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Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage

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  • van der Heijde, Bram
  • Vandermeulen, Annelies
  • Salenbien, Robbe
  • Helsen, Lieve

Abstract

The design and operational optimisation of fourth generation district heating networks is a crucial step towards highly renewable energy systems of the future. In order to optimise such complex systems, a toolbox modesto (multi-objective district energy systems toolbox for optimisation) is being developed. Seasonal thermal energy storage is an essential technology to allow larger shares of renewable energy sources, yet large computational power is required for its representation in full-year operational optimisations, as a step towards district energy system optimal design. To decrease computational complexity, a technique with representative days able to include seasonal thermal energy storage systems is developed and validated. This methodology combines different part-solutions from literature, but also adds a novel aspect to safeguard the chronology of the optimisation problem. To validate the approach, the design optimisation of a fictitious solar district heating system with seasonal thermal energy storage is compared to different representative day optimisations in two steps. The operational optimisation is a linear optimisation problem, implemented using modesto; the design optimisation is built as a genetic algorithm, optimising the size of the storage and solar systems in the network. The validation exercise is done for the operational and for the design optimisation separately. This comparative study shows that modelling with representative days adequately mimics the behaviour for the presented case. Furthermore, a solution speed-up in the order of 10–30 times is shown for the representative optimisations with respect to the full year optimisation, in line with the reduction of the number of variables.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:79-94
    DOI: 10.1016/j.apenergy.2019.04.030
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    References listed on IDEAS

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    1. Timmerman, Jonas & Hennen, Maike & Bardow, André & Lodewijks, Pieter & Vandevelde, Lieven & Van Eetvelde, Greet, 2017. "Towards low carbon business park energy systems: A holistic techno-economic optimisation model," Energy, Elsevier, vol. 125(C), pages 747-770.
    2. Fazlollahi, Samira & Becker, Gwenaelle & Ashouri, Araz & Maréchal, François, 2015. "Multi-objective, multi-period optimization of district energy systems: IV – A case study," Energy, Elsevier, vol. 84(C), pages 365-381.
    3. 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.
    4. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    5. 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.
    6. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    7. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    8. De Jaeger, Ina & Reynders, Glenn & Ma, Yixiao & Saelens, Dirk, 2018. "Impact of building geometry description within district energy simulations," Energy, Elsevier, vol. 158(C), pages 1060-1069.
    9. Welsch, M. & Howells, M. & Bazilian, M. & DeCarolis, J.F. & Hermann, S. & Rogner, H.H., 2012. "Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code," Energy, Elsevier, vol. 46(1), pages 337-350.
    10. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    11. Prina, Matteo Giacomo & Lionetti, Matteo & Manzolini, Giampaolo & Sparber, Wolfram & Moser, David, 2019. "Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning," Applied Energy, Elsevier, vol. 235(C), pages 356-368.
    12. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
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