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Article

Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects

1
Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, USA
2
Bioenergy and Sustainable Technologies Research GmbH, 3250 Wieselburg, Austria
3
Center for Energy and Innovative Technologies (CET), 3681 Hofamt Priel, Austria
4
Center for Energy Research, University of California at San Diego, 9500 Gilman Dr., San Diego, CA 92037, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(17), 4460; https://doi.org/10.3390/en13174460
Received: 10 August 2020 / Revised: 26 August 2020 / Accepted: 28 August 2020 / Published: 28 August 2020
(This article belongs to the Special Issue Microgrids: Planning, Protection and Control)
Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings. View Full-Text
Keywords: Microgrid; DER; planning; MILP; optimization; run-time; full time-series optimization; data reduction; DER-CAM; XENDEE Microgrid; DER; planning; MILP; optimization; run-time; full time-series optimization; data reduction; DER-CAM; XENDEE
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MDPI and ACS Style

Stadler, M.; Pecenak, Z.; Mathiesen, P.; Fahy, K.; Kleissl, J. Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects. Energies 2020, 13, 4460. https://doi.org/10.3390/en13174460

AMA Style

Stadler M, Pecenak Z, Mathiesen P, Fahy K, Kleissl J. Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects. Energies. 2020; 13(17):4460. https://doi.org/10.3390/en13174460

Chicago/Turabian Style

Stadler, Michael, Zack Pecenak, Patrick Mathiesen, Kelsey Fahy, and Jan Kleissl. 2020. "Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects" Energies 13, no. 17: 4460. https://doi.org/10.3390/en13174460

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