Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint

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This paper compares several electrical load models for estimating the efficiency of DC vs. AC distribution in microgrids. Candidate models include energy balance, harmonic power flow, and time-domain modeling. Model results are compared with numerical studies and validated with experimental measurements. Based on quantitative and qualitative considerations, the most appropriate load modeling approach for larger-scale DC distribution efficiency studies is proposed.

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TY - DATA AB - This paper compares several electrical load models for estimating the efficiency of DC vs. AC distribution in microgrids. Candidate models include energy balance, harmonic power flow, and time-domain modeling. Model results are compared with numerical studies and validated with experimental measurements. Based on quantitative and qualitative considerations, the most appropriate load modeling approach for larger-scale DC distribution efficiency studies is proposed. AU - Santos, Arthur A2 - Cale, James A3 - Othee, Avpreet A4 - Gerber, Daniel A5 - Frank, Stephen A6 - Duggan, Gerald A7 - Zimmerle, Dan A8 - Brown, Rich DB - C-MIX - Community Microgrid Information Exchange DP - Open EI | National Laboratory of the Rockies DO - KW - Solar KW - Photovoltaics KW - PV KW - Battery energy storage KW - Diesel generators KW - Other liquid-fuel generators KW - Wind energy KW - Case studies KW - Performance KW - Local energy resources (LER) LA - English DA - 2020/09/01 PY - 2020 PB - Colorado State University T1 - Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint UR - https://cmix.openei.org/submissions/1 ER -
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Santos, Arthur, et al. Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint. Colorado State University, 1 September, 2020, C-MIX - Community Microgrid Information Exchange. https://cmix.openei.org/submissions/1.
Santos, A., Cale, J., Othee, A., Gerber, D., Frank, S., Duggan, G., Zimmerle, D., & Brown, R. (2020). Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint. [Data set]. C-MIX - Community Microgrid Information Exchange. Colorado State University. https://cmix.openei.org/submissions/1
Santos, Arthur, James Cale, Avpreet Othee, Daniel Gerber, Stephen Frank, Gerald Duggan, Dan Zimmerle, and Rich Brown. Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint. Colorado State University, September, 1, 2020. Distributed by C-MIX - Community Microgrid Information Exchange. https://cmix.openei.org/submissions/1
@misc{CMIX_Dataset_1, title = {Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint}, author = {Santos, Arthur and Cale, James and Othee, Avpreet and Gerber, Daniel and Frank, Stephen and Duggan, Gerald and Zimmerle, Dan and Brown, Rich}, abstractNote = {This paper compares several electrical load models for estimating the efficiency of DC vs. AC distribution in microgrids. Candidate models include energy balance, harmonic power flow, and time-domain modeling. Model results are compared with numerical studies and validated with experimental measurements. Based on quantitative and qualitative considerations, the most appropriate load modeling approach for larger-scale DC distribution efficiency studies is proposed.}, url = {https://cmix.openei.org/submissions/1}, year = {2020}, howpublished = {C-MIX - Community Microgrid Information Exchange, Colorado State University, https://cmix.openei.org/submissions/1}, note = {Accessed: 2026-06-18} }

Details

Data from Sep 1, 2020

Last updated Mar 30, 2026

Submitted Jun 2, 2026

Organization

Colorado State University

Contact

Arthur Santos

Authors

Arthur Santos

Colorado State University

James Cale

Colorado State University

Avpreet Othee

Colorado State University

Daniel Gerber

Lawrence Berkeley National Laboratory

Stephen Frank

NLR

Gerald Duggan

Colorado State University

Dan Zimmerle

Colorado State University

Rich Brown

Lawrence Berkeley National Laboratory
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