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Dynamic optimization of control setpoints for an integrated heating and cooling system with thermal energy storages

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  • Rohde, Daniel
  • Knudsen, Brage Rugstad
  • Andresen, Trond
  • Nord, Natasa

Abstract

Energy systems for buildings and neighborhoods are expected to become more complex and flexible. Advanced control strategies are required to exploit the full potential of this flexibility and are especially important for systems with storages. In this study, the control of an integrated heating and cooling system for a building complex in Oslo, Norway, was analyzed. Focus was on the control setpoints for the main heat pumps, which had a total heating capacity of about 1 MW and were connected to thermal storage tanks. Previously developed simulation models of the system and its main components were made suitable for dynamic optimization with long time horizons. JModelica.org was used to find optimal control trajectories for the system with two different objectives. The first objective was to reduce the electricity use of the system and the second objective was to reduce the electricity costs of the system. The results showed that the electricity use of the system could be reduced by about 5% for the analyzed year. The electricity costs could be reduced by about 5–11% for the three analyzed winter months, depending on the variability of the electricity price and the size of the storage tanks.

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  • 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).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219324661
    DOI: 10.1016/j.energy.2019.116771
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    4. 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.
    5. 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).
    6. 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).
    7. Tian, Shen & Yang, Qifan & Hui, Na & Bai, Haozhi & Shao, Shuangquan & Liu, Shengchun, 2020. "Discharging process and performance of a portable cold thermal energy storage panel driven by embedded heat pipes," Energy, Elsevier, vol. 205(C).
    8. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    9. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    10. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).

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