Projects & Grants
Microgrids and Energy Access
Recent research in microgrids and energy access in the Cale research group has included modeling, simulation and validation of microgrid generation, control technologies, and loads. Funded research has included:
Energy Access: modeling and characterization of highly-inductive loads found in rural minigrids, evaluation of solar pump efficiency, and development of a microgrid controller testbed. This work included at-power evaluation at the CSU Powerhouse Energy Campus (see publications below).
Hybrid Microgrids: research in hybrid microgrids has included the assessment of power quality metrics and emissions for hybrid mobile microgrid generators, which include a combination of battery energy storage with natural gas generators under uncertain loads.
In addition, Cale’s group is developing the Rapid Microgrid Control Development Testbed (RMCDT), a bench-top hardware platform for emulating three-phase microgrids in both grid-connected and islanded mode. The RMCDT includes power converters for controllable grid simulation, solar, energy storage, electrical motors, and controllable loads [more details soon].
Selected Publications:
J. Cale, C. Lute, J. Simon and A. Delcore, “Modeling Minimally-Processed Shielded Metal Arc Weld Transformers for Rural Minigrid Applications,” IEEE Power and Energy Technology Systems Journal, vol. 6, no. 2, pp. 95-103, June 2019.
C. Lute, T. Decker, J. Cale and A. Delcore, “A Method for Evaluating Unregulated Solar Irrigation Pumping Systems: Results and Observations,” 2019 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 2019, pp. 1-8.
C. Lute, J. Cale, and D. Moorman, “Battery electric-natural gas hybrid generator for microgrid power applications,” in International Symposium on Microgrids, 2019 (poster), Fort Collins, CO, 2019.
J. Cale, C. Lute, G. Ross, and A. Othee, “Characterization procedure for unsymmetrical split-phase capacitor-start induction machines,” IEEE Power and Energy Technology Systems Journal, 2020, (in press).
Power Electronics and Controls
[New] Recent research in power electronics and controls includes the development of a mathematical framework and modeling toolkit for evaluating the potential advantages of DC distribution in buildings with a high proportion of power-electronic devices (e.g., plug-loads). This work, funded by the DOE BENEFIT program, has resulted in a new open-source software toolkit, Building Energy Efficiency Analysis Model (BEEAM).
[New] Cale’s research group is currently working on a funded project to model and assess new electrical drive architectures for implementing all-electric and hybrid electric thrust reverse actuation systems (TRAS) for aerospace applications. This research is in partnership with Woodward and the Institut National des Sciences Appliquées (INSA) in Toulouse, France. As part of this project, the new Woodward HIL laboratory is being constructed at the Powerhouse [more details soon].
Cale also received internally-competitive research funding to develop a novel feedback control strategy for mitigating communication delays in remote hardware-in-loop (HIL) systems. This methodology was verified experimentally between two systems running at CSU and NREL (approximately 100 km away).
Selected Publications:
Cale, J., S. Frank, D. Zimmerle, Santos, A., D. Gerber, Othee, A., and G. Duggan, “Comparison of load models for estimating electrical efficiency in DC microgrids,” in Proceedings of the 3rd IEEE International Conference on DC Microgrids, 2019, Matsue, Japan (in press).
Cale, J., E. Dall’Anese, B. Johnson, P. Young, G. Duggan, Bedge, P., D. Zimmerle, and Holton, L., “Mitigating communication delays in remotely connected hardware-in-the-loop experiments,” IEEE Transactions on Industrial Electronics, vol. 65, no. 12, pp. 9739–9748, 2018.
Magnetics
Recent funded research in magnetics includes a computational and experimental procedure for in situ characterization of nonlinear magnetization in transformers and parameter extraction of electrically non-symmetric and unbalanced capacitor-start induction machines.
[New] Cale is also developing a magnetic material characterization laboratory at Powerhouse. He currently (2020) serves as faculty of a senior ECE undergraduate project to develop a testbed and graphical user interface for automated extraction of the nonlinear permeability and frequency-dependent coercivity of ferromagnetic materials. More about the project can be found here.
Selected Publications:
J. Cale, C. Lute, J. Simon and A. Delcore, “Modeling Minimally-Processed Shielded Metal Arc Weld Transformers for Rural Minigrid Applications,” IEEE Power and Energy Technology Systems Journal, vol. 6, no. 2, pp. 95-103, June 2019.
J. Cale, C. Lute, G. Ross, and A. Othee, “Characterization procedure for unsymmetrical split-phase capacitor-start induction machines,” IEEE Power and Energy Technology Systems Journal, 2020, (in press).
Machine Learning
Recent research in machine learning has included a pattern recognition approach for enhancing the reliability and lifecycle maintainability of battery energy storage systems from a fleet perspective. This research includes the synthesis of numerical classification technology with finite queuing system optimization.
Cale developed a computational approach for applying principal components analysis (PCA) and k-mediod clustering for classifying types of distribution feeders on an electrical distribution system; this research was funded under the DOE SunShot program.
Selected Publications:
Pirani, R. and Cale, J., “A pattern recognition approach for enhancing lifecycle maintain- ability of battery systems,” in 2019 International Symposium on Systems Engineering (ISSE), 2019, Edinburgh, Scotland, 2019, pp. 1–8.
Cale, J., B. Palmintier, D. Narang, and K. Carroll, “Clustering distribution feeders in the Arizona Public Service territory,” in Proceedings of the 40th IEEE Photovoltaic Specialist Conference (PVSC), 2014, Denver, Colorado (USA), 2014, pp. 2076–2081.
Stochastic Variability Modeling and Optimization
[New] Recent funded research includes modeling and characterization of stochastic load variability found in modern buildings, which will ultimately be used to model and enhance energy flexibility.
[New] Cale’s group is currently developing a new stochastic optimization approach for risk-constrained optimization for wind-based microgrids [more details soon].