16 research outputs found
Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids
The variable nature of the solar generation and the inherent uncertainty in
solar generation forecasts are two challenging issues for utility grids,
especially as the distribution grid integrated solar generation proliferates.
This paper offers to utilize microgrids as local solutions for mitigating these
negative drawbacks and helping the utility grid in hosting a higher penetration
of solar generation. A microgrid optimal scheduling model based on robust
optimization is developed to capture solar generation variability and
uncertainty. Numerical simulations on a test feeder indicate the effectiveness
of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201
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Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19
With the uncertain physical and mental health implications of COVID-19 infection, companies have taken a myriad of actions that aim to reduce the risk of employees contracting the virus, with most grounded in reducing or eliminating in-person interactions. Our preliminary analysis indicates that while there is some data to support modelling absenteeism, there are gaps in the available evidence, requiring the use of assumptions that limit precision and efficacy for decision support. Improved data on time-to-recovery after hospitalization, absenteeism due to family or other household member illness, and mental health’s impact on returning to work will support the development of more robust absenteeism models and analytical approaches
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Using analytics to support a utility's initial response to the COVID-19 pandemic amid an uncertain evidence base
Energy utilities play a critical role in fostering disaster resilience. Much of the world is increasingly dependent on the availability and reliability of safe and efficient energy. In addition to its importance for industrial, commercial and household functionality, energy provision is increasingly significant in determining health and equity outcomes during a disaster. Amid the COVID-19 pandemic, issues of workforce protection and absenteeism are critical for public safety as well as for the continuity of operations for utilities and the businesses that rely upon them. However, COVID-19, and pandemics generally, have rapidly evolving and imperfect evidence available to support rapid and real-time decision making. This article reflects the initial setup and operations of frameworks utilising analytics to support decision making from March through July 2020 for a major US electric utility. These initial strategies have enhanced decision making and provided a foundation for additional integration of the evidence base and use of analytics for anticipated decision support in the coming phases of the COVID-19 pandemic, as well as for future pandemics of unknown aetiology