Resilient Automated Distributed Device Scheduling in Smart Grids under False Data Injection Attacks

Utility companies are expected to leverage the flexibility in residential demand and the penetration of smart/IoT devices to implement efficient and effective residential demand response schemes with automated device scheduling. The use of distributed architectures in these systems allows adversaries to employ false data injection attacks (FDIA) to manipulate optimal decisions, as utility companies do not have complete control over the systems and data. Thus, reliable implementations are required to ensure the resilience of residential DR schemes against FDIAs. In this work, we integrate DR optimisation, anomaly detection in the context of FDIA detection, and attack impact mitigation methods to implement a resilient, automated device scheduling framework. We evaluate the implemented framework using a real-world dataset to emphasise the robustness of the framework against FDIAs.