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

Author

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  • Michael Stadler

    (Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, USA
    Bioenergy and Sustainable Technologies Research GmbH, 3250 Wieselburg, Austria
    Center for Energy and Innovative Technologies (CET), 3681 Hofamt Priel, Austria
    Center for Energy Research, University of California at San Diego, 9500 Gilman Dr., San Diego, CA 92037, USA)

  • Zack Pecenak

    (Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, USA)

  • Patrick Mathiesen

    (Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, USA)

  • Kelsey Fahy

    (Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, USA)

  • Jan Kleissl

    (Center for Energy Research, University of California at San Diego, 9500 Gilman Dr., San Diego, CA 92037, USA)

Abstract

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.

Suggested Citation

  • Michael Stadler & Zack Pecenak & Patrick Mathiesen & Kelsey Fahy & Jan Kleissl, 2020. "Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects," Energies, MDPI, vol. 13(17), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4460-:d:405727
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    References listed on IDEAS

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    Cited by:

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    6. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.

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