I developed a cost-focused two-stage stochastic optimization model for planning and scheduling renewable energy microgrids over a 20-year horizon, with system expansion modeled in year 10. The model accounts for outages ranging from 10 hours to one week and incorporates annual increases in load demand, providing a realistic framework for long-term microgrid planning under uncertainty.  This work was conducted as part of a U.S. Department of Energy, Solar Energy Technologies Office funded project. 

The problem is solved using Benders Decomposition integrated with Branch and Bound, enabling efficient handling of a large number of stochastic scenarios. Extensive numerical and sensitivity analyses were conducted to generate actionable insights for utilities and policymakers, supporting resilient and cost-effective microgrid investment and operation strategies.

The corresponding paper is under publication process.
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