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Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling

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  • Coccia, Gianluca
  • Mugnini, Alice
  • Polonara, Fabio
  • Arteconi, Alessia

Abstract

District cooling systems (DCSs) belonging to multi-energy systems can be managed by model predictive controls (MPCs) designed to reduce the amount of electrical energy collected from the grid for backup cooling systems when there is a temporal mismatch between energy demand and availability. In this paper, a DCS recovering cold thermal energy from a liquid-to-compressed natural gas fuel station is used in an 8-user residential neighborhood to provide space cooling in summertime. In the residential neighborhood, there is a multi-energy system, including the DCS, photovoltaic panels, and backup systems based on variable-load air-to-water heat pumps. One user of the district was allowed to manage its energy demand with an MPC based on an artificial neural network (ANN). By integrating the ANN-based MPC routine in the building simulation environment and unlocking the energy flexibility of thermostatically controlled loads (TCLs) using variable setpoints, it was possible to reduce electrical energy consumption up to −71% with respect to a reference case with a rule-based control. This work highlights also the importance of the ANN training process for a proper representation of the TCL flexibility in the building model, which is not a trivial aspect to be taken into account in data driven models.

Suggested Citation

  • Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002073
    DOI: 10.1016/j.energy.2021.119958
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    1. Sun, Fangtian & Li, Junlong & Fu, Lin & Li, Yonghong & Wang, Ruixiang & Zhang, Shigang, 2020. "New configurations of district heating and cooling system based on absorption and compression chillers driven by waste heat of flue gas from coke ovens," Energy, Elsevier, vol. 193(C).
    2. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    3. Arteconi, Alessia & Mugnini, Alice & Polonara, Fabio, 2019. "Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Sommer, Tobias & Sulzer, Matthias & Wetter, Michael & Sotnikov, Artem & Mennel, Stefan & Stettler, Christoph, 2020. "The reservoir network: A new network topology for district heating and cooling," Energy, Elsevier, vol. 199(C).
    5. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    6. Arabkoohsar, A. & Sadi, M., 2020. "A solar PTC powered absorption chiller design for Co-supply of district heating and cooling systems in Denmark," Energy, Elsevier, vol. 193(C).
    7. Du, Chenqiu & Li, Baizhan & Yu, Wei & Liu, Hong & Yao, Runming, 2019. "Energy flexibility for heating and cooling based on seasonal occupant thermal adaptation in mixed-mode residential buildings," Energy, Elsevier, vol. 189(C).
    8. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2016. "District cooling systems: Technology integration, system optimization, challenges and opportunities for applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 253-264.
    9. He, Tianbiao & Chong, Zheng Rong & Zheng, Junjie & Ju, Yonglin & Linga, Praveen, 2019. "LNG cold energy utilization: Prospects and challenges," Energy, Elsevier, vol. 170(C), pages 557-568.
    10. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2019. "Potential of District Cooling Systems: A Case Study on Recovering Cold Energy from Liquefied Natural Gas Vaporization," Energies, MDPI, vol. 12(15), pages 1-13, August.
    11. Dorotić, Hrvoje & Pukšec, Tomislav & Duić, Neven, 2019. "Multi-objective optimization of district heating and cooling systems for a one-year time horizon," Energy, Elsevier, vol. 169(C), pages 319-328.
    12. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
    13. Jakubcionis, Mindaugas & Carlsson, Johan, 2017. "Estimation of European Union residential sector space cooling potential," Energy Policy, Elsevier, vol. 101(C), pages 225-235.
    14. Alessia Arteconi & Fabio Polonara, 2018. "Assessing the Demand Side Management Potential and the Energy Flexibility of Heat Pumps in Buildings," Energies, MDPI, vol. 11(7), pages 1-19, July.
    15. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    16. Lyons, Ben & O’Dwyer, Edward & Shah, Nilay, 2020. "Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems," Energy, Elsevier, vol. 197(C).
    17. Lund, Henrik & Duic, Neven & Østergaard, Poul Alberg & Mathiesen, Brian Vad, 2018. "Future district heating systems and technologies: On the role of smart energy systems and 4th generation district heating," Energy, Elsevier, vol. 165(PA), pages 614-619.
    18. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    19. Jangsten, Maria & Lindholm, Torbjörn & Dalenbäck, Jan-Olof, 2020. "Analysis of operational data from a district cooling system and its connected buildings," Energy, Elsevier, vol. 203(C).
    20. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    21. Vandermeulen, Annelies & van der Heijde, Bram & Helsen, Lieve, 2018. "Controlling district heating and cooling networks to unlock flexibility: A review," Energy, Elsevier, vol. 151(C), pages 103-115.
    22. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    23. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
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    Cited by:

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    2. Wang, Huijie & Qiu, Baoyun & Zhao, Fangling & Yan, Tianxu, 2023. "Method for increasing net power of power plant based on operation optimization of circulating cooling water system," Energy, Elsevier, vol. 282(C).
    3. Gao, Cheng & Wang, Dan & Sun, Yuying & Wang, Wei & Zhang, Xiuyu, 2023. "Optimal load dispatch of multi-source looped district cooling systems based on energy and hydraulic performances," Energy, Elsevier, vol. 274(C).

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