Block-oriented modeling of superstructure optimization problems

Journal Article · Tue Oct 15 00:00:00 EDT 2013 · Computers and Chemical Engineering

DOI:https://doi.org/10.1016/j.compchemeng.2013.04.008· OSTI ID:1211023

Friedman, Z; Ingalls, J; Siirola, JD; Watson, JP

We present a novel software framework for modeling large-scale engineered systems as mathematical optimization problems. A key motivating feature in such systems is their hierarchical, highly structured topology. Existing mathematical optimization modeling environments do not facilitate the natural expression and manipulation of hierarchically structured systems. Rather, the modeler is forced to "flatten" the system description, hiding structure that may be exploited by solvers, and obfuscating the system that the modeling environment is attempting to represent. To correct this deficiency, we propose a Python-based "block-oriented" modeling approach for representing the discrete components within the system. Our approach is an extension of the Pyomo library for specifying mathematical optimization problems. 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 naturally lends itself to expressing design and operational decisions as disjunctive expressions over the component blocks. By expressing a mathematical optimization problem in a block-oriented manner, inherent structure (e.g., multiple scenarios) is preserved for potential exploitation by solvers. In particular, we show that block-structured mathematical optimization problems can be straightforwardly manipulated by decomposition-based multi-scenario algorithmic strategies, specifically in the context of the PySP stochastic programming library. We illustrate our block-oriented modeling approach using a case study drawn from the electricity grid operations domain: unit commitment with transmission switching and N - 1 reliability constraints. Finally, we demonstrate that the overhead associated with block-oriented modeling only minimally increases model instantiation times, and need not adversely impact solver behavior. (C) 2013 Elsevier Ltd. All rights reserved.

• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• DOE Contract Number: DE-AR0000214
• OSTI ID: 1211023
• Journal Information: Computers and Chemical Engineering, Vol. 57; ISSN 0098-1354
• Country of Publication: United States
• Language: English