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Testing and performance evaluation of the STORM controller in two demonstration sites

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  • Van Oevelen, Tijs
  • Vanhoudt, Dirk
  • Johansson, Christian
  • Smulders, Ed

Abstract

District heating and cooling (DHC) network control systems have a big part to play in enhancing energy system integration, which is vital to allow the transition towards a more sustainable and affordable energy system. The STORM controller is an example of an advanced DHC network controller that allows activating the flexibility from building thermal capacity. The STORM controller is capable of shifting and managing thermal demands in time, to improve the operational performance of an entire DHC system. This article presents the basic properties of the STORM controller, and the results of a field test campaign in two operational demonstration networks: in Heerlen (The Netherlands) and Rottne (Sweden). The performance of three control strategies is evaluated and discussed. The peak shaving strategy led to 3.1% reduction of peak heat production in Rottne. The market interaction tests demonstrated the possibility to temporarily increase the heat load by up to 96%, by charging the buildings, while limiting the overall heat consumption increase to 5.8%. The cell balancing tests in Heerlen achieved a 37–49% increase of the system capacity and peak reduction in the range 7.5–34%. DHC system operators can benefit from the STORM controller in the form of savings on operational costs and CO2 emissions, and increased system capacity.

Suggested Citation

  • Van Oevelen, Tijs & Vanhoudt, Dirk & Johansson, Christian & Smulders, Ed, 2020. "Testing and performance evaluation of the STORM controller in two demonstration sites," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s036054422030284x
    DOI: 10.1016/j.energy.2020.117177
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    References listed on IDEAS

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    1. Lund, Henrik & Østergaard, Poul Alberg & Chang, Miguel & Werner, Sven & Svendsen, Svend & Sorknæs, Peter & Thorsen, Jan Eric & Hvelplund, Frede & Mortensen, Bent Ole Gram & Mathiesen, Brian Vad & Boje, 2018. "The status of 4th generation district heating: Research and results," Energy, Elsevier, vol. 164(C), pages 147-159.
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    Cited by:

    1. Saloux, Etienne & Runge, Jason & Zhang, Kun, 2023. "Operation optimization of multi-boiler district heating systems using artificial intelligence-based model predictive control: Field demonstrations," Energy, Elsevier, vol. 285(C).
    2. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    3. Nielsen, Tore Bach & Lund, Henrik & Østergaard, Poul Alberg & Duic, Neven & Mathiesen, Brian Vad, 2021. "Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference," Energy, Elsevier, vol. 216(C).
    4. Angelidis, O. & Ioannou, A. & Friedrich, D. & Thomson, A. & Falcone, G., 2023. "District heating and cooling networks with decentralised energy substations: Opportunities and barriers for holistic energy system decarbonisation," Energy, Elsevier, vol. 269(C).
    5. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    6. Saloux, Etienne & Candanedo, José A., 2021. "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Applied Energy, Elsevier, vol. 291(C).

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