T he intent of this document is to introduce a framework that will enable more thoughtful and deliberate consideration of resilience as it relates to electrical energy delivery systems (EEDS), with specific application to distributed wind systems. The need for this framework was established in a previous report from Idaho National Laboratory (INL), Distributed Wind Resilience Metrics for Electric Energy Delivery Systems, where definitions of resilience and resilience of EEDS were developed and a critical characteristic of resilience for EEDS, the distinctiveness quality, was identified. 1 This distinctiveness quality reflects the difficulty in applying resilience metrics broadly to the widely varied risk perception of stakeholders and stakeholder groups, the varied range of potential consequences to a system based upon events, and the large set of potential mitigation strategies. Development of resilience metrics, and more specifically distributed wind resilience metrics, must come from a resilience process that addresses this distinctiveness quality and is separate from well-established reliability processes. These two factors are the primary drivers demonstrating the need to establish a resilience methodology that can be applied to any electrical energy delivery system, any set of stakeholders, and any set of events.This framework is proposed under the Department of Energy (DOE) Wind Energy Technologies Office (WETO) Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project. While the focus of this project is on distributed wind, INL believes resilience is best evaluated at a system level. As such, the framework has been developed to broadly apply to EEDS so that all elements of systems that contain distributed wind can be part of the resilience evaluation. With this broad view, it is also possible to apply this framework to systems without distributed wind.
Sandia National Laboratories and Idaho National Laboratory deployed state-of-the-art cybersecurity technologies within a virtualized, cyber-physical wind energy site to demonstrate their impact on security and resilience. This work was designed to better quantify cost-benefit tradeoffs and risk reductions when layering different security technologies on wind energy industrial control system, operational technology networks. Standardized step-by-step attack scenarios were drafted for adversaries with remote and local access to the wind network. Then, the team investigated the impact of encryption, access control, intrusion detection, security information and event management, and security, orchestration, automation, and response (SOAR) tools on multiple metrics, including physical impacts to the power system and termination of the adversary kill chain. We found, once programmed, the intrusion detection systems could detect attacks and the SOAR system was able to effectively and autonomously quarantine the adversary, prior to impacting the power system. Cyber and physical metrics indicated good network and endpoint visibility were essential to demonstrate the resilience of the system. In particular, certain hardening technologies, like encryption, reduced access to the adversaries, but recognition and response were able to ensure there was no impact to the operation of the wind site generators.INDEX TERMS wind turbine generation, cybersecurity, cyber-resilience, co-simulation, cyber-physical systems However, the control systems necessary to integrate renewable energy systems expand the attack surface by exposing
W hile most people have a general concept of what it means to be "resilient, " an examination of definitions from different sources reveals that there are key commonalities, but key differences as well. The lack of a generally accepted definition and application of resilience extends to electric energy delivery systems. Without an accepted definition, it is difficult to implement programs or processes to improve resiliency. In this paper, existing work from industry, regulatory bodies, and national laboratories to define and apply resilience to electric energy delivery systems is studied to understand the key components to define resilience and better understand associated metrics. This understanding is then applied to distributed wind for a specific example of how resilience of a system is affected by the technologies and generation sources used to support it.A key finding is that there is no "one size fits all" process for resilience. Each system has a "distinctiveness" characteristic, which qualifies the possibility of differences in resilience due to different threats, geography, stakeholders, risk tolerance, and mitigations. The distinctiveness characteristic extends to distributed wind technologies and applications where different configurations may lend the distributed wind assets to contribute to the resilience of each system in a variety of ways.The findings of this research demonstrate the need for a resilience framework that can be readily applied by stakeholders to improve resilience based on the specific system, threat, risk tolerance and stakeholders.
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