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DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints

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  • Baumgärtner, Nils
  • Shu, David
  • Bahl, Björn
  • Hennen, Maike
  • Hollermann, Dinah Elena
  • Bardow, André

Abstract

Long-term operational optimization of energy systems results in challenging, large-scale problems. These large-scale problems can be directly decomposed into smaller subproblems, in the absence of time-coupling constraints and variables. However, time-coupling is common in energy systems, e. g. due to (seasonal) energy storage and peak-power prices. To solve time-coupled long-term operational optimization problems, we propose the method DeLoop for the Decomposition-based Long-term operational optimization of energy systems with time-coupling. DeLoop calculates feasible solutions (upper bounds) by decomposing the operational optimization problem into smaller subproblems. The solutions of these subproblems are recombined to obtain a feasible solution for the original long-term problem. To evaluate the quality of the feasible solutions, DeLoop computes lower bounds by linear programming relaxation. DeLoop iteratively decreases the number of subproblems and employs the Branch-and-Cut procedure to tighten the bounds. In a case study of an energy system, DeLoop converges fast, outperforming a commercial state-of-the-art solver by a factor of 32.

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  • Baumgärtner, Nils & Shu, David & Bahl, Björn & Hennen, Maike & Hollermann, Dinah Elena & Bardow, André, 2020. "DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220303790
    DOI: 10.1016/j.energy.2020.117272
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    References listed on IDEAS

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    1. Renaldi, Renaldi & Friedrich, Daniel, 2017. "Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage," Energy, Elsevier, vol. 133(C), pages 784-795.
    2. Thomas Hanne & Rolf Dornberger, 2017. "Computational Intelligence in Logistics and Supply Chain Management," International Series in Operations Research and Management Science, Springer, number 978-3-319-40722-7, December.
    3. Rong, Aiying & Lahdelma, Risto & Luh, Peter B., 2008. "Lagrangian relaxation based algorithm for trigeneration planning with storages," European Journal of Operational Research, Elsevier, vol. 188(1), pages 240-257, July.
    4. Kavvadias, K.C. & Maroulis, Z.B., 2010. "Multi-objective optimization of a trigeneration plant," Energy Policy, Elsevier, vol. 38(2), pages 945-954, February.
    5. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    6. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    7. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2019. "A rolling-horizon optimization algorithm for the long term operational scheduling of cogeneration systems," Energy, Elsevier, vol. 184(C), pages 73-90.
    8. Fu Lin & Sven Leyffer & Todd Munson, 2016. "A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings," Computational Optimization and Applications, Springer, vol. 65(1), pages 1-46, September.
    9. Voll, Philip & Klaffke, Carsten & Hennen, Maike & Bardow, André, 2013. "Automated superstructure-based synthesis and optimization of distributed energy supply systems," Energy, Elsevier, vol. 50(C), pages 374-388.
    10. Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.
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