Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984025
Priscila Silva, Mariana Hermosillo Hidalgo, I. Linkov, L. Fiondella
Resilience is the ability of a system to respond, absorb, adapt, and recover from a disruptive event. Dozens of metrics to quantify resilience have been proposed in the literature. However, fewer studies have proposed models to predict these metrics or the time at which a system will be restored to its nominal performance level after experiencing degradation. This paper presents two alternative approaches to model and predict performance and resilience metrics with techniques from reliability engineering, including (i) bathtub-shaped hazard functions and (ii) mixture distributions . Given their ease of accessibility, historical data sets on job losses during recessions in the United States are used to assess the predictive accuracy of these approaches. Goodness of fit measures and confidence interval are computed to assess how well the models perform on the data sets considered. The results suggest that both approaches can produce accurate predictions for data sets exhibiting V and U shaped curves, but that L and W shaped curves that respectively experience a sudden drop in performance or deviate from the assumption of a single decrease and subsequent increase cannot be characterized well by either class of model proposed, necessitating additional modeling efforts that can capture these more general scenarios.
{"title":"Predictive Resilience Modeling","authors":"Priscila Silva, Mariana Hermosillo Hidalgo, I. Linkov, L. Fiondella","doi":"10.1109/RWS55399.2022.9984025","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984025","url":null,"abstract":"Resilience is the ability of a system to respond, absorb, adapt, and recover from a disruptive event. Dozens of metrics to quantify resilience have been proposed in the literature. However, fewer studies have proposed models to predict these metrics or the time at which a system will be restored to its nominal performance level after experiencing degradation. This paper presents two alternative approaches to model and predict performance and resilience metrics with techniques from reliability engineering, including (i) bathtub-shaped hazard functions and (ii) mixture distributions . Given their ease of accessibility, historical data sets on job losses during recessions in the United States are used to assess the predictive accuracy of these approaches. Goodness of fit measures and confidence interval are computed to assess how well the models perform on the data sets considered. The results suggest that both approaches can produce accurate predictions for data sets exhibiting V and U shaped curves, but that L and W shaped curves that respectively experience a sudden drop in performance or deviate from the assumption of a single decrease and subsequent increase cannot be characterized well by either class of model proposed, necessitating additional modeling efforts that can capture these more general scenarios.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122055181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984016
Michael Abdelmalak, Sean Ericson, Jordan Cox, Mohammed Ben-Idris, E. Hotchkiss
Catastrophic impacts to power systems due to disruptive events have increased significantly during the last decade. These events highlight the need to develop approaches to assess the resilience of power systems against extreme events. However, the availability of data that capture power system performance during and after disruptive events is scarce. This paper proposes an assessment framework to evaluate the performance aspects of the grid system during extreme outage events using the Environment for Analysis of Geo-Located Energy Information (EAGLE-I) data. EAGLE-I includes information related to the number of impacted customers, duration, and location of power outages in the United States. Statistical analyses were conducted to extract resilient-based outage data and derive probability distribution functions of their impact and recovery characteristics. A list of extreme events is identified based on few predetermined threshold values. Metrics from other power outage assessments were used to measure the characteristics of each event, including impact rate and duration, recovery rate and duration, and impact level. A probability distribution function is obtained for each metric. The obtained results provide a representation of national grid performance during extreme events, which can be applied as a framework to evaluate various resilience enhancement techniques.
{"title":"A Power Outage Data Informed Resilience Assessment Framework","authors":"Michael Abdelmalak, Sean Ericson, Jordan Cox, Mohammed Ben-Idris, E. Hotchkiss","doi":"10.1109/RWS55399.2022.9984016","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984016","url":null,"abstract":"Catastrophic impacts to power systems due to disruptive events have increased significantly during the last decade. These events highlight the need to develop approaches to assess the resilience of power systems against extreme events. However, the availability of data that capture power system performance during and after disruptive events is scarce. This paper proposes an assessment framework to evaluate the performance aspects of the grid system during extreme outage events using the Environment for Analysis of Geo-Located Energy Information (EAGLE-I) data. EAGLE-I includes information related to the number of impacted customers, duration, and location of power outages in the United States. Statistical analyses were conducted to extract resilient-based outage data and derive probability distribution functions of their impact and recovery characteristics. A list of extreme events is identified based on few predetermined threshold values. Metrics from other power outage assessments were used to measure the characteristics of each event, including impact rate and duration, recovery rate and duration, and impact level. A probability distribution function is obtained for each metric. The obtained results provide a representation of national grid performance during extreme events, which can be applied as a framework to evaluate various resilience enhancement techniques.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130122775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984018
C. Fallon, Brett A. Jefferson, E. Andersen
Although a wide array of tools and technologies have been developed over the last decade to support power grid operators, deployment of these tools has been less successful. One reason for unsuccessful deployment may be a focus on error reduction without an adequate understanding of the factors that contribute to operator error in the control room. An analysis of these factors (i.e., vulnerabilities) may provide the baseline understanding needed to inform new technology integration. In an attempt to learn more about these vulnerabilities and their perceived impact on human error we collected and analyzed survey data from 20 electric grid control room operators. We asked survey respondents to consider the various operator, technology and interaction vulnerabilities that may arise during work in the control room and record their attitudes and experiences toward each. Results suggest operator inexperience, high mental workload and fatigue are the most common vulnerabilities experienced during a shift. Technology solutions should set operators up for success by addressing these factors. Survey results were analyzed to explore these vulnerabilities in greater depth.
{"title":"An Analysis of Grid Operator Survey Responses: Inexperience, Workload and Fatigue in the Control Room","authors":"C. Fallon, Brett A. Jefferson, E. Andersen","doi":"10.1109/RWS55399.2022.9984018","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984018","url":null,"abstract":"Although a wide array of tools and technologies have been developed over the last decade to support power grid operators, deployment of these tools has been less successful. One reason for unsuccessful deployment may be a focus on error reduction without an adequate understanding of the factors that contribute to operator error in the control room. An analysis of these factors (i.e., vulnerabilities) may provide the baseline understanding needed to inform new technology integration. In an attempt to learn more about these vulnerabilities and their perceived impact on human error we collected and analyzed survey data from 20 electric grid control room operators. We asked survey respondents to consider the various operator, technology and interaction vulnerabilities that may arise during work in the control room and record their attitudes and experiences toward each. Results suggest operator inexperience, high mental workload and fatigue are the most common vulnerabilities experienced during a shift. Technology solutions should set operators up for success by addressing these factors. Survey results were analyzed to explore these vulnerabilities in greater depth.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984022
W. Al-Aqqad, Hassan S. Hayajneh, Xuewei Zhang
A general modeling and simulation framework is developed in this work to quantitatively evaluate the resilience of networked systems under two types of failures: connectivity- and load-based. Two newly designed dynamic healing mechanisms are demonstrated. The model considers concurrent cascading failure and healing processes on networks. The discrete-time simulations generate system trajectories, i.e., number of failed nodes at each time step. The 95% recovery time is used as the resilience metric to evaluate and compare the healing performance. Based on two real-world networks, the dependence of system trajectories and resilience metric on various model parameters is explored. If the triggering level (fraction of inactive nodes when healing starts) is too high, the system would either undergo a very slow recovery or never recover to a satisfactory level at all. However, this work provides a counter example to the intuition that the smaller the triggering level, the shorter the recovery time. While low budgets (number of nodes allowed to recover at each time step) lead to prolonged or unsuccessful recovery, it appears that the resilience metric converges to a limit when budget is raised to high enough. This may have practical implications, as node recovery requires resources and a budget too high or too low would be wasteful. This works lays the foundation for subsequent studies on more complex mechanisms and processes on the networks, optimization of model parameters for maximum resilience, as well as applications to more real-world scenarios.
{"title":"Resilience of Networked Systems under Connectivity-Based and Load-Based Failures","authors":"W. Al-Aqqad, Hassan S. Hayajneh, Xuewei Zhang","doi":"10.1109/RWS55399.2022.9984022","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984022","url":null,"abstract":"A general modeling and simulation framework is developed in this work to quantitatively evaluate the resilience of networked systems under two types of failures: connectivity- and load-based. Two newly designed dynamic healing mechanisms are demonstrated. The model considers concurrent cascading failure and healing processes on networks. The discrete-time simulations generate system trajectories, i.e., number of failed nodes at each time step. The 95% recovery time is used as the resilience metric to evaluate and compare the healing performance. Based on two real-world networks, the dependence of system trajectories and resilience metric on various model parameters is explored. If the triggering level (fraction of inactive nodes when healing starts) is too high, the system would either undergo a very slow recovery or never recover to a satisfactory level at all. However, this work provides a counter example to the intuition that the smaller the triggering level, the shorter the recovery time. While low budgets (number of nodes allowed to recover at each time step) lead to prolonged or unsuccessful recovery, it appears that the resilience metric converges to a limit when budget is raised to high enough. This may have practical implications, as node recovery requires resources and a budget too high or too low would be wasteful. This works lays the foundation for subsequent studies on more complex mechanisms and processes on the networks, optimization of model parameters for maximum resilience, as well as applications to more real-world scenarios.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130751731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984032
B. Poudel, T. McJunkin, J. Reilly, Juan Gallego-Calderon, Ning Kang, M. Stadler
This paper presents a financial, resilience, and environmental case for including small reactors (SRs) in the suite of candidate electricity and heat generation for microgrids. Microgrids are accepted as a strong provider of resilience to sustaining life and mission-critical services. However, in today’s form, they are based on carbon-intensive energy sources. The analysis of this paper develops a new technoeconomic model for SRs that approximately translates their financial and operational characteristics to a gas generator modeled in a microgrid optimization software. The SR model captures the major technoeconomic characteristics of SRs and effectively utilizes them for microgrid planning studies. A feasibility study is conducted for a microgrid proposed for a military base in California. Multiple optimization scenarios are developed based on the resilience requirement, cost reduction, potential taxation on CO2 emission, and lower investment costs by sizing the plant at scale (economies of scale). The results from the scenarios and subsequent comparative analysis show that SRs would be a cost-competitive generation option when the CO2 tax is imposed on carbon fuels. Furthermore, if capital costs are modeled considering the potential of cost reduction from sizing the plant at scale, SRs would be even more attractive than gas generators.
{"title":"Small Reactors in Microgrids: A Financial, Resilience and Environmental Case","authors":"B. Poudel, T. McJunkin, J. Reilly, Juan Gallego-Calderon, Ning Kang, M. Stadler","doi":"10.1109/RWS55399.2022.9984032","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984032","url":null,"abstract":"This paper presents a financial, resilience, and environmental case for including small reactors (SRs) in the suite of candidate electricity and heat generation for microgrids. Microgrids are accepted as a strong provider of resilience to sustaining life and mission-critical services. However, in today’s form, they are based on carbon-intensive energy sources. The analysis of this paper develops a new technoeconomic model for SRs that approximately translates their financial and operational characteristics to a gas generator modeled in a microgrid optimization software. The SR model captures the major technoeconomic characteristics of SRs and effectively utilizes them for microgrid planning studies. A feasibility study is conducted for a microgrid proposed for a military base in California. Multiple optimization scenarios are developed based on the resilience requirement, cost reduction, potential taxation on CO2 emission, and lower investment costs by sizing the plant at scale (economies of scale). The results from the scenarios and subsequent comparative analysis show that SRs would be a cost-competitive generation option when the CO2 tax is imposed on carbon fuels. Furthermore, if capital costs are modeled considering the potential of cost reduction from sizing the plant at scale, SRs would be even more attractive than gas generators.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115571919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984017
T. Farkas, M. Bernauer, Umang Shah, Kaitlyn Webster, Trisha Miller
Predicting which communities will be most disrupted by natural or anthropogenic disasters is of central concern to strategic planners seeking to optimize equitable outcomes of infrastructure investment. In this paper, we describe an approach to using mobile location data to estimate the relative magnitude of disruption across communities with arbitrary boundary delineations and use predictive modeling to show how mobility metrics and Census-based demographic information can be combined to predict the impact of similar disasters in novel scenarios. We demonstrate our approach through application of the proposed methodology to the Colonial Pipeline hack of 2021 and discuss opportunities for alternatives and refinements given additional data sets. The resulting movement-based estimation and prediction approach offers an avenue for ensuring a more resilient nation through strategic planning.
{"title":"Movement-based disruption estimators: Using mobile location data to predict community variation in disaster impacts","authors":"T. Farkas, M. Bernauer, Umang Shah, Kaitlyn Webster, Trisha Miller","doi":"10.1109/RWS55399.2022.9984017","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984017","url":null,"abstract":"Predicting which communities will be most disrupted by natural or anthropogenic disasters is of central concern to strategic planners seeking to optimize equitable outcomes of infrastructure investment. In this paper, we describe an approach to using mobile location data to estimate the relative magnitude of disruption across communities with arbitrary boundary delineations and use predictive modeling to show how mobility metrics and Census-based demographic information can be combined to predict the impact of similar disasters in novel scenarios. We demonstrate our approach through application of the proposed methodology to the Colonial Pipeline hack of 2021 and discuss opportunities for alternatives and refinements given additional data sets. The resulting movement-based estimation and prediction approach offers an avenue for ensuring a more resilient nation through strategic planning.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984040
Burhan Hyder, Harrison Majerus, Hayden Sellars, Jonathan Greazel, Joseph Strobel, Nicholas Battani, Stefan Peng, M. Govindarasu
Cyber physical system (CPS) Critical infrastructures (CIs) like the power and energy systems are increasingly becoming vulnerable to cyber attacks. Mitigating cyber risks in CIs is one of the key objectives of the design and maintenance of these systems. These CPS CIs commonly use legacy devices for remote monitoring and control where complete upgrades are uneconomical and infeasible. Therefore, risk assessment plays an important role in systematically enumerating and selectively securing vulnerable or high-risk assets through optimal investments in the cybersecurity of the CPS CIs. In this paper, we propose a CPS CI security framework and software tool, CySec Game, to be used by the CI industry and academic researchers to assess cyber risks and to optimally allocate cybersecurity investments to mitigate the risks. This framework uses attack tree, attack-defense tree, and game theory algorithms to identify high-risk targets and suggest optimal investments to mitigate the identified risks. We evaluate the efficacy of the framework using the tool by implementing a smart grid case study that shows accurate analysis and feasible implementation of the framework and the tool in this CPS CI environment.
{"title":"CySec Game: A Framework and Tool for Cyber Risk Assessment and Security Investment Optimization in Critical Infrastructures","authors":"Burhan Hyder, Harrison Majerus, Hayden Sellars, Jonathan Greazel, Joseph Strobel, Nicholas Battani, Stefan Peng, M. Govindarasu","doi":"10.1109/RWS55399.2022.9984040","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984040","url":null,"abstract":"Cyber physical system (CPS) Critical infrastructures (CIs) like the power and energy systems are increasingly becoming vulnerable to cyber attacks. Mitigating cyber risks in CIs is one of the key objectives of the design and maintenance of these systems. These CPS CIs commonly use legacy devices for remote monitoring and control where complete upgrades are uneconomical and infeasible. Therefore, risk assessment plays an important role in systematically enumerating and selectively securing vulnerable or high-risk assets through optimal investments in the cybersecurity of the CPS CIs. In this paper, we propose a CPS CI security framework and software tool, CySec Game, to be used by the CI industry and academic researchers to assess cyber risks and to optimally allocate cybersecurity investments to mitigate the risks. This framework uses attack tree, attack-defense tree, and game theory algorithms to identify high-risk targets and suggest optimal investments to mitigate the identified risks. We evaluate the efficacy of the framework using the tool by implementing a smart grid case study that shows accurate analysis and feasible implementation of the framework and the tool in this CPS CI environment.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984036
Ruixuan Li, T. Phillips, T. McJunkin, K. L. Blanc
In recent years, integrating innovative technologies into the work domain have reduced workload, simplified the work process, saved business costs, and generated additional revenue. Use case analysis is widely applied to identify functionalities and communicate the applicational details for technology implementations. In this study, we propose a way to frame a use case for effectively communicating across the multidisciplinary team in the design and continuous improvement phases. The use case highlights the heterogeneous and concise characteristics to reduce biases. An electric grid transmission system’s dynamic line rating use case example illustrates the structure.
{"title":"A Use Case Structure for Technology Integration","authors":"Ruixuan Li, T. Phillips, T. McJunkin, K. L. Blanc","doi":"10.1109/RWS55399.2022.9984036","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984036","url":null,"abstract":"In recent years, integrating innovative technologies into the work domain have reduced workload, simplified the work process, saved business costs, and generated additional revenue. Use case analysis is widely applied to identify functionalities and communicate the applicational details for technology implementations. In this study, we propose a way to frame a use case for effectively communicating across the multidisciplinary team in the design and continuous improvement phases. The use case highlights the heterogeneous and concise characteristics to reduce biases. An electric grid transmission system’s dynamic line rating use case example illustrates the structure.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984024
Michael Vázquez Nieves, Javier A. Moscoso Cabrera, Fernando Lozano-I, E. Ortiz-Rivera, R. Darbali-Zamora, C. Birk Jones
Culebra is a remote island located at the east of Puerto Rico. In September 2017, Puerto Rico including Culebra was impacted by María, a category 5 hurricane. It caused catastrophic damage, especially to the electric power distribution due to the breakage of the 38 kV submarine cable that powered the remote island. According to reports, after 6 months and hundreds of deaths, the electric service in Culebra was repaired. So, what alternatives can be explored to avoid the interruption of basic services in remote islands? As grid-forming inverter technology becomes more popular, we designed, and simulated two hybrid PV systems of 314.6 kW, and 265 kW to generate enough electricity to supply the energy demand of the island’s critical buildings using Aurora Solar software. Also, the required technologies were selected, and evaluated through a power analysis. Shadow reports were conducted to quantify the solar energy production efficiency. A battery storage system was analyzed to withstand 3 months of power outage using ReOPT software. Finally, a financial analysis was performed which breaks down an initial investment of approximately $1,581,905. Incentives, and rebates were identified to lower the initial investment. It resulted in a resilient, and feasible implementation because the break even point can be reached in approximately 3 years.
{"title":"Analysis of PV Microgrids with Storage to Improve the Resiliency of the Island of Culebra, Puerto Rico","authors":"Michael Vázquez Nieves, Javier A. Moscoso Cabrera, Fernando Lozano-I, E. Ortiz-Rivera, R. Darbali-Zamora, C. Birk Jones","doi":"10.1109/RWS55399.2022.9984024","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984024","url":null,"abstract":"Culebra is a remote island located at the east of Puerto Rico. In September 2017, Puerto Rico including Culebra was impacted by María, a category 5 hurricane. It caused catastrophic damage, especially to the electric power distribution due to the breakage of the 38 kV submarine cable that powered the remote island. According to reports, after 6 months and hundreds of deaths, the electric service in Culebra was repaired. So, what alternatives can be explored to avoid the interruption of basic services in remote islands? As grid-forming inverter technology becomes more popular, we designed, and simulated two hybrid PV systems of 314.6 kW, and 265 kW to generate enough electricity to supply the energy demand of the island’s critical buildings using Aurora Solar software. Also, the required technologies were selected, and evaluated through a power analysis. Shadow reports were conducted to quantify the solar energy production efficiency. A battery storage system was analyzed to withstand 3 months of power outage using ReOPT software. Finally, a financial analysis was performed which breaks down an initial investment of approximately $1,581,905. Incentives, and rebates were identified to lower the initial investment. It resulted in a resilient, and feasible implementation because the break even point can be reached in approximately 3 years.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133380817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/RWS55399.2022.9984035
M. Cutshaw, Rita Foster, Jedediah Haile
Modern malware analysis is stymied by dependence on the manual components of reverse engineering, which require skilled reverse engineers to perform static analysis. Machine learning and statistical analysis allow for augmentation of static analysis, detection of common benign code in malicious samples, and grouping similar bodies of low-level code. In this work four malware campaigns along with a dataset of known benign executables were utilized to test a process for grouping nearly identical functions to find similarities across executables and identify common code. In addition, those groups were collated to create sets of shared common code which could be used to better understand malware sample variants.
{"title":"Function Grouping & Visualization Through Machine Learning to Aid and Automate Reverse Engineering of Malware","authors":"M. Cutshaw, Rita Foster, Jedediah Haile","doi":"10.1109/RWS55399.2022.9984035","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984035","url":null,"abstract":"Modern malware analysis is stymied by dependence on the manual components of reverse engineering, which require skilled reverse engineers to perform static analysis. Machine learning and statistical analysis allow for augmentation of static analysis, detection of common benign code in malicious samples, and grouping similar bodies of low-level code. In this work four malware campaigns along with a dataset of known benign executables were utilized to test a process for grouping nearly identical functions to find similarities across executables and identify common code. In addition, those groups were collated to create sets of shared common code which could be used to better understand malware sample variants.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121858275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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