  Industrial sensor data provides significant insights into the failure risks
of microgrid generation assets. In traditional applications, these
sensor-driven risks are used to generate alerts that initiate maintenance
actions without considering their impact on operational aspects. The focus of
this paper is to propose a framework that i) builds a seamless integration
between sensor data and operational & maintenance drivers, and ii) demonstrates
the value of this integration for improving multiple aspects of microgrid
operations. The proposed framework offers an integrated stochastic optimization
model that jointly optimizes operations and maintenance in a multi-microgrid
setting. Maintenance decisions identify optimal crew routing, opportunistic
maintenance, and repair schedules as a function of dynamically evolving
sensor-driven predictions on asset life. Operational decisions identify
commitment and generation from a fleet of distributed energy resources,
storage, load management, as well as power transactions with the main grid and
neighboring microgrids. Operational uncertainty from renewable generation,
demand, and market prices are explicitly modeled through scenarios in the
optimization model. We use the structure of the model to develop a
decomposition-based solution algorithm to ensure computational scalability. The
proposed model provides significant improvements in reliability and enhances a
range of operational outcomes, including costs, renewables, generation
availability, and resilience.
