Researcher Highlight: Jordan Kern

Originally published in our October 2021 newsletter (Issue 10)


Jordan’s group is making progress on a key MSD challenge, using higher spatio-temporal and sectoral resolutions to consider how conditions, processes, and dynamics at local scales influence the interactions and failure modes of systems and sectors at higher levels of aggregation. This work contributes to better understanding how the challenges faced by grid operators could be affected by evolving drought conditions and future system configurations affected by different investments in renewable technologies.

Jordan Kern

Jordan Kern is an assistant professor at North Carolina State University in the Department of Forestry and Environmental Resources. Jordan’s group at NC State develops open source software for simulating electric power system operations under uncertainty, especially hydroclimatic extremes, and exploring system vulnerabilities from an environmental, engineering reliability, and financial/economic perspective. Jordan is a three-time graduate from the University of North Carolina at Chapel Hill (BS in Environmental Science, MS and PhD in Environmental Science and Engineering) where he was a research faculty from 2016-2018, before joining NC State. His research has been supported by multiple National Science Foundation funding programs (Innovations at the Nexus of Food, Energy, and Water Systems; Coupled Natural Human Systems) and Department of Energy funding programs (ARPA-E, Bioenergy Technologies Office, Office of Science). He is an institutional lead on the DOE Office of Science funded Integrated Multi-sector Multiscale Modeling (IM3) project lead by Pacific Northwest National Laboratory (PNNL). As part of IM3, Jordan’s team at NC State is collaborating with PNNL researchers (Nathalie Voisin, Kostas Oikonomou, Wenwei Xu) on the development of high-resolution grid operations models for each of the three major electric power interconnections in the U.S. (Western, Eastern, and the Electric Reliability Council of Texas). These models will be used to support large, integrated modeling experiments that evaluate the current and future vulnerability of the nation’s power grids to population change, technology adoption, climate change and extreme weather. This follows on several years during which Jordan’s group has been focused on the impacts of extreme weather on the grid and markets for electricity.

Power system operators meet constantly fluctuating electricity demand through coordinated operations of power plants, transmission lines, and other critical infrastructure. Even with physical redundancy built-in and emergency protocols in place, extreme weather events regularly overwhelm these measures and disrupt the tenuous balance between electricity supply and demand, resulting in outages and dramatic increases in prices in wholesale electricity markets (the institutions that oversee the production and sale of electricity in most of the U.S.). At the same time, it is increasingly accepted that the electric power sector (responsible for 27% of greenhouse gas emissions in the U.S.) must expand and decarbonize by 2050. System operators are tasked with managing the effects of growing renewable energy penetration on wholesale electricity market dynamics and physical reliability, while contending with growing exposure to drought (e.g. reduced hydropower production); extreme temperatures (e.g. spikes in heating/cooling demand); wildfire (e.g. transmission line impacts and power shutoff and flooding (i.e. prolonged outages and damaged equipment).

Jordan’s group explores the impacts of extreme weather on decarbonizing power systems by forcing grid operations models with a mixture of historical and expanded synthetic hydrometeorological data, as well as climate change projections. These data are passed through statistical and physical/engineering models that estimate spatially explicit, daily and hourly dynamics in electricity demand (load) and the availability of variable renewable energy and hydropower. The grid operations models then solve for the least cost schedule on a plant-by-plant basis and determine flows of electricity throughout the network, outputting high resolution spatial estimates of generation, emissions, and electricity prices. Jordan’s group uses these types of simulations to assess physical, environmental/public health and economic risks for power sector participants, including utilities and customers.

Image from Su et al. (2020): Above diagonal: pair plots among the two performance metrics (market prices and CO2 emissions) and five system state variables. Annual values from the stochastic simulation (colored dots) are plotted alongside annual values using historical hydrometeorology (black dots). Diagonal: distributions of power system state variables and performance metrics produced using historical (black) and synthetic (gray) hydrometeorological data. Below diagonal: 3D scatter plot for demand, California hydropower and PNW imports on an annual basis. Size of the dots correlate to the value of PNW imports. The diagonal plots are the distribution for each variable using either historical or synthetic datasets. Bottom half, color coded correlation for all variables.

Highlighted articles:

1. Wessel, J., Kern, J.D., Voisin, N., Oikonomou, K., Haas, J. (in revision). “Technology pathways could help drive the U.S. West Coast grid’s exposure to hydrometeorological uncertainty.” Earth’s Future.

2. Hill, J., Kern, J.D, Rupp, D., Voisin, N., Characklis, G. (in revision). “The Effects of Climate Change on Interregional Electricity Market Dynamics on the U.S. West Coast” Earth’s Future.

3. Su, Y., Kern, J.D., Reed, P., Characklis, G. (2020). “Compound Hydrometeorological Extremes Across Multiple Timescales Drive Volatility in California Electricity Market Prices and Emissions”. Applied Energy.

4. Kern, J.D., Su, Y., Hill, J. (2020). “A retrospective study of the 2012-2016 California drought and its impacts on the power sector.” Environmental Research Letters.

5. Su, Y., Kern, J.D., Denaro, S., Hill, J., Reed, P., Sun, Y., Cohen, J., Characklis, G. (2020). “An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes”. Environmental Modelling and Software. Vol. 126.

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