COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization. (September 2021)

• Record Type: Journal Article
• Authors: Langiu, Marco; Shu, David Yang; Baader, Florian Joseph; Hering, Dominik; Bau, Uwe; Xhonneux, André; Müller, Dirk; Bardow, André; Mitsos, Alexander; Dahmen, Manuel
• Is Part Of: Computers & chemical engineering. Volume 152(2021)
• Publication Date: 2021-09
• Subjects: Energy systems modeling -- Integrated energy systems -- Design and operation -- Nonlinear optimization; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107366
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Open-source framework for optimization of energy systems design and operation Component-oriented modeling, allowing for hybrid mechanistic/data-driven models Optimization considering nonlinearity, dynamics and parametric uncertainty Four case studies, demonstrating flexibility and wide range of application Graphical abstract: Abstract: Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack granularity for technical system design and operation. We present COMANDO, an open-source Python package for c omponent-o riented m odeling and optimiza tion for n onlinear d esign and o peration of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks (ANNs) as surrogate models in a reduced-space formulation for deterministic global optimization.