Recent studies have proposed using solar-plus-storage microgrids to minimize public safety concerns from power shutoffs (PSPS) during the wildfire season for communities located in wildland-urban interfaces, such as California and much of the US west coast.

A comprehensive assessment of microgrids had not been performed to evaluate the potential to enhance resilience for up to 46 million Americans living next to forests, or a wildland-urban interface, where wildfire risk is acute.

To address this research gap, a study by the Lawrence Berkeley National Laboratory looked at a novel modeling framework and assessed the potential of solar and batteries for districts where power can be turned off based on wildfire warnings.

LBNL’s modeling framework consists of:

Clustering algorithms that identify communities based on building footprint data, fire hazard severity, and renewable energy potential;

A building simulation model to quantify the energy demand;

An energy system optimization model to assist the microgrid.

LBNL defines a microgrid as a controllable and localized energy grid that could be disconnected from the regional grid and operate independently.

An optimization tool was introduced to model microgrids in forest-bordering regions, and subsequently, an assessment was performed focusing on seven localities in California with different climate conditions.