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Optimal design of decentralized energy conversion systems for smart microgrids using decomposition methods

Author

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  • Schütz, Thomas
  • Hu, Xiaolin
  • Fuchs, Marcus
  • Müller, Dirk

Abstract

The design of decentralized energy conversion systems in smart residential microgrids is a challenging optimization problem due to the variety of available generation and storage devices. Common measures to reduce the problem's size and complexity are to reduce modeling accuracy, aggregate multiple loads or change the temporal resolution. However, since these attempts alter the optimization problem and consequently lead to different solutions as intended, this paper presents and analyses a decomposition method for solving the original problem iteratively.

Suggested Citation

  • Schütz, Thomas & Hu, Xiaolin & Fuchs, Marcus & Müller, Dirk, 2018. "Optimal design of decentralized energy conversion systems for smart microgrids using decomposition methods," Energy, Elsevier, vol. 156(C), pages 250-263.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:250-263
    DOI: 10.1016/j.energy.2018.05.050
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    Cited by:

    1. Rigo-Mariani, Rémy, 2022. "Optimized time reduction models applied to power and energy systems planning – Comparison with existing methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    2. Timo Kannengießer & Maximilian Hoffmann & Leander Kotzur & Peter Stenzel & Fabian Schuetz & Klaus Peters & Stefan Nykamp & Detlef Stolten & Martin Robinius, 2019. "Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System," Energies, MDPI, vol. 12(14), pages 1-27, July.
    3. Xia, Tian & Huang, Wujing & Lu, Xi & Zhang, Ning & Kang, Chongqing, 2020. "Planning district multiple energy systems considering year-round operation," Energy, Elsevier, vol. 213(C).
    4. Ghanaee, Reza & Akbari Foroud, Asghar, 2019. "Enhanced structure and optimal capacity sizing method for turbo-expander based microgrid with simultaneous recovery of cooling and electrical energy," Energy, Elsevier, vol. 170(C), pages 284-304.
    5. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2020. "A near-optimal solution method for coordinated operation planning problem of power- and heat-interchange networks using column generation-based decomposition," Energy, Elsevier, vol. 197(C).
    6. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    7. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions," Energy, Elsevier, vol. 214(C).
    8. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    9. Aguado, José A. & Paredes, Ángel, 2023. "Coordinated and decentralized trading of flexibility products in Inter-DSO Local Electricity Markets via ADMM," Applied Energy, Elsevier, vol. 337(C).
    10. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    11. Andiappan, Viknesh, 2022. "Optimization of smart energy systems based on response time and energy storage losses," Energy, Elsevier, vol. 258(C).

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