IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1514-d1056630.html
   My bibliography  Save this article

Multi-Method Simulation and Multi-Objective Optimization for Energy-Flexibility-Potential Assessment of Food-Production Process Cooling

Author

Listed:
  • Daniel Anthony Howard

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

  • Bo Nørregaard Jørgensen

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

  • Zheng Ma

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Process cooling for food production is an energy-intensive industry with complex interactions and restrictions that complicate the ability to utilize energy-flexibility due to unforeseen consequences in production. Therefore, methods for assessing the potential flexibility in individual facilities to enable the active participation of process-cooling facilities in the electricity system are essential, but not yet well discussed in the literature. Therefore, this paper introduces an assessment method based on multi-method simulation and multi-objective optimization for investigating energy flexibility in process cooling, with a case study of a Danish process-cooling facility for canned-meat food production. Multi-method simulation is used in this paper: multi-agent-based simulation to investigate individual entities within the process-cooling system and the system’s behavior; discrete-event simulation to explore the entire process-cooling flow; and system dynamics to capture the thermophysical properties of the refrigeration unit and states of the refrigerated environment. A simulation library is developed, and is able to represent a generic production-flow of the canned-food process cooling. A data-driven symbolic-regression approach determines the complex logic of individual agents. Using a binary tuple-matrix for refrigeration-schedule optimization, the refrigeration-cycle operation is determined, based on weather forecasts, electricity price, and electricity CO 2 emissions without violating individual room-temperature limits. The simulation results of one-week’s production in October 2020 show that 32% of energy costs can be saved and 822 kg of CO 2 emissions can be reduced. The results thereby show the energy-flexibility potential in the process-cooling facilities, with the benefit of overall production cost and CO 2 emissions reduction; at the same time, the production quality and throughput are not influenced.

Suggested Citation

  • Daniel Anthony Howard & Bo Nørregaard Jørgensen & Zheng Ma, 2023. "Multi-Method Simulation and Multi-Objective Optimization for Energy-Flexibility-Potential Assessment of Food-Production Process Cooling," Energies, MDPI, vol. 16(3), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1514-:d:1056630
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1514/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1514/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ding Ding, 2022. "The impacts of carbon pricing on the electricity market in Japan," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    2. Meenu R Mridula & Ashalatha S Nair & K Satheesh Kumar, 2018. "Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-13, February.
    3. Zheng Ma & Mette Jessen Schultz & Kristoffer Christensen & Magnus Værbak & Yves Demazeau & Bo Nørregaard Jørgensen, 2019. "The Application of Ontologies in Multi-Agent Systems in the Energy Sector: A Scoping Review," Energies, MDPI, vol. 12(16), pages 1-31, August.
    4. Stoll, Pia & Brandt, Nils & Nordström, Lars, 2014. "Including dynamic CO2 intensity with demand response," Energy Policy, Elsevier, vol. 65(C), pages 490-500.
    5. Alcázar-Ortega, Manuel & Álvarez-Bel, Carlos & Domijan, Alexander & Escrivá-Escrivá, Guillermo, 2012. "Economic and environmental evaluation of customers' flexibility participating in operation markets: Application to the meat industry," Energy, Elsevier, vol. 41(1), pages 368-379.
    6. Hovgaard, Tobias Gybel & Larsen, Lars F.S. & Edlund, Kristian & Jørgensen, John Bagterp, 2012. "Model predictive control technologies for efficient and flexible power consumption in refrigeration systems," Energy, Elsevier, vol. 44(1), pages 105-116.
    7. Ramírez, C.A. & Patel, M. & Blok, K., 2006. "How much energy to process one pound of meat? A comparison of energy use and specific energy consumption in the meat industry of four European countries," Energy, Elsevier, vol. 31(12), pages 2047-2063.
    8. Fatras, Nicolas & Ma, Zheng & Jørgensen, Bo Nørregaard, 2022. "Process-to-market matrix mapping: A multi-criteria evaluation framework for industrial processes’ electricity market participation feasibility," Applied Energy, Elsevier, vol. 313(C).
    9. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    Full references (including those not matched with items on IDEAS)

    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. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    2. Biegel, Benjamin & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Value of flexible consumption in the electricity markets," Energy, Elsevier, vol. 66(C), pages 354-362.
    3. Qianwen Li & Zhilong Chen & Jialin Min & Mengjie Xu & Yanhong Zhan & Wenyue Zhang & Chuanwang Sun, 2024. "Hybrid transaction model for optimizing the distributed power trading market," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    4. Biegel, Benjamin & Westenholz, Mikkel & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Integration of flexible consumers in the ancillary service markets," Energy, Elsevier, vol. 67(C), pages 479-489.
    5. Montuori, Lina & Alcázar-Ortega, Manuel, 2021. "Demand response strategies for the balancing of natural gas systems: Application to a local network located in The Marches (Italy)," Energy, Elsevier, vol. 225(C).
    6. Karakaya, Ahmet & Özilgen, Mustafa, 2011. "Energy utilization and carbon dioxide emission in the fresh, paste, whole-peeled, diced, and juiced tomato production processes," Energy, Elsevier, vol. 36(8), pages 5101-5110.
    7. Sun, Kai & Tseng, Chen-Ting & Shan-Hill Wong, David & Shieh, Shyan-Shu & Jang, Shi-Shang & Kang, Jia-Lin & Hsieh, Wei-Dong, 2015. "Model predictive control for improving waste heat recovery in coke dry quenching processes," Energy, Elsevier, vol. 80(C), pages 275-283.
    8. Heidari, M.D. & Omid, M. & Akram, A., 2011. "Energy efficiency and econometric analysis of broiler production farms," Energy, Elsevier, vol. 36(11), pages 6536-6541.
    9. Wolf, Isabel & Holzapfel, Peter K.R. & Meschede, Henning & Finkbeiner, Matthias, 2023. "On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation," Applied Energy, Elsevier, vol. 348(C).
    10. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    11. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    12. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    13. Li, Hao & Wang, Zhao-Hua & Zhang, Bin, 2023. "How social interaction induce energy-saving behaviors in buildings: Interpersonal & passive interactions v.s. public & active interactions," Energy Economics, Elsevier, vol. 118(C).
    14. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    15. Henri de Groot & Peter Mulder, 2011. "Energy-intensity developments for 19 OECD countries and 51 sectors," CPB Discussion Paper 171, CPB Netherlands Bureau for Economic Policy Analysis.
    16. L. (Lisa B.) Ryan & Sarah La Monaca & Linda Mastrandrea & Petr Spodniak, 2018. "Harnessing Electricity Retail Tariffs to Support Climate Change Policy," Working Papers 201822, School of Economics, University College Dublin.
    17. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    18. Roldán Fernández, Juan Manuel & Payán, Manuel Burgos & Santos, Jesús Manuel Riquelme & García, Ángel Luis Trigo, 2017. "The voluntary price for the small consumer: Real-time pricing in Spain," Energy Policy, Elsevier, vol. 102(C), pages 41-51.
    19. Giacone, E. & Mancò, S., 2012. "Energy efficiency measurement in industrial processes," Energy, Elsevier, vol. 38(1), pages 331-345.
    20. Chen, Yuzhu & Xu, Jinzhao & Wang, Jun & Lund, Peter D., 2022. "Optimization of a weather-based energy system for high cooling and low heating conditions using different types of water-cooled chiller," Energy, Elsevier, vol. 252(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:gam:jeners:v:16:y:2023:i:3:p:1514-:d:1056630. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.