potential of energy recovery and the tariff value. The potential of energy recovery, in turn, is calculated considering the probability of non-technical loss existence and the forecast of unmeasured energy. The Rotation Forest algorithm is utilized to indicate the non-technical loss existence, while the eXtreme Gradient Boosting algorithm is utilized for energy recovery forecast. The proposed approach was applied in a real database of 261,489 consumers from a Brazilian utility. Then, 338 new field inspections were performed on unlabeled consumers in order to corroborate results in a real application. Achieved results showed an increase of up to 11.5 times in the financial return of field inspections when applying the proposed approach. Results could also be used to determine the optimal number of inspections in the utility, for which the financial return is maximum.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3107602