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From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems

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  • Triebs, Merlin Sebastian
  • Tsatsaronis, George

Abstract

Using the heating demand of the final customer as the heat supply input time series in investment or dispatch models of district heating systems could lead to erroneous results. Both thermal losses and the network’s transient behavior lead to a mismatch between heat demand and required heat supply and should be considered. Following the methodology of standard load profiles for natural gas usage, standard load profiles for district heating systems at the plant level are developed by analyzing the measured heat supply of four different district heating systems for multiple years. The derived standard load profiles can be used to consider network transients without a complex physical model. The correction of the transient behavior is coupled with three different options to consider thermal losses in the network. Considering the transient behavior leads to an average reduction in Root Mean Square Error of 35 % compared to the neglection of the transients. Compared to a direct forecast method, the proposed approach shows a 5 % decrease in Root Mean Square Error with an increase in peak load estimation accuracy by 7 percentage points. The proposed methodology is best coupled with loss distribution methods relying on a constant share of the actual load or on the grid’s flow and return temperatures. The proposed method and the published dataset aim to develop annual load profiles for data-scarce district heating networks to serve as an input parameter for long-term dispatch or investment problems.

Suggested Citation

  • Triebs, Merlin Sebastian & Tsatsaronis, George, 2022. "From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922000551
    DOI: 10.1016/j.apenergy.2022.118571
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    1. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Maciej Bujalski & Paweł Madejski, 2021. "Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models," Energies, MDPI, vol. 14(8), pages 1-15, April.
    3. Heitkoetter, Wilko & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2020. "Regionalised heat demand and power-to-heat capacities in Germany – An open dataset for assessing renewable energy integration," Applied Energy, Elsevier, vol. 259(C).
    4. Wang, Yaran & You, Shijun & Zhang, Huan & Zheng, Xuejing & Zheng, Wandong & Miao, Qingwei & Lu, Gang, 2017. "Thermal transient prediction of district heating pipeline: Optimal selection of the time and spatial steps for fast and accurate calculation," Applied Energy, Elsevier, vol. 206(C), pages 900-910.
    5. Guelpa, Elisa, 2020. "Impact of network modelling in the analysis of district heating systems," Energy, Elsevier, vol. 213(C).
    6. Guelpa, Elisa & Marincioni, Ludovica & Capone, Martina & Deputato, Stefania & Verda, Vittorio, 2019. "Thermal load prediction in district heating systems," Energy, Elsevier, vol. 176(C), pages 693-703.
    7. Henning, Hans-Martin & Palzer, Andreas, 2014. "A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies—Part I: Methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 1003-1018.
    8. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    9. Lotta Kannari & Jussi Kiljander & Kalevi Piira & Jouko Piippo & Pekka Koponen, 2021. "Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator," Forecasting, MDPI, vol. 3(2), pages 1-13, April.
    10. Narula, Kapil & de Oliveira Filho, Fleury & Villasmil, Willy & Patel, Martin K., 2020. "Simulation method for assessing hourly energy flows in district heating system with seasonal thermal energy storage," Renewable Energy, Elsevier, vol. 151(C), pages 1250-1268.
    11. Palzer, Andreas & Henning, Hans-Martin, 2014. "A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies – Part II: Results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 1019-1034.
    12. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    13. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    14. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    15. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    16. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    17. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    18. Schweiger, Gerald & Rantzer, Jonatan & Ericsson, Karin & Lauenburg, Patrick, 2017. "The potential of power-to-heat in Swedish district heating systems," Energy, Elsevier, vol. 137(C), pages 661-669.
    19. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).
    20. Braas, Hagen & Jordan, Ulrike & Best, Isabelle & Orozaliev, Janybek & Vajen, Klaus, 2020. "District heating load profiles for domestic hot water preparation with realistic simultaneity using DHWcalc and TRNSYS," Energy, Elsevier, vol. 201(C).
    21. Difs, Kristina & Danestig, Maria & Trygg, Louise, 2009. "Increased use of district heating in industrial processes - Impacts on heat load duration," Applied Energy, Elsevier, vol. 86(11), pages 2327-2334, November.
    22. Papadis, Elisa & Tsatsaronis, George, 2020. "Challenges in the decarbonization of the energy sector," Energy, Elsevier, vol. 205(C).
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