Dr. Schell’s research develops mathematical models that inform the energy transition. She works at both the generation level, improving wind energy resource assessment and forecasting, and at the systems level, designing adaptable microgrids that can respond to future climate events. Methodologically, she has contributed to advancing spatio-temporal statistical forecasting models, deep learning architectures and energy system network optimization. These models are informed by large, empirical data from wind power producers, electric utilities, independent system operators and numerical weather prediction models. Her doctoral degrees are from the Engineering and Public Policy programs at Carnegie Mellon University and the Faculty of Engineering of the University of Porto, Portugal. She holds a Master’s of Science in Environmental Engineering from Johns Hopkins University, and a Bachelor of Science degree in Chemical Engineering from Carnegie Mellon University, with an additional major in Engineering and Public Policy.

Dr. Schell is accepting new PhD students interested in optimization modeling, statistical, machine and deep learning modeling and forecasting problems.

Contact Information: 

Email: schelk@rpi.edu

Focus Area: 

Large scale optimization, applied statistics and machine learning, power system modeling, renewable energy forecasting, electricity markets, energy policy, decision theory, risk analysis

Selected Publications: 

• Yang, H., Schell, K.R. ATTnet: An Explainable Gated Recurrent Unit Neural Network for
  High Frequency Electricity Price Forecasting. International Journal of Electrical Power and Energy
  Systems. April 2024. https://doi.org/10.1016/j.ijepes.2024.10997
• Arrieta-Prieto, M. and Schell, K.R. Spatially transferable machine learning wind power prediction models: logit random forests. Renewable Energy. March 2024. https://doi.org/10.1016/j.renene.2024.120066
• Arwa, E. and Schell, K.R. Batteries or Silos: Optimizing Storage Capacity in Direct Air Capture
  Plants to Maximize Renewable Energy Use. Applied Energy. February 2024. https://doi.org/10.1016/j.apenergy.2023.122345
• Owusu-Obeng, P.Y. and Schell, K.R. Bi-level goal programming model for simultaneous microgrid
  investment and tariff design: A Case Study in the Volta Region of Ghana. Renewable Energy Focus.
  December 2023. https://doi.org/10.1016/j.ref.2023.100511
• Arrieta-Prieto, M. and Schell, K.R. Data driven optimization of Wind Farm Siting. International
  Journal of Electrical Power and Energy Systems. September 2023.
  https://doi.org/10.1016/j.ijepes.2023.109552