Quantifying the Microeconomic Impact of Stealthy Meter Tampering Attacks on Real-Time Electricity Markets

• Adaan Ahsun

  Covenant University

  Author

Stealthy meter tampering attacks pose a dual threat to modern electricity distribution systems: they erode utility revenues through non-technical losses while simultaneously distorting real-time market clearing signals that determine locational marginal prices. Despite extensive research on electricity theft detection, no validated framework exists that simultaneously quantifies the microeconomic impact of stealthy tampering on real-time market outcomes and provides cryptographically verifiable detection mechanisms. This study addresses this gap through a quantitative, design-based research approach combining retrospective market simulation with machine learning classification and blockchain validation. Using synthetic attack injections into a 5-bus real-time market model calibrated to Nigerian distribution conditions, we demonstrate that stealthy tampering attacks produce average price distortions of 12.7% and utility revenue losses of 18.3% per compromised meter. The proposed hybrid detection framework, integrating a Random Forest classifier with SHA-256 blockchain validation, achieves 89.4% detection accuracy with a false positive rate of 8.2%, outperforming baseline statistical and deep learning approaches. Blockchain anchoring reduced false positive disputes by 73% in simulated regulatory review scenarios. The framework provides utilities and regulators with a replicable methodology for quantifying tampering-induced market distortions while establishing tamper-evident audit trails that satisfy emerging regulatory requirements. These findings extend both the electricity theft detection literature and market security research by demonstrating that detection accuracy alone is insufficient—cryptographic validation is necessary for evidentiary integrity in enforcement proceedings.

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Copyright (c) 2026 Adaan Ahsun (Author)

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