IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p4938-d1665951.html
   My bibliography  Save this article

A Review of Industrial Load Flexibility Enhancement for Demand-Response Interaction

Author

Listed:
  • Jiubo Zhang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Yuanjun Guo

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Chen Lv

    (China Electric Power Research Institute, Beijing 100192, China)

  • Xiaofeng Xu

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Jichun Liu

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

The global transition toward low-carbon energy systems necessitates fundamental innovations in demand-side flexibility, particularly in industrial load regulation. This study presents a systematic review and critical analysis of 90 key research works (2015–2025) to establish a comprehensive framework for industrial load flexibility enhancement. We rigorously examined the tripartite interdependencies among the following: (1) Multi-energy flow physical coupling , addressing temporal-scale disparities in electricity-thermal-gas coordination under renewable penetration; (2) Uncertainty quantification , integrating data-driven and physics-informed modeling for robust decision-making; (3) Market mechanism synergy , analyzing demand response, carbon-P2P hybrid markets, and regulatory policy impacts. Our analysis reveals three fundamental challenges: the accuracy-stability trade-off in cross-timescale optimization, the policy-model disconnect in carbon-aware scheduling, and the computational complexity barrier for real-time industrial applications. The paper further proposes a roadmap for next-generation industrial load regulation systems, emphasizing co-optimization of technical feasibility, economic viability, and policy compliance. These findings advance both academic research and practical implementations for carbon-neutral power systems.

Suggested Citation

  • Jiubo Zhang & Bowen Zhou & Zhile Yang & Yuanjun Guo & Chen Lv & Xiaofeng Xu & Jichun Liu, 2025. "A Review of Industrial Load Flexibility Enhancement for Demand-Response Interaction," Sustainability, MDPI, vol. 17(11), pages 1-31, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4938-:d:1665951
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/4938/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/11/4938/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    2. Hu, Junfeng & Yan, Qingyou & Kahrl, Fredrich & Liu, Xu & Wang, Peng & Lin, Jiang, 2021. "Evaluating the ancillary services market for large-scale renewable energy integration in China's northeastern power grid," Utilities Policy, Elsevier, vol. 69(C).
    3. Wang, Kang & Wang, Chengfu & Yao, Wenliang & Zhang, Zhenwei & Liu, Chao & Dong, Xiaoming & Yang, Ming & Wang, Yong, 2024. "Embedding P2P transaction into demand response exchange: A cooperative demand response management framework for IES," Applied Energy, Elsevier, vol. 367(C).
    4. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
    5. Haakana, Juha & Tikka, Ville & Lassila, Jukka & Partanen, Jarmo, 2017. "Methodology to analyze combined heat and power plant operation considering electricity reserve market opportunities," Energy, Elsevier, vol. 127(C), pages 408-418.
    6. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    7. Liu, Wei & Li, Zhuangzhuang & Xing, Xinran & Chen, Xi & Wang, Yufei & Wang, Xuechun, 2025. "Non-cooperative game optimization for virtual power plants considering carbon trading market," Energy, Elsevier, vol. 317(C).
    8. Song, Xiao-hua & Han, Jing-jing & Zhang, Lu & Zhao, Cai-ping & Wang, Peng & Liu, Xiao-yan & Li, Qiao-chu, 2021. "Impacts of renewable portfolio standards on multi-market coupling trading of renewable energy in China: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 159(C).
    9. Zeng, Bo & Zhou, Yinyu & Xu, Xinzhu & Cai, Danting, 2024. "Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets," Applied Energy, Elsevier, vol. 357(C).
    10. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2024. "Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning," Energy, Elsevier, vol. 304(C).
    11. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    12. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    13. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Jia, Hongjie & Wang, Xiaoyu & Jin, Xiaolong & Cheng, Lin & Mu, Yunfei & Yu, Xiaodan & Wei, Wei, 2024. "Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading," Applied Energy, Elsevier, vol. 365(C).
    15. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    16. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    17. Huang, Chunyi & Li, Kangping & Zhang, Ning, 2025. "Strategic joint bidding and pricing of load aggregators in day-ahead demand response market," Applied Energy, Elsevier, vol. 377(PC).
    18. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    19. Yang, Yuyan & Xu, Xiao & Pan, Li & Liu, Junyong & Liu, Jichun & Hu, Weihao, 2024. "Distributed prosumer trading in the electricity and carbon markets considering user utility," Renewable Energy, Elsevier, vol. 228(C).
    20. Lou, Chengwei & Jin, Zekai & Zhou, Yue & Tang, Wei & Zhang, Lu & Yang, Jin, 2025. "Privacy-protected P2P electricity and carbon emission trading markets based on distributionally robust proximal atomic coordination algorithm," Applied Energy, Elsevier, vol. 384(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. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2024. "Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning," Energy, Elsevier, vol. 304(C).
    2. Ali Muqtadir & Bin Li & Bing Qi & Leyi Ge & Nianjiang Du & Chen Lin, 2025. "Demand Response Potential Forecasting: A Systematic Review of Methods, Challenges, and Future Directions," Energies, MDPI, vol. 18(19), pages 1-37, October.
    3. Zang, Xingyu & Li, Hangxin & Wang, Shengwei, 2025. "Levelized cost quantification of energy flexibility in high-density cities and evaluation of demand-side technologies for providing grid services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    4. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    5. Li, Sihui & Peng, Jinqing & Wang, Meng & Wang, Kai & Li, Houpei & Lu, Chujie, 2024. "Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM," Renewable Energy, Elsevier, vol. 221(C).
    6. Hua, Haochen & Du, Can & Chen, Xingying & Kong, Hui & Li, Kang & Liu, Zhao & Naidoo, Pathmanathan & Lv, Mian & Hu, Nan & Fu, Ming & Li, Bing, 2024. "Optimal dispatch of multiple interconnected-integrated energy systems considering multi-energy interaction and aggregated demand response for multiple stakeholders," Applied Energy, Elsevier, vol. 376(PA).
    7. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    9. Roksana Yasmin & B. M. Ruhul Amin & Rakibuzzaman Shah & Andrew Barton, 2024. "A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy," Sustainability, MDPI, vol. 16(2), pages 1-41, January.
    10. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    11. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. Zhang, Zhihui & Jing, Rui & Lin, Jian & Wang, Xiaonan & van Dam, Koen H. & Wang, Meng & Meng, Chao & Xie, Shan & Zhao, Yingru, 2020. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation," Applied Energy, Elsevier, vol. 263(C).
    13. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
    14. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    15. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    16. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    17. Wei Chen & Yongle Tian & Kaiming Zheng & Nana Wan, 2023. "Influences of mechanisms on investment in renewable energy storage equipment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12569-12595, November.
    18. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    19. Portilla-Paveri, Manuel & Cariaga, Denise & Negrete-Pincetic, Matías & Lorca, Álvaro & Anjos, Miguel F., 2024. "A long-term generation and transmission expansion planning model considering desalination flexibility and coordination: A Chilean case study," Applied Energy, Elsevier, vol. 371(C).
    20. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:17:y:2025:i:11:p:4938-:d:1665951. 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.