Modeling and Optimization of Superstructure-based Stochastic Programs for Risk-aware Decision Support

This manuscript presents a unified software framework for modeling and optimizing large-scale engineered systems with uncertainty. We propose a Python-based "block-oriented" modeling approach for representing the discrete components within the system. Through the use of a modeling components library, the block-oriented approach facilitates a clean separation of system superstructure from the details of individual components. This approach also lends itself naturally to expressing design and operational decisions as disjunctive expressions over the component blocks. We then apply a Python-based risk and uncertainty analysis library that leverages the explicit representation of the mathematical program in Python to automatically expand the deterministic system model into a multi-stage stochastic program, which can then be solved either directly or via decomposition-based solution strategies. This manuscript demonstrates the application of this modeling approach for risk-aware analysis of an electric distribution system.