IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2086-d1384090.html
   My bibliography  Save this article

Systematic Development of Application-Oriented Operating Strategies for the Example of an Industrial Heating Supply System

Author

Listed:
  • Lukas Theisinger

    (Institute of Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany)

  • Michael Frank

    (Institute of Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany)

  • Matthias Weigold

    (Institute of Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany)

Abstract

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to transform their energy system. This transformation usually leads to an increase in topological complexity, which in turn results in increased operational complexity. Existing approaches from the field of supervisory and optimal control are capable of mastering complex operational problems. However, due to the complex and non-transparent implementation, there is still no industrial penetration, which hinders the necessary transformation of energy systems. This work aims at establishing trust in these control approaches and presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy heating systems. It combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies, which are inherently easy to understand and implementable. By splitting the procedure model into five phases, expert knowledge can be integrated in a targeted manner. The procedure model is validated by the exemplary application to an industrial heating supply system. As part of an optimization study, the operating strategy developed is compared with both an MPC strategy and a baseline strategy. While the conventional MPC approach represents the upper limit of optimality, the operating strategy developed is able to achieve comparable results. Compared to the baseline strategy, a relative reduction in operating costs of 5.4% to 37.0% is achieved.

Suggested Citation

  • Lukas Theisinger & Michael Frank & Matthias Weigold, 2024. "Systematic Development of Application-Oriented Operating Strategies for the Example of an Industrial Heating Supply System," Energies, MDPI, vol. 17(9), pages 1-13, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2086-:d:1384090
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2086/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/9/2086/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Drgoňa, Ján & Picard, Damien & Kvasnica, Michal & Helsen, Lieve, 2018. "Approximate model predictive building control via machine learning," Applied Energy, Elsevier, vol. 218(C), pages 199-216.
    2. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(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. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    2. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    3. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    4. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    5. Zhe Tian & Chuang Ye & Jie Zhu & Jide Niu & Yakai Lu, 2023. "Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    6. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    7. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    8. 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).
    9. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    10. Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    11. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    12. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
    13. Byung-Ki Jeon & Eui-Jong Kim & Younggy Shin & Kyoung-Ho Lee, 2018. "Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems," Sustainability, MDPI, vol. 11(1), pages 1-16, December.
    14. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    15. Kumar, Devesh & Pindoriya, Naran M., 2024. "A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant," Energy, Elsevier, vol. 290(C).
    16. Kyoumars Habibi & Seyedeh Maryam Hoseini & Majid Dehshti & Mojtaba Khanian & Amir Mosavi, 2020. "The Impact of Natural Elements on Environmental Comfort in the Iranian-Islamic Historical City of Isfahan," IJERPH, MDPI, vol. 17(16), pages 1-18, August.
    17. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    18. Campos, Gustavo & Liu, Yu & Schmidt, Devon & Yonkoski, Joseph & Colvin, Daniel & Trombly, David M. & El-Farra, Nael H. & Palazoglu, Ahmet, 2021. "Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant," Applied Energy, Elsevier, vol. 300(C).
    19. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    20. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(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:17:y:2024:i:9:p:2086-:d:1384090. 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.