Contextual Dishonest Behaviour Detection for Cognitive Adaptive Charging in Dynamic Smart Micro-Grids

Abstract

The emerging Smart Grid (SG) paradigm promises to address decreasing grid stability from thinning safe operating margins, meet continually rising demand from pervasive high capacity devices such as electric vehicles (EVs), and fully embrace the shift towards green energy solutions. At the SG edge, widespread decentralisation of heterogeneous devices coupled with fluctuating energy availability and need as well as a greatly increased fluidity between their roles as energy producers, consumers, and stores raises significant challenges to ensuring robustness and security of both information and energy exchange. Detecting and mitigating both malicious and non-malicious threats in these environments is essential to the realisation of the full potential of the SG. To address this need for robust, localised, real-time security at the grid edge we propose CONCEDE, a collaborative cross-layer ego-network integrity awareness and attack impact reduction extension to our previous work on delay-tolerant cognitive adaptive energy exchange. We detail a substantial, targeted, energy disruption attack perpetrated by colluding mobile energy prosumers. Our CONCEDE proposal is then evaluated in multiple, diverse smart micro-grid (SMG) scenarios using hybrid traces of EVs and infrastructure from Europe, North America, and South America in the presence of a coordinated attack from malicious distributors seeking to disrupt energy supply to a target community. We show that CONCEDE successfully detects and identifies the nodes exhibiting malicious, dishonest behaviour and that CONCEDE also reduces the impact of a coordinated energy disruption attack on innocent parties in all explored scenarios across multiple criteria. Keywords— Smart energy, Mobile DTNs, Autonomous Vehicles, Security