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

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.