IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v268y2023ics0360544223000658.html
   My bibliography  Save this article

A simple dynamic optimization-based approach for sizing thermal energy storage using process data

Author

Listed:
  • Nakama, Caroline S.M.
  • Knudsen, Brage R.
  • Tysland, Agnes C.
  • Jäschke, Johannes

Abstract

Thermal energy storage (TES) can increase waste heat utilization in district heating (DH) by storing excess energy to be used later to compensate for energy deficit. When sizing a TES tank for DH, incorporating operational conditions can prevent suboptimal volumes and improve the utility enabled by the TES. However, the different time scales of the payback period for the tank and DH operation poses a challenge for optimizing the tank volume. We propose a method to optimally design a TES tank considering operational conditions of a DH plant using time varying waste heat. We formulate a multi-objective dynamic optimization model based on heat data for a long period, which is solved with a two-step approach. First, the data is screened by solving the model for short-term periods to detect intervals that allow for peak heating savings. Then, the model is re-solved using all selected intervals to determine an optimal tank volume. We conduct a trade-off analysis of the conflicting objectives, energy-saving and costs. The proposed method is demonstrated on a case study with historical data. Our method can explore the full feasible space of TES tank volumes and efficiently provide a trade-off curve without the need of exhaustive search.

Suggested Citation

  • Nakama, Caroline S.M. & Knudsen, Brage R. & Tysland, Agnes C. & Jäschke, Johannes, 2023. "A simple dynamic optimization-based approach for sizing thermal energy storage using process data," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000658
    DOI: 10.1016/j.energy.2023.126671
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223000658
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126671?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luca Urbanucci & Francesco D’Ettorre & Daniele Testi, 2019. "A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage," Energies, MDPI, vol. 12(5), pages 1-17, March.
    2. Badami, Marco & Fonti, Antonio & Carpignano, Andrea & Grosso, Daniele, 2018. "Design of district heating networks through an integrated thermo-fluid dynamics and reliability modelling approach," Energy, Elsevier, vol. 144(C), pages 826-838.
    3. Simeoni, Patrizia & Ciotti, Gellio & Cottes, Mattia & Meneghetti, Antonella, 2019. "Integrating industrial waste heat recovery into sustainable smart energy systems," Energy, Elsevier, vol. 175(C), pages 941-951.
    4. Benalcazar, Pablo, 2021. "Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study," Energy, Elsevier, vol. 234(C).
    5. 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.
    6. Martínez-Lera, S. & Ballester, J. & Martínez-Lera, J., 2013. "Analysis and sizing of thermal energy storage in combined heating, cooling and power plants for buildings," Applied Energy, Elsevier, vol. 106(C), pages 127-142.
    7. Li, Zhi & Lu, Yiji & Huang, Rui & Chang, Jinwei & Yu, Xiaonan & Jiang, Ruicheng & Yu, Xiaoli & Roskilly, Anthony Paul, 2021. "Applications and technological challenges for heat recovery, storage and utilisation with latent thermal energy storage," Applied Energy, Elsevier, vol. 283(C).
    8. Hanne Kauko & Daniel Rohde & Brage Rugstad Knudsen & Terje Sund-Olsen, 2020. "Potential of Thermal Energy Storage for a District Heating System Utilizing Industrial Waste Heat," Energies, MDPI, vol. 13(15), pages 1-12, July.
    9. Li, Haoran & Hou, Juan & Hong, Tianzhen & Ding, Yuemin & Nord, Natasa, 2021. "Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres," Energy, Elsevier, vol. 219(C).
    10. Guelpa, Elisa & Verda, Vittorio, 2019. "Thermal energy storage in district heating and cooling systems: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Connolly, D. & Lund, H. & Mathiesen, B.V. & Werner, S. & Möller, B. & Persson, U. & Boermans, T. & Trier, D. & Østergaard, P.A. & Nielsen, S., 2014. "Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system," Energy Policy, Elsevier, vol. 65(C), pages 475-489.
    12. Knudsen, Brage Rugstad & Rohde, Daniel & Kauko, Hanne, 2021. "Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach," Energy, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Trabert, Ulrich & Pag, Felix & Orozaliev, Janybek & Jordan, Ulrike & Vajen, Klaus, 2024. "Peak shaving at system level with a large district heating substation using deep learning forecasting models," Energy, Elsevier, vol. 301(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Knudsen, Brage Rugstad & Rohde, Daniel & Kauko, Hanne, 2021. "Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach," Energy, Elsevier, vol. 234(C).
    2. Trabert, Ulrich & Pag, Felix & Orozaliev, Janybek & Jordan, Ulrike & Vajen, Klaus, 2024. "Peak shaving at system level with a large district heating substation using deep learning forecasting models," Energy, Elsevier, vol. 301(C).
    3. Li, Haoran & Hou, Juan & Hong, Tianzhen & Nord, Natasa, 2022. "Distinguish between the economic optimal and lowest distribution temperatures for heat-prosumer-based district heating systems with short-term thermal energy storage," Energy, Elsevier, vol. 248(C).
    4. Chambers, Jonathan & Narula, Kapil & Sulzer, Matthias & Patel, Martin K., 2019. "Mapping district heating potential under evolving thermal demand scenarios and technologies: A case study for Switzerland," Energy, Elsevier, vol. 176(C), pages 682-692.
    5. Xu, Ruoyu & Liu, Xiaochen & Liu, Xiaohua & Zhang, Tao, 2024. "Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports," Energy, Elsevier, vol. 298(C).
    6. 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.
    7. 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).
    8. Saletti, Costanza & Zimmerman, Nathan & Morini, Mirko & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2021. "Enabling smart control by optimally managing the State of Charge of district heating networks," Applied Energy, Elsevier, vol. 283(C).
    9. Jalil-Vega, Francisca & Hawkes, Adam D., 2018. "The effect of spatial resolution on outcomes from energy systems modelling of heat decarbonisation," Energy, Elsevier, vol. 155(C), pages 339-350.
    10. Salah Vaisi & Saleh Mohammadi & Kyoumars Habibi, 2021. "Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks," Energies, MDPI, vol. 14(17), pages 1-17, September.
    11. Gonzalez-Salazar, Miguel & Klossek, Julia & Dubucq, Pascal & Punde, Thomas, 2023. "Portfolio optimization in district heating: Merit order or mixed integer linear programming?," Energy, Elsevier, vol. 265(C).
    12. Franz Zach & Florian Kretschmer & Gernot Stoeglehner, 2019. "Integrating Energy Demand and Local Renewable Energy Sources in Smart Urban Development Zones: New Options for Climate-Friendly Resilient Urban Planning," Energies, MDPI, vol. 12(19), pages 1-28, September.
    13. Antonino D’Amico & Domenico Panno & Giuseppina Ciulla & Antonio Messineo, 2020. "Multi-Energy School System for Seasonal Use in the Mediterranean Area," Sustainability, MDPI, vol. 12(20), pages 1-27, October.
    14. Simeoni, Patrizia & Ciotti, Gellio & Cottes, Mattia & Meneghetti, Antonella, 2019. "Integrating industrial waste heat recovery into sustainable smart energy systems," Energy, Elsevier, vol. 175(C), pages 941-951.
    15. Fester, Jakob & Østergaard, Peter Friis & Bentsen, Fredrik & Nielsen, Brian Kongsgaard, 2023. "A data-driven method for heat loss estimation from district heating service pipes using heat meter- and GIS data," Energy, Elsevier, vol. 277(C).
    16. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
    17. Dominković, Dominik Franjo & Stunjek, Goran & Blanco, Ignacio & Madsen, Henrik & Krajačić, Goran, 2020. "Technical, economic and environmental optimization of district heating expansion in an urban agglomeration," Energy, Elsevier, vol. 197(C).
    18. Li, Haoran & Hou, Juan & Tian, Zhiyong & Hong, Tianzhen & Nord, Natasa & Rohde, Daniel, 2022. "Optimize heat prosumers' economic performance under current heating price models by using water tank thermal energy storage," Energy, Elsevier, vol. 239(PB).
    19. Du, Binglin & Liu, Pei & Li, Zheng, 2023. "Coal power plants transition based on joint planning of power and central heating sectors: A case study of China," Energy, Elsevier, vol. 283(C).
    20. Pierpaolo Garavaso & Fabio Bignucolo & Jacopo Vivian & Giulia Alessio & Michele De Carli, 2021. "Optimal Planning and Operation of a Residential Energy Community under Shared Electricity Incentives," Energies, MDPI, vol. 14(8), pages 1-24, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000658. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.