Abstract
In the near future, it is highly expected that smart grid (SG) utilities will replace existing fixed pricing with dynamic pricing, such as time-of-use real-time tariff (ToU). In ToU, the price of electricity varies throughout the whole day based on the respective utilities’ decisions. We classify the whole day into two periods with very high and low probabilities of theft activities, termed as the “theft window” and “non-theft window”, respectively. A “smart” malicious consumer can adjust his/her theft to mostly targeting the theft window, manipulate actual usage reporting to outsmart existing theft detectors, and achieve the goal of “paying reduced tariff”. Simulation results show that existing schemes do not detect well such window-based theft activities conversely exploiting ToU strategies. In this paper, we begin by introducing the core concept of window-based theft cases, which is defined at the basis of ToU pricing as well as consumption usage. A modified extreme gradient boosting (XGBoost) based machine learning (ML) technique called dynamic electricity theft detector (DETD) has been presented to detect a new type of theft cases.
Highlights
One of the upcoming features of load analytics in the smart grid (SG) is to analyze overall load on the grid and determine dynamic pricing schemes so as to redistribute the consumption load in a balanced way
We see that dynamic electricity theft detector (DETD) detects higher theft cases than 87% and 89% of the theft cases detected by “vanilla” Gradient Boosting-based Theft Detector (GBTD) [12] and 67% and 72% of the theft cases detected by consumption pattern-based energy theft detector (CPBETD) for dynamic ToU (d-ToU) and fixed ToU (f-ToU) cases, respectively
For the upcoming SG utilities adopting the dynamic (d-ToU or f-ToU) pricing program, we have proposed a modified gradient boosting-based theft detector (DETD) that has a good detection capability for highly probable window-based theft cases
Summary
One of the upcoming features of load analytics in the smart grid (SG) is to analyze overall load on the grid and determine dynamic pricing schemes so as to redistribute the consumption load in a balanced way. We propose a novel algorithm called the dynamic electricity theft detector (DETD) by adding window-based theft cases (discussed below), where the ToUbased dataset of the Low Carbon London project [19] is used as an example to illustrate our concept. In order to tackle the lack of ToU pricing scheme-based theft detection, this paper begins by discussing how to model realistically feasible synthetic theft cases in the SG, followed by proposing a novel algorithm to detect the theft. In the DETD algorithm, we use ToU as an example external factor, such that training dataset generation is modified to accommodate the latest ToU pricing-based SG metering
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