Cyber Physical Security Analytics for Transactive Energy Systems

With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large number of distributed edge-computing and Internet of Things (IoT) devices making autonomous/semi-autonomous decisions on energy production, and demand response. However, the electric grid cyber assets and the IoT devices are increasingly vulnerable to attack. TES will likely have similar vulnerabilities and cyber attacks especially with financial interest motives of stakeholders, which could affect the operation of the power grid. Therefore, new analytical methods are needed to continuously monitor these systems operations and detect malicious activity. In this research work, various components of transactive energy systems are modeled and simulated in detail. Various cyber attack models are developed based on threats identified in TES. A deep learning approach called deep stacked autoencoder (SAE) is utilized to detect possible anomalies in the market and physical system measurements. The proposed unsupervised technique is validated for satisfactory performance to detect anomalies and trigger a further investigation for root cause analysis using end-to-end TES testbed and use case.