A5038162651- Integrated Energy Systems Optimization
- Water-Energy-Food Nexus Studies
- Social Acceptance of Renewable Energy
- Energy and Environment Impacts
- Climate variability and models
- demographic modeling and climate adaptation
- Power System Reliability and Maintenance
- Flood Risk Assessment and Management
- Smart Grid and Power Systems
- Breastfeeding Practices and Influences
- Hydrology and Drought Analysis
- Hydropower, Displacement, Environmental Impact
- Energy Load and Power Forecasting
- Solar Radiation and Photovoltaics
- Water Governance and Infrastructure
- Mining and Resource Management
- Electric Power System Optimization
- Simulation Techniques and Applications
- Building Energy and Comfort Optimization
- Meteorological Phenomena and Simulations
- Energy Efficiency and Management
- Nutrition, Health and Food Behavior
University of North Carolina Health Care
2024
University of North Carolina at Chapel Hill
2023-2024
Columbia University
2018-2023
Environmental Earth Sciences
2023
Abstract While most electricity systems are designed to handle peak demand during summer months, long-term energy pathways consistent with deep decarbonization generally electrify building heating, thus increasing winter. A key question is how climate variability and change will affect heating cooling in an electrified future. We conduct a spatially explicit analysis of trends temperature-based proxies over the past 70 years. Average annual for (cooling) decreases (increases) contiguous US....
Variable hydrometeorological conditions can impact electric utilities' financial stability. Extreme temperatures often increase electricity demand, raising utility costs, while drought reduces hydropower generation and revenues, with impacts potentially exacerbated by spikes in fuel prices, particularly natural gas. In this study, a model of the U.S. West Coast power system is combined risk large California as it responds to variable hydrometeorology market conditions, used test performance...
We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind solar) can simulate synthetic realizations of arbitrary length. illustrate how this statistical simulation tool be used context regional power system planning under scenario high reliance on solar generation when long historical records potential are not available. show our model to assess probability distribution severity...
Climate variability influences renewable electricity supply and demand hence system reliability. Using the hidden states of sea surface temperature tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics is objectively identified by a nonhomogeneous Markov model, we provide first example potential predictability monthly wind solar energy heating cooling for 1 to 6 months ahead Texas, United States, region has high penetration susceptible disruption climate-driven...
The potential for extreme climate events to cluster in space and time has driven increased interest understanding predicting compound risks. Through a case study on floods the Ohio River Basin, we demonstrated that low-frequency variability could drive spatial temporal clustering of risk regional extremes. Long records annual maximum streamflow from 24 USGS gauges were used explore spatiotemporal patterns flooding their associated large-scale modes. We found dominant scales flood this basin...
While most electricity systems are designed to handle peak demand during summer months, pathways deep decarbonization generally electrify building heating, thus increasing winter. A key question is how climate variability and change will affect heating cooling in an electrified future. We conduct a spatially explicit analysis of trends temperature-based proxies over the past 70 years. Average annual for (cooling) decreases (increases) contiguous US. However, while drives robust increases...
Spatially distributed renewable energy generation poses unique risks to power systems since the aggregate amount of produced in any hour depends on spatial correlation structure sources. Moreover, can vary with time day and season depend state large-scale climate. These features pose a challenge for resource adequacy risk assessment using traditional statistical or machine learning methods. A new algorithm based spatially clustered k-nearest neighbors capture spatio-temporal dynamics wind...