Robustness of Local Energy Markets to Cyberattacks: Case Study of False Data Injection

2026-09-29

AI Summary

This study addresses the vulnerability of prediction-reliant community energy markets to coordinated false data injection (FDI) attacks by proposing a bilevel optimization framework to identify worst-case bus-level attacks. The upper level maximizes physical impact, while the lower level re-clears the interconnected market. By formulating a trade-off model balancing physical consequences against detectability, this work reveals that system vulnerability depends on the spatiotemporal distribution of tampered data rather than uniform diffusion. Leveraging Karush–Kuhn–Tucker conditions, the framework is reformulated as a single-level mixed-integer program and solved via the ε-constraint method. Validation on the IEEE 33-bus system demonstrates that bounded zero-sum attacks can reshape transactions, erode voltage security margins, and induce asymmetric market outcomes.

Abstract

Market clearing in community-based local energy markets relies on power demand and PV forecasts, making it vulnerable to coordinated false data injection (FDI) attacks. This paper proposes a bilevel optimization framework to identify worst-case bus-level FDI against interconnected multi-community electricity markets within a distribution network. The upper level attacker maximizes physical impact, measured by cumulative voltage deviation, while accounting for detectability. The lower level re-clears the interconnected multi-community markets subject to operational and network constraints. The bilevel model is reformulated as a single-level mixed-integer program through Karush-Kuhn-Tucker conditions and solved using the epsilon constraint method to characterize the trade-off between physical impact and detectability. Case studies on the IEEE 33-bus system with three communities show that even bounded, system-level zero-sum attacks can reshape local trading, reduce voltage security margins, and produce asymmetric community-level market outcomes. The results further show that vulnerability depends strongly on the spatiotemporal placement of falsified data rather than on uniform spreading across buses and time periods.

Problem

• Local Energy Markets
• False Data Injection
• Cyberattacks
• Market Clearing
• Voltage Security

Innovation

• Bilevel optimization
• False data injection
• Local energy markets
• Mixed-integer programming
• Cyberattack robustness