Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. An application of the methodology is presented using real event, location, and asset data.
Graph theory and nighttime imagery based microgrid design
Melvin Lugo-Alvarez, Jan Kleissl, Adil Khurram, Matthew Lave, C. Birk Jones; Graph theory and nighttime imagery based microgrid design. J. Renewable Sustainable Energy 1 May 2022; 14 (3): 036302. https://doi.org/10.1063/5.0083188
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