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Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage

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  • Renaldi, Renaldi
  • Friedrich, Daniel

Abstract

As a vital part of future low carbon energy systems, storage technologies need to be included in the overall optimisation of energy systems. However, this comes with a price of increasing complexity and computational cost. The increase in complexity can be limited by using simplified time series formulations in the optimisation process, e.g. typical days or multiple time grids. This in turn will affect the computational cost and quality of the optimisation results. The trade-off between these two aspects has to be quantified in order to appropriately use the simplification method. This paper investigates the implementation of the multiple time grids approach in the optimisation of a solar district heating system with short- and long-term thermal energy storage. The multiple time grids can improve the optimisation computational time by over an order of magnitude. Nevertheless, this is not a general rule since it is shown that there is a possibility for the computational time to increase with time step size. Furthermore, the benefits of multiple time grids become more evident in optimisation with a longer time horizon, reaching almost two order of magnitude improvement in computational time for the case with 6 years time horizon and 5% MIP gap.

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  • 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.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:784-795
    DOI: 10.1016/j.energy.2017.05.120
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    Cited by:

    1. Mansoor, Muhammad & Stadler, Michael & Zellinger, Michael & Lichtenegger, Klaus & Auer, Hans & Cosic, Armin, 2021. "Optimal planning of thermal energy systems in a microgrid with seasonal storage and piecewise affine cost functions," Energy, Elsevier, vol. 215(PA).
    2. Reveron Baecker, Beneharo & Candas, Soner, 2022. "Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    4. Ikäheimo, Jussi & Weiss, Robert & Kiviluoma, Juha & Pursiheimo, Esa & Lindroos, Tomi J., 2022. "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system," Applied Energy, Elsevier, vol. 305(C).
    5. Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
    6. Zhang, Zhaoyan & Wang, Peiguang & Jiang, Ping & Liu, Zhiheng & Fu, Lei, 2022. "Energy management of ultra-short-term optimal scheduling of integrated energy system considering the characteristics of heating network," Energy, Elsevier, vol. 240(C).
    7. Cuisinier, E. & Lemaire, P. & Ruby, A. & Bourasseau, C. & Penz, B., 2023. "Impact of operational modelling choices on techno-economic modelling of local energy systems," Energy, Elsevier, vol. 276(C).
    8. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    9. Klemm, Christian & Wiese, Frauke & Vennemann, Peter, 2023. "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution," Applied Energy, Elsevier, vol. 334(C).
    10. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2022. "A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems," Energy, Elsevier, vol. 258(C).
    11. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal configuration of battery energy storage system with multiple types of batteries based on supply-demand characteristics," Energy, Elsevier, vol. 206(C).
    12. 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).
    13. Zhibin Liu & Feng Guo & Jiaqi Liu & Xinyan Lin & Ao Li & Zhaoyan Zhang & Zhiheng Liu, 2023. "A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System," Energies, MDPI, vol. 16(1), pages 1-19, January.
    14. Zhang, Lizhi & Sun, Bo & Li, Fan, 2024. "Triple-layer joint optimization of capacity and operation for integrated energy systems by coordination on multiple timescales," Energy, Elsevier, vol. 302(C).
    15. Wang, Lixiao & Jing, Z.X. & Zheng, J.H. & Wu, Q.H. & Wei, Feng, 2018. "Decentralized optimization of coordinated electrical and thermal generations in hierarchical integrated energy systems considering competitive individuals," Energy, Elsevier, vol. 158(C), pages 607-622.
    16. Göke, Leonard, 2021. "A graph-based formulation for modeling macro-energy systems," Applied Energy, Elsevier, vol. 301(C).
    17. Bravo, Ruben & Ortiz, Carlos & Chacartegui, Ricardo & Friedrich, Daniel, 2021. "Multi-objective optimisation and guidelines for the design of dispatchable hybrid solar power plants with thermochemical energy storage," Applied Energy, Elsevier, vol. 282(PB).
    18. Pan, Guangsheng & Gu, Wei & Qiu, Haifeng & Lu, Yuping & Zhou, Suyang & Wu, Zhi, 2020. "Bi-level mixed-integer planning for electricity-hydrogen integrated energy system considering levelized cost of hydrogen," Applied Energy, Elsevier, vol. 270(C).
    19. Lei, Zijian & Yu, Hao & Li, Peng & Ji, Haoran & Yan, Jinyue & Song, Guanyu & Wang, Chengshan, 2024. "A compact time horizon compression method for planning community integrated energy systems with long-term energy storage," Applied Energy, Elsevier, vol. 361(C).
    20. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    21. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    22. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    23. Gronier, Timothé & Fitó, Jaume & Franquet, Erwin & Gibout, Stéphane & Ramousse, Julien, 2022. "Iterative sizing of solar-assisted mixed district heating network and local electrical grid integrating demand-side management," Energy, Elsevier, vol. 238(PA).
    24. Rohde, Daniel & Knudsen, Brage Rugstad & Andresen, Trond & Nord, Natasa, 2020. "Dynamic optimization of control setpoints for an integrated heating and cooling system with thermal energy storages," Energy, Elsevier, vol. 193(C).
    25. Diana Enescu & Gianfranco Chicco & Radu Porumb & George Seritan, 2020. "Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends," Energies, MDPI, vol. 13(2), pages 1-21, January.

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