Microgrids for power system resilience enhancement

Power system resilience is defined as the ability of power grids to anticipate,withstand,adapt and recover from high-impact low-probability(HILP)events.There are both long-term and short-term measures that system operators can employ for resilience rein-forcement.Longer-term measures include infrastructure hardening and resilient planning,while short-term operational measures are applied in the pre-event,during-event and post-event phases.Microgrids(MGs)can effectively enhance resilience for both transmission and distribution systems,due to their ability to operate in a controlled,coordinated way,when connected to the main power grid and in islanded mode.In this paper,MG-based strategies for resilience enhancement are presented,including MG-based resilient planning and MG-based operational measures,consisting of preventive MG scheduling and emergency measures and MG-based system restoration.Classification of literature is made by considering whether the transmission system,distribution system or individual MG resilience is targeted.The way uncertainties are handled by various methods is also outlined.Finally,challenges and future research requirements for improving MG-based power system resilience are highlighted.

A Quantitative Security Metric Model for Security Controls:Secure Virtual Machine Migration Protocol as Target of Assessment

Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the Info Sec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure(ARM), Performance Improvement Factor(PIF), and Cost/Benefit Measure(CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine(VM) migration protocol as the target of assessment. The virtual-ization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.

A Critical Review of Social Resilience Properties and Pathways in Disaster Management

Resilience as a concept is multi-faceted with complex dimensions.In a disaster context,there is lack of consistency in conceptualizing social resilience.This results in ambiguity of its definition,properties,and pathways for assessment.A number of key research gaps exist for critically reviewing social resilience conceptualization,projecting resilience properties in a disaster-development continuum,and delineating a resilience trajectory in a multiple disaster timeline.This review addressed these research gaps by critically reviewing social resilience definitions,properties,and pathways.The review found four variations in social resilience definitions,which recognize the importance of abilities of social systems and processes in disaster phases at different levels.A review of resilience properties and pathways in the disaster resilience literature suggested new resilience properties—“risk-sensitivity”and“regenerative”in the timeline of two consecutive disasters.This review highlights a causal pathway for social resilience to better understand the resilience status in a multi-shock scenario by depicting inherent and adaptive resilience for consecutive disaster scenarios and a historical case study for a resilience trajectory in a multiple disaster timeline.The review findings will assist disaster management policymakers and practitioners to formulate appropriate resilience enhancement strategies within a holistic framework in a multi-disaster timeline.

Establishment of the Psychometric Properties of a Disaster Resilience Measuring Tool for Healthcare Rescuers in China:A Cross-Sectional Study

The aim of this study was to test the validity and reliability of a tool for measuring the disaster resilience of healthcare disaster rescuers.A cross-sectional study involving 936 healthcare disaster rescuers of the Sichuan Disaster Response Team was conducted to establish the psychometric properties of the disaster resilience measuring tool(DRMT).Item analysis,exploratory factor analysis,confirmatory factor analysis,and correlation analysis were adopted to analyze the data.Item analysis showed that all but three items had the critical ratio over 3,which indicates adequate discriminability for inclusion in the measuring tool.The exploratory factor analysis showed that 65.93%of the total variance was explained by four factors—self-efficacy,social support,positive growth,and altruism.The confirmatory factor analysis showed goodness of fit for the four-factor model:CMIN/DF(2.846),GFI(0.916≥0.90),CFI(0.949≥0.90),AGFI(0.891≥0.80),and RMSEA(0.063≤0.08).Criterion validity demonstrated significant associations of the DRMT and the Connor-Davidson Resilience Scale(P<0.01,r=0.566).Convergent validity was established by correlation with stress(P<0.05,r=-0.095),depression(P<0.01,r=-0.127),posttraumatic stress disorder-PCL-C(P<0.05,r=-0.100),compassion satisfaction(P<0.01,r=0.536),and burnout(P<0.01,r=-0.330).The DRMT demonstrated adequate internal consistency(Cronbach’s alpha>0.84)and stability over the two-week study period(intraclass correlation coefficient>0.85),and a cut-off point of 61 was suggested.The disaster resilience measuring tool has satisfactory psychometric properties and is a valid,reliable,and valuable instrument for assessing disaster resilience in healthcare rescue workers.The scale needs to be tested further among other populations and those from other cultures.

Improved resilience measure for component recovery priority in power grids

Given the complexity of power grids,the failure of any component may cause large-scale economic losses.Consequently,the quick recovery of power grids after disasters has become a new research direction.Considering the severity of power grid disasters,an improved power grid resilience measure and its corresponding importance measures are proposed.The recovery priority of failed components after a disaster is determined according to the influence of the failed components on the power grid resilience.Finally,based on the data from the 2019 Power Yearbook of each city in Shandong Province,China,the power grid resilience after a disaster is analyzed for two situations,namely,partial components failure and failure of all components.Result shows that the recovery priorities of components with different importance measures vary.The resilience evaluations under different repair conditions prove the feasibility of the proposed method.

Measuring County Resilience After the 2008 Wenchuan Earthquake

The catastrophic earthquake that struck Sichuan Province,China,in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China.In recent years,great attention has been paid to the resilience of the affected area.This study applied the resilience inference measurement(RIM) model to quantify and validate the community resilience of 105 counties in the impacted area.The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure,damage,and recovery conditions.The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience.Analysis results show that counties located at the epicenter had the lowest resilience,but counties immediately adjacent to the epicenter had the highest resilience capacities.Counties that were farther away from the epicenter returned to normal resiliency quickly.Socioeconomic variables—including sex ratio,per capita GDP,percent of ethnic minority,and medical facilities—were identified as the most influential characteristics influencing resilience.This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.