COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization
Metadata only
Date
2021-09Type
- Journal Article
Citations
Cited 6 times in
Web of Science
Cited 10 times in
Scopus
ETH Bibliography
yes
Altmetrics
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 component-oriented modeling and optimization for nonlinear design and operation 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. Show more
Publication status
publishedExternal links
Journal / series
Computers & Chemical EngineeringVolume
Pages / Article No.
Publisher
ElsevierSubject
Energy systems modeling; Integrated energy systems; Design and operation; Nonlinear optimizationOrganisational unit
09696 - Bardow, André / Bardow, André
More
Show all metadata
Citations
Cited 6 times in
Web of Science
Cited 10 times in
Scopus
ETH Bibliography
yes
Altmetrics