Biblio

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

Resilience is regarded nowadays as the ideal solution that can be envisaged by sociotechnical systems for coping with potential threats and crises. This being said, gaining and maintaining this ability is not always easy, given the multitude of risks driving the adverse and challenging events. This paper aims to propose a method consecrated to the assessment of risks directly affecting resilience. This work is conducted within the framework of risk assessment and resilience engineering approaches. A 5×5 matrix, dedicated to the identification and assessment of risk factors that constitute threats to the system resilience, has been elaborated. This matrix consists of two axes, namely, the impact on resilience metrics and the availability and effectiveness of resilience planning. Checklists serving to collect information about these two attributes are established and a case study is undertaken. In this paper, a new method for identifying and assessing risk factors menacing directly the resilience of a given system is presented. The analysis of these risks must be given priority to make the system more resilient to shocks.

Cyber-Physical Systems (CPSs) interconnect the physical world with digital computers and networks in order to automate production and distribution processes. Nowadays, most CPSs do not work in isolation, but their digital part is connected to the Internet in order to enable remote monitoring, control and configuration. Such a connection may offer entry-points enabling attackers to gain control silently and exploit access to the physical world at the right time to cause service disruption and possibly damage to the surrounding environment. Prevention and monitoring measures can reduce the risk brought by cyber attacks, but the residual risk can still be unacceptably high in critical infrastructures or services. Resilience - i.e., the ability of a system to withstand adverse events while maintaining an acceptable functionality - is therefore a key property for such systems. In our research, we seek a model-free, quantitative, and general-purpose evaluation methodology to extract resilience indexes from, e.g., system logs and process data. While a number of resilience metrics have already been put forward, little experimental evidence is available when it comes to the cyber security of CPSs. By using the model of a real wastewater treatment plant, and simulating attacks that tamper with a critical feedback control loop, we provide a comparison between four resilience indexes selected through a thorough literature review involving over 40 papers. Our results show that the selected indexes differ in terms of behavior and sensitivity with respect to specific attacks, but they can all summarize and extract meaningful information from bulky system logs. Our evaluation includes an approach for extracting performance indicators from observed variables which does not require knowledge of system dynamics; and a discussion about combining resilience indexes into a single system-wide measure is included. 11The authors wish to thank Leonardo S.p.A. for its financial support. The research herein presented is partially supported by project NEFERIS awarded by the Italian Ministry of Defense to Leonardo S.p.A. in partnership with the University of Genoa. This work received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 830892 for project SPARTA.

In this work, we use a subjective approach to compute cyber resilience metrics for industrial control systems. We utilize the extended form of the R4 resilience framework and span the metrics over physical, technical, and organizational domains of resilience. We develop a qualitative cyber resilience assessment tool using the framework and a subjective questionnaire method. We make sure the questionnaires are realistic, balanced, and pertinent to ICS by involving subject matter experts into the process and following security guidelines and standards practices. We provide detail mathematical explanation of the resilience computation procedure. We discuss several usages of the qualitative tool by generating simulation results. We provide a system architecture of the simulation engine and the validation of the tool. We think the qualitative simulation tool would give useful insights for industrial control systems' overall resilience assessment and security analysis.