About

Jordan Kern is an Associate Professor at NC State University's Edward P. Fitts Department of Industrial and Systems Engineering. His research focuses on advancing the optimal design and management of energy systems, with particular emphasis on building high-fidelity models of bulk electric power systems, simulating system dynamics under uncertainty and stress such as extreme weather and physical attacks, and informing decision-making around capital investment and short-term operations to meet cost and reliability objectives. Kern's work aims to support real-world decision-making through collaboration with stakeholders like electric power utilities. Since joining NC State, Kern's group has received funding from prominent agencies including the U.S. Department of Energy, the National Science Foundation, the Alfred P. Sloan Foundation, the Bezos Earth Fund, and the State of North Carolina. In 2022, he was awarded the NSF CAREER award for his research on navigating the dual challenges of extreme weather and decarbonization for the electric power grid. He has served as an expert witness for the U.S. House of Representatives Committee on Energy and Commerce and contributed to a White House Office of Science and Technology Policy panel on artificial intelligence in energy systems. Currently, he serves on a National Academies of Sciences, Engineering, and Medicine expert committee advising the Department of Energy on regional energy-water demonstration programs. Kern's research has been featured in various national media outlets, including NPR, the LA Times, TIME, Vox, The Hill, Wired, and The Guardian, highlighting his contributions to energy systems and resilience.

Research topics

• Computer Science
• Economics
• Geography
• Engineering
• Meteorology
• Environmental science
• Risk analysis (engineering)
• Agricultural economics
• Ecology
• Operations research
• Management science
• Systems engineering
• Business
• Econometrics
• Telecommunications
• Climatology
• Natural resource economics

Selected publications

• Capturing Dynamic Hydropower Performance in Long-Term Energy Planning

  2026-03-14

  articleOpen access

  Hydropower is a reliable renewable energy source that plays a central role in the water–energy nexus and in integrating emerging energy loads and generation technologies. Hydropower plants (HPs) are designed to operate efficiently within defined ranges of reservoir releases and water levels. Deviations from these conditions, driven by changes in water availability, regulatory constraints, and evolving energy grid demands, reduce operational efficiency. As a result, less energy may be produced per unit of water in a future where hydrology and water operations are meaningfully different from what the HPs were designed for.However, current state of the art large-scale, long-term energy planning models assume constant, near-optimal turbine efficiency or even constant hydraulic head, ignoring variability in HP efficiency and losses. These simplifications lead to systematic overestimation in our future projections of hydropower generation and capacity. They can also lead to underestimation of future resource adequacy needs, and consequent underinvestment in complementary energy infrastructure, increasing risks to future grid reliability. Models and approaches that provide more accurate, temporally and regionally resolved assessments of hydropower potential are therefore needed to support informed planning decisions.Here we introduce HEADFIT (Hydraulic-Energy Analysis and Dynamic Fitting), a physics-informed framework for analyzing and calibrating hydropower system performance in long-term water–energy planning models. HEADFIT integrates plant hydraulics, including frictional and minor head losses, tailwater dynamics, and operational limits, with turbine efficiency curves for a high-fidelity estimation of how hydropower generation is expected to change in a changing climate. These relationships are approximated at the plant level using physics-informed relationships calibrated with observed hydrological and operational data. The calibrated plant-level models are then used to project hydropower generation under future hydrological scenarios. Lastly, we employ a Western United States power system model to propagate refined hydropower projections into more accurate grid performance assessments across time scales.Preliminary analysis for 15 major hydropower plants across the Colorado and Columbia River basins shows that constant-efficiency assumptions overestimate annual hydropower production by an average of five percent, with larger biases during periods of high releases combined with low reservoir levels. These discrepancies reduce the accuracy of capacity and flexibility estimates that support essential grid services. They can also misguide investment and design decisions, increasing risks to grid reliability as climate and demand variability intensify.

  PublisherDOI

• The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

  Energy Reports · 2026-03-12

  articleOpen accessCorresponding

  Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity. • Water availability and hydropower scheduling decisions influence electricity prices. • Hydropower scheduling causes larger price forecast errors than low hydrologic forecast quality. • Effects within the Western U.S. vary by season, hydrologic conditions, and subregion. • Pacific Northwest price forecast errors are most influenced by hydropower scheduling. • In hydro-dominant markets, optimal hydropower scheduling should anticipate related price effects.

  PublisherDOI

• ATLAS: Assessment of exTreme event impacts on LArge-scale power systems via Synthetic augmentation

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Integrating Refinery Placement, Storage Optimization, and Transportation Logistics for Resilient, Low-Carbon Biofuel Supply Chains in the U.S. Corn Belt

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• IM3 Projected U.S. Western Interconnection Grid Stress Dataset

  OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2025-01-01

  articleOpen access

  This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.

  PublisherDOI

• Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

  Biomass and Bioenergy · 2025-01-09 · 9 citations

  articleOpen access

  As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield. • Explore tradeoffs between cost, GHG emissions, and supply risk in biofuel production. • Results show that corn/soy minimize costs and switchgrass minimizes GHG emissions. • Crop choice impacts objectives more than strategic siting decisions. • High production quota restricts decision space and reduce differences among solutions. • Results show the need for further exploration of weather and supply chain logistic.

  PublisherDOI

• A composite index-based insurance instrument for managing the financial risk of variable hydrometeorology for electric utilities

  2025-02-27

  preprintOpen access

  Variable hydrometeorological conditions can impact electric utilities' financial stability. Extreme temperatures often increase electricity demand, raising utility costs, while drought reduces hydropower generation and often reduces revenues, with financial 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 with a financial risk model of a large California electric utility as it responds to variable hydrometeorology and market conditions, and is used to test the performance of a novel financial tool for managing risk. An insurance contract based on a composite index of measures related to streamflow, temperature, and natural gas prices is developed and its cost-effectiveness is compared against a portfolio of three currently available index contracts each based on a single index. The new composite index contract achieves an equivalent reduction in the utility’s net revenue variance as a portfolio of the three existing contract types for roughly half the cost with the cost reduction largely attributable to lower basis risk associated with the composite index contract. The utility's financial risk and the effectiveness of the new contract are also explored under an alternative regulatory scenario involving a pollution tax intended to reduce air pollution damages and emissions. Overall, this case study represents a new approach to managing financial risk arising from hydrometeorological and market variability for vertically integrated utilities, the most common utility structure.

  PublisherOA PDFDOI

• The impacts of residential space heating electrification and climate change on variable renewable energy capacity credits in Texas

  Environmental Research Energy · 2025-11-24

  articleOpen accessCorresponding

  Abstract The capacity credit of a variable renewable energy (VRE) resource reflects its ability to contribute to meeting peak demand or provide generation during critical peak demand periods. Accurate estimation of these capacity credits can help inform system planners about how much dispatchable capacity can be retired without a reduction in reliability. Previous research has explored marginal changes in the capacity credit of wind and solar as a function of new capacity added to the system (i.e. renewable energy ‘eats it own lunch’), but the impacts of potential future changes in load profiles-driven by technology adoption and/or climate change has not been thoroughly explored. This study investigates how the capacity credit of VRE resources could be affected by space heating electrification (widespread adoption of heat pumps (HPs)) and climate change (increasing summer temperatures), focusing on the electric reliability corporation of Texas (ERCOT) as a case study. As a proxy for capacity credit, we measure the average capacity factor during peak and near-peak load hours. We explore multiple HP adoption scenarios and climate change scenarios over an 80 year span (2020–2099). One of our findings is that cooler climate scenarios, combined with the adoption of standard-efficiency HPs, leads to an increased frequency of peak load hours occurring during winter nights, during which solar resources have nearly zero capacity factors. This raises questions about how parallel decarbonization efforts (e.g. electrification) could influence the capacity value of VRE resources and, consequently, the reliability of different decarbonization pathways.

  PublisherDOI

• The Relative Influences of Hydrologic Information and Dams’ Hydropower Scheduling Decisions on Electricity Price Forecasts

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• U.S. West Coast droughts and heat waves exacerbate pollution inequality and can evade emission control policies

  UNC Libraries · 2025-04-04

  articleOpen access
  PublisherDOI

Frequent coauthors

• Nathalie Voisin

  Pacific Northwest National Laboratory

  40 shared

• Gregory W. Characklis

  37 shared

• Joy Hill

  17 shared

• Yufei Su

  15 shared

• Konstantinos Oikonomou

  Pacific Northwest National Laboratory

  14 shared

• Kerem Ziya Akdemir

  North Carolina State University

  14 shared

• Jannik Haas

  12 shared

• David E. Rupp

  10 shared

Education

• Doctor of Philosophy, Department of Environmental Sciences and Engineering

  University of North Carolina at Chapel Hill

  2014

• Masters of Science, Department of Environmental Sciences and Engineering

  University of North Carolina at Chapel Hill

  2010

• Bachelor of Science, Environmental Science

  University of North Carolina at Chapel Hill

  2007

Awards & honors

• NSF CAREER Award (2022)