Emergency Diesel Generator Reliability and Installation Energy Security

Marqusee, Jeffrey; Ericson, Sean; Jenket, Donald R.

doi:10.2172/1659892

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Only the five datasets listed in Table A-1 provide relevant data and are large enough to derive statistically meaningful results. The PREP data (25) do not include information on the number of attempted starts or run time of the generators. Thus, estimates for FTS and MTTF cannot be constructed.…”

Section: Appendices a Edgsmentioning

confidence: 99%

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Distributed Energy Resource (DER) Reliability for Backup Electric Power Systems

Marqusee¹,

Stringer²

2023

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Section: Appendices a Edgsmentioning

confidence: 99%

“…The third dataset we consider was collected in support of a Ph.D. thesis (23) supported by the U.S. Navy and previously used to estimate EDG reliability metrics ( 27) (25). The scope of the study was limited to modern, high-efficiency, low-emission generator sets from multiple manufacturers.…”

Section: Appendices a Edgsmentioning

confidence: 99%

Distributed Energy Resource (DER) Reliability for Backup Electric Power Systems

Marqusee¹,

Stringer²

2023

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“…Resilience performance comparisons are made by looking at the performance of LDES-based solutions within a microgrid to the standard of an N+1 redundant microgrid of EDGs (13). Only microgrid configurations are considered, because building-tied systems alone cannot meet DoD's requirements (14).…”

Section: Report's Purposementioning

confidence: 99%

“…Two issues cannot be treated by the current publicly available REopt tool. To conduct realistic modeling of the baseline installation at military diesel-based microgrids requires considering the reliability of EDGs (13) (14). A Markovian matrix methodology (13) and empirical data (19) were integrated into an internal version of REopt to address this issue.…”

Section: Figure 3 Reopt Modelmentioning

confidence: 99%

Long-Duration Energy Storage: Resiliency for Military Installations

Marqusee,

Olis,

et al. 2023

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“…The values used were taken from other validated studies. The values were taken from four papers [11,18,38,39]. The TIME_PEAK and TIME_OFFPEAK values represent daytime and nighttime, respectively.…”

Section: Case Studiesmentioning

confidence: 99%

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals

Silva

Brito

Araújo

et al. 2022

Sensors

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The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure’s power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital’s computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN’s operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks’ dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.

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Emergency Diesel Generator Reliability and Installation Energy Security

Abstract: Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Cited by 7 publications

References 4 publications

Distributed Energy Resource (DER) Reliability for Backup Electric Power Systems

Distributed Energy Resource (DER) Reliability for Backup Electric Power Systems

Long-Duration Energy Storage: Resiliency for Military Installations

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals

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