Digital Process Engineering of Sustainable Power2X Processes

Modeling, simulation, and optimization are important methodological pillars of digital engineering and have triggered a revolution in the way we approach things. Spearheaded by applications in mechanical engineering and logistics, also chemical engineering and information technologogy have been witnessing an explosion of computational approaches. This trend is further accelerated by availability of data, open source software, hardware improvements, and interdisciplinary approaches and cooperations. At the same time, we have been entering a transition phase towards a sustainable energy policy using several fluctuating energy sources.

Electricity load in Germany for two exemplary weeks, showing the impact of renewable energy.

Electricity load in Germany for two exemplary weeks, showing the impact of renewable energy.

Power2X is the name for a number of electricity conversion, energy storage, and reconversion pathways that use surplus electric power, typically during periods where fluctuating renewable energy generation exceeds load, as indicated in the example illustration above.

It is important to do this conversion efficiently, as massive installations and import of renewable energies are necessary to reduce CO2 emissions. Here E-Fuels (H2, CH4, CH3OH, OME) made from renewable energy and CO2 are an attractive option. The flexible design and operation of Power2X processes by optimization and optimal control can give insight into the most promising solutions.

However, there are many challenges to a computational approach. Usually, there are many different units / processes involved, often modeled via complicated partial differential equation (PDE) models and involving still unknown/unmodeled mechanisms. In addition there are uncertainties in the fluctuating inputs (electricity, products of biogas plants) and demand imposing intelligent storage solutions. The design of novel processes is impaired by many combinatorial choices.

The following figure from the paper CO2 Methanation Process Synthesis by Superstructure Optimization shows an optimal Power2Methane choice for particular assumptions on available processes in a preimposed order (one choice per column possible) and particular purity constraints and objective function specifications. It was calculated based on steady-state assumptions with an optimization-driven software solution developed in my group. An extension to transient processes and a clever interaction between detailed PDE models and underestimating surrogate models is ongoing work.

Discrete choices in Power2Methane.

Discrete choices in Power2Methane.

We have been working on storage capacities in electricity grids and on the optimal design and control of Power2X processes, with a focus on Methane and Methanol as energy carriers.

Selected publications



AuthorTitleYearJournal/ProceedingsReftypeLink
Huschto, T., Podolskij, M., Sager, S. The asymptotic error of chaos expansion approximations for stochastic differential equations 2019 Modern Stochastics: Theory and Applications   article DOI
 
BibTeX:
@article{Huschto2019,
    author = {Huschto, T. and Podolskij, M. and Sager, S.},
    title = {The asymptotic error of chaos expansion approximations for stochastic differential equations},
    journal = {Modern {S}tochastics: {T}heory and {A}pplications},
    year = {2019},
    volume = {6},
    number = {2},
    pages = {145--165},
    doi = {10.15559/19-VMSTA133}
}
Jost, F., Sager, S., Le, T. A Feedback Optimal Control Algorithm with Optimal Measurement Time Points 2017 Processes   article
url  
BibTeX:
@article{Jost2017,
    author = {Jost, F. and Sager, S. and Le, T.T.T.},
    title = {A Feedback Optimal Control Algorithm with Optimal Measurement Time Points},
    journal = {Processes},
    year = {2017},
    volume = {5},
    number = {10},
    pages = {1--19},
    url = {http://www.mdpi.com/2227-9717/5/1/10}
}
Matke, C., Bienstock, D., Munoz, G., Yang, S., Kleinhans, D., Sager, S. Robust optimization of power network operation: storage devices and the role of forecast errors in renewable energies 2017 Studies in Computational Intelligence: Complex Networks and Their Applications V   inproceedings DOI
 
BibTeX:
@inproceedings{Matke2017,
    author = {Matke, C. and Bienstock, D. and Munoz, G. and Yang, S. and Kleinhans, D. and Sager, S.},
    title = {Robust optimization of power network operation: storage devices and the role of forecast errors in renewable energies},
    booktitle = {Studies in Computational Intelligence: Complex Networks and Their Applications V},
    year = {2017},
    number = {693},
    pages = {809--820},
    doi = {10.1007/978-3-319-50901-3}
}
Matke, C., Medjroubi, W., Kleinhans, D., Sager, S. Structure Analysis of the German Transmission Network Using the Open Source Model SciGRID 2017 Advances in Energy System Optimization   inproceedings
 
BibTeX:
@inproceedings{Matke2017a,
    author = {Matke, Carsten and Medjroubi, Wided and Kleinhans, David and Sager, Sebastian},
    title = {Structure Analysis of the German Transmission Network Using the Open Source Model SciGRID},
    booktitle = {Advances in Energy System Optimization},
    publisher = {Springer International Publishing},
    year = {2017},
    editor = {Bertsch, Valentin and Fichtner, Wolf and Heuveline, Vincent and Leibfried, Thomas},
    pages = {177--188},
    address = {Cham}
}
Huschto, T., Sager, S. Pricing Conspicuous Consumption Products in Recession Periods with Uncertain Strength 2014 European Journal of Decision Processes   article
url  
BibTeX:
@article{Huschto2014,
    author = {Huschto, T. and Sager, S.},
    title = {{P}ricing {C}onspicuous {C}onsumption {P}roducts in {R}ecession {P}eriods with {U}ncertain {S}trength},
    journal = {{E}uropean {J}ournal of {D}ecision {P}rocesses},
    year = {2014},
    volume = {2},
    number = {1--2},
    pages = {3--30},
    url = {http://www.optimization-online.org/DB_HTML/2012/09/3620.html}
}
Huschto, T., Sager, S. Solving Stochastic Optimal Control Problems by a Wiener Chaos Approach 2014 Vietnam Journal of Mathematics   article
url  
BibTeX:
@article{Huschto2014a,
    author = {Huschto, T. and Sager, S.},
    title = {{S}olving {S}tochastic {O}ptimal {C}ontrol {P}roblems by a {W}iener {C}haos {A}pproach},
    journal = {{V}ietnam {J}ournal of {M}athematics},
    year = {2014},
    volume = {42},
    number = {1},
    pages = {83--113},
    url = {https://mathopt.de/publications/Huschto2014a.pdf}
}
Huschto, T. Numerical Methods for Random Parameter Optimal Control and the Optimal Control of Stochastic Differential Equations 2014 School: University Heidelberg   phdthesis
url  
BibTeX:
@phdthesis{Huschto2014b,
    author = {Huschto, T.},
    title = {{N}umerical {M}ethods for {R}andom {P}arameter {O}ptimal {C}ontrol and the {O}ptimal {C}ontrol of {S}tochastic {D}ifferential {E}quations},
    school = {University Heidelberg},
    year = {2014},
    url = {https://mathopt.de/publications/Huschto2014.pdf}
}

Further references of the MathOpt group can be found on this page.

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Prof. Dr. Sebastian Sager
Head of MathOpt group
at the Institute of Mathematical Optimization
at the Faculty of Mathematics
at the Otto-von-Guericke University Magdeburg

Universitätsplatz 2, 02-224
39106 Magdeburg, Germany

: +49 391 67 58745
:

Susanne Heß

Universitätsplatz 2, 02-201
39106 Magdeburg, Germany

: +49 391 67 58756
:

  • currently no upcoming news

...more

Prof. Dr. Sebastian Sager
Head of MathOpt group
at the Institute of Mathematical Optimization
at the Faculty of Mathematics
at the Otto-von-Guericke University Magdeburg

Universitätsplatz 2, 02-224
39106 Magdeburg, Germany

: +49 391 67 58745
:

Susanne Heß

Universitätsplatz 2, 02-201
39106 Magdeburg, Germany

: +49 391 67 58756
: