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| Hauptseite > Publikationsdatenbank > COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization > print |
| Hauptseite > Publikationsdatenbank > COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization > print |
| 001 | 891106 | ||
| 005 | 20240712112903.0 | ||
| 024 | 7 | _ | |a 0022-7722 |2 ISSN |
| 024 | 7 | _ | |a 1447-073X |2 ISSN |
| 024 | 7 | _ | |a 1447-6959 |2 ISSN |
| 024 | 7 | _ | |a arXiv:2102.02057 |2 arXiv |
| 024 | 7 | _ | |a 2128/27422 |2 Handle |
| 024 | 7 | _ | |a altmetric:99365031 |2 altmetric |
| 037 | _ | _ | |a FZJ-2021-01369 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Langiu, Marco |0 P:(DE-Juel1)165324 |b 0 |u fzj |
| 245 | _ | _ | |a COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1615892537_15226 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 500 | _ | _ | |a 28 pages, 1 graphical abstract, 13 figures |
| 520 | _ | _ | |a Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom 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 as surrogate models in a reduced-space formulation for deterministic global optimization. |
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| 773 | _ | _ | |0 PERI:(DE-600)2077837-5 |t Anatomical science international |x - |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/891106/files/2102.02057.pdf |y OpenAccess |
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