461089 A Stochastic Optimization Approach for Improving Power Systems Resilience through Operations and Planning
In this work, we present two-stage stochastic programming formulations for minimizing the consequence of extreme weather events to the electricity grid. While the second stage decisions are made in response to a particular event, the number and flexibility of options for recourse are limited. Therefore, the first stage decisions must be made carefully to make the system resilient to all scenarios. We utilize generator dispatch, transmission switching, and physical protection of transmission lines in the first stage, and we compare the effectiveness of each, along with combinations of the three. A linearized AC transmission model is used in the optimization, resulting in a large-scale stochastic mixed-integer linear program. The effectiveness of the approach is validated with the full nonlinear AC transmission model. The optimization problem is formulated with Pyomo, a flexible, python-based optimization modeling language. The analysis is performed on a real system with thousands of buses and transmission lines, and the scenarios used for the stochastic programming problem are generated with historical outage data.
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