A5010549500- Infrastructure Resilience and Vulnerability Analysis
- Power System Reliability and Maintenance
- Energy Load and Power Forecasting
- Electric Power System Optimization
- Energy and Environment Impacts
- Tropical and Extratropical Cyclones Research
- Atmospheric chemistry and aerosols
- Atmospheric aerosols and clouds
- COVID-19 epidemiological studies
- Smart Grid Security and Resilience
- Meteorological Phenomena and Simulations
- Solar Radiation and Photovoltaics
- Wind Energy Research and Development
- Wind and Air Flow Studies
- Optimization and Search Problems
- Air Quality Monitoring and Forecasting
- Optimal Power Flow Distribution
- Fire effects on ecosystems
- Integrated Energy Systems Optimization
- Risk and Safety Analysis
- Satellite Communication Systems
- Energy Efficient Wireless Sensor Networks
- Vehicle Routing Optimization Methods
- Social Acceptance of Renewable Energy
- Data-Driven Disease Surveillance
Sandia National Laboratories
2016-2024
Electric Power Research Institute
2022-2024
Sandia National Laboratories California
2016-2021
ORCID
2021
Johns Hopkins University
2013-2015
University of Baltimore
2014
The evolving nature of electricity production, transmission, and consumption necessitates an update to the IEEE's Reliability Test System (RTS), which was last modernized in 1996. presented here introduces a generation mix more representative modern power systems, with removal several nuclear oil-generating units addition natural gas, wind, solar photovoltaics, concentrating power, energy storage. includes assigning test system geographic location southwestern United States enable...
Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These have significant impacts on other infrastructure dependent electric population living in impacted area. Efficient effective emergency response planning within utilities, utilities power, private companies, local, state, federal government agencies benefit from accurate estimates of extent spatial distribution advance an approaching hurricane. A number models been developed for predicting a...
Abstract Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling ongoing SARS-CoV-2 pandemic. We estimated weekly values of effective basic reproductive number (R eff ) using a mechanistic metapopulation model and associated these with county-level characteristics NPIs in United States (US). Interventions that included school leisure activities closure nursing home visiting bans were all median R below 1 when combined either stay at orders (median 0.97,...
Abstract Forecasts of available wind power are critical in key electric systems operations planning problems, including economic dispatch and unit commitment. Such forecasts necessarily uncertain, limiting the reliability cost‐effectiveness models based on a single deterministic or “point” forecast. A common approach to address this limitation involves use number probabilistic scenarios, each specifying possible trajectory production, with associated probability. We present analyze novel...
Abstract Nine in ten major outages the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts hurricane frequency and intensity; yet projections both remain highly uncertain. However, models do not account for epistemic uncertainties physics-based under change, largely due to extreme computational complexity. Instead they use simple probabilistic assumptions model such uncertainties. Here, we propose transparent efficient framework to, first...
Abstract While power outages caused by tropical cyclones (TCs) already pose a great threat to coastal communities, how—and why—these risks will change in warming climate is poorly understood. To address this need, we develop robust machine learning model capture TC-induced outage risk. When applied 900 000 synthetic TCs downscaled from simulated historical and future conditions under strong scenario, find risk the United States Puerto Rico expected increase broadly end of century, with some...
Stochastic versions of the unit commitment problem have been advocated for addressing uncertainty presented by high levels wind power penetration. However, little work has done to study trade-offs between computational complexity and quality solutions obtained as number probabilistic scenarios is varied. Here, we describe extensive experiments using real publicly available data from Bonneville Power Administration. Solution measured re-enacting day-ahead reliability (which selects thermal...
Wind power is becoming an increasingly important part of the global energy portfolio, and there growing interest in developing offshore wind farms United States to better utilize this resource. have certain environmental benefits, notably near‐zero emissions greenhouse gases, particulates, other contaminants concern. However, are significant challenges ahead achieving large‐scale integration States, particularly wind. Environmental impacts from a concern, these subject number on‐going...
Power system utilities continue to strive for increased resiliency. However, quantifying a baseline resilience, and deciding the optimal investments improve their resilience is challenging. This paper discusses method create scenarios, based on historical data, that represent threats of severe weather events, probability occurrence, wide consequences they generate. also presents mixed-integer stochastic nonlinear optimization model which uses scenarios as an input determine reduce impacts...
Abstract High wind speeds can pose a great risk to structures and operations conducted in offshore environments. When forecasting speeds, most models focus on the average over given period, but this value alone represents only small part of true conditions. We present statistical predict full distribution maximum‐value 3 h interval. take detailed look at performance linear models, generalized additive multivariate adaptive regression splines using meteorological covariates such as gust...
Smoke from wildfires results in air pollution that can impact the performance of solar photovoltaic plants. Production is impacted by factors including proximity fire to a site interest, extent wildfire, wind direction, and ambient weather conditions. We construct model quantifies relationships among weather, wildfire-induced pollution, PV production for utility-scale distributed generation sites located western United States. The regression identified 9.4%-37.8% reduction on smokey days....
Abstract Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze challenges in estimation for onshore farms. Turbine wake effects strong determinant of farm production. With given input conditions, losses typically cause downstream turbines produce significantly less than upstream...
We present scalable stochastic optimization approaches for improving power systems' resilience to extreme weather events. consider both proactive redispatch and transmission line hardening as alternatives mitigating expected load shed due weather, resulting in large-scale linear programs (LPs) mixed-integer (MILPs). solve these problems with progressive hedging (PH), a parallel, scenario-based decomposition algorithm. Our computational experiments indicate that our proposed method enhancing...
Abstract Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling ongoing SARS-CoV-2 pandemic. We estimated weekly values of effective basic reproductive number ( R eff ) using a mechanistic metapopulation model and associated these with county-level characteristics NPIs in United States (US). Interventions that included school leisure activities closure nursing home visiting bans were all an below 1 when combined either stay at orders (median 0.97, 95%...
Recently, the concept of black swans has gained increased attention in fields risk assessment and management. Different types have been suggested, distinguishing between unknown unknowns (nothing past can convincingly point to its occurrence), knowns (known some, but not relevant analysts), or known where probability occurrence is judged as negligible. Traditional assessments questioned, their standard probabilistic methods may be capable predicting even identifying these rare extreme...
Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes interest complex problems. Often, these computationally and time consuming run. This latter point may be at odds with time‐sensitive evaluations or limit number parameters that considered. In this article, we give an introductory tutorial focused parallelizing simulation code better leverage modern computing hardware, enabling analysts utilize simulation‐based methods for quantifying...