IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v222y2021ics0360544221002073.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221002073
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.119958?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. 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.
    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. Oscar Villegas Mier & Anna Dittmann & Wiebke Herzberg & Holger Ruf & Elke Lorenz & Michael Schmidt & Rainer Gasper, 2023. "Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast," Energies, MDPI, vol. 16(19), pages 1-19, October.
    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).

    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. 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).
    2. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Zabala, Laura & Febres, Jesus & Sterling, Raymond & López, Susana & Keane, Marcus, 2020. "Virtual testbed for model predictive control development in district cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    4. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    5. 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).
    6. 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.
    7. Saletti, Costanza & Morini, Mirko & Gambarotta, Agostino, 2022. "Smart management of integrated energy systems through co-optimization with long and short horizons," Energy, Elsevier, vol. 250(C).
    8. John Clauß & Sebastian Stinner & Christian Solli & Karen Byskov Lindberg & Henrik Madsen & Laurent Georges, 2019. "Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating," Energies, MDPI, vol. 12(7), pages 1-25, April.
    9. Chicherin, Stanislav & Anvari-Moghaddam, Amjad, 2021. "Adjusting heat demands using the operational data of district heating systems," Energy, Elsevier, vol. 235(C).
    10. 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.
    11. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2021. "Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling," Energies, MDPI, vol. 14(2), pages 1-30, January.
    12. Shabnam Homaei & Mohamed Hamdy, 2021. "Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes," Energies, MDPI, vol. 14(10), pages 1-32, May.
    13. 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).
    14. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    15. Hou, Juan & Li, Haoran & Nord, Natasa & Huang, Gongsheng, 2023. "Model predictive control for a university heat prosumer with data centre waste heat and thermal energy storage," Energy, Elsevier, vol. 267(C).
    16. Zhang, Wei & Hong, Wenpeng & Jin, Xu, 2022. "Research on performance and control strategy of multi-cold source district cooling system," Energy, Elsevier, vol. 239(PB).
    17. Romanov, D. & Leiss, B., 2022. "Geothermal energy at different depths for district heating and cooling of existing and future building stock," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    18. Majdalani, Naim & Aelenei, Daniel & Lopes, Rui Amaral & Silva, Carlos Augusto Santo, 2020. "The potential of energy flexibility of space heating and cooling in Portugal," Utilities Policy, Elsevier, vol. 66(C).
    19. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    20. Wirtz, Marco, 2023. "nPro: A web-based planning tool for designing district energy systems and thermal networks," Energy, Elsevier, vol. 268(C).

    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:222:y:2021:i:c:s0360544221002073. 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.