Biblio

The integration of distributed energy resources (DERs) and expansion of complex network in the distribution grid requires an advanced two-level state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper implements a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the implemented MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs), deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement this methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the "valley of death" and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex (CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time.

Distributed wind is an electric energy resource segment with strong potential to be deployed in many applications, but special consideration of resilience and cybersecurity is needed to address the unique conditions associated with distributed wind. Distributed wind is a strong candidate to help meet renewable energy and carbon-free energy goals. However, care must be taken as more systems are installed to ensure that the systems are reliable, resilient, and secure. The physical and communications requirements for distributed wind mean that there are unique cybersecurity considerations, but there is little to no existing guidance on best practices for cybersecurity risk management for distributed wind systems specifically. This research develops an architecture for managing cyber risks associated with distributed wind systems through resilience functions. The architecture takes into account the configurations, challenges, and standards for distributed wind to create a risk-focused perspective that considers threats, vulnerabilities, and consequences. We show how the resilience functions of identification, preparation, detection, adaptation, and recovery can mitigate cyber threats. We discuss common distributed wind architectures and interconnections to larger power systems. Because cybersecurity cannot exist independently, the cyber-resilience architecture must consider the system holistically. Finally, we discuss risk assessment recommendations with special emphasis on what sets distributed wind systems apart from other distributed energy resources (DER).