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

Michael Stadler, Zack Pecenak, Patrick Mathiesen, Kelsey Fahy and Jan Kleissl
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Michael Stadler: Bankable Energy|XENDEE Inc., 6540 Lusk Blvd, San Diego, CA 92121, 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

Energies, 2020, vol. 13, issue 17, 1-24

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.

Keywords: Microgrid; DER; planning; MILP; optimization; run-time; full time-series optimization; data reduction; DER-CAM; XENDEE (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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