IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3092-d1420476.html
   My bibliography  Save this article

A Multi-Type Dynamic Response Control Strategy for Energy Consumption

Author

Listed:
  • Lantao Jing

    (Shenyang Institute of Engineering, Shenyang 110000, China)

  • Enyu Wei

    (Shenyang Institute of Engineering, Shenyang 110000, China)

  • Liang Wang

    (Shenyang Institute of Engineering, Shenyang 110000, China)

  • Jinkuo Li

    (China Energy Construction Group Liaoning Electric Power Survey and Design Institute Co., Ltd., Shenyang 110000, China)

  • Qiang Zhang

    (State Grid Liaoning Electric Power Academy, Shenyang 110000, China)

Abstract

In the context of the “Dual-Carbon Strategy”, the seamless integration and optimal utilization of renewable energy sources present a pressing challenge for the emerging power system. The advent of demand-side response technology offers a promising solution to this challenge. This study proposes a two-stage response control strategy for multiple DR clusters based on the specific response time characteristics of industrial and residential loads. The strategy enhances the utilization rate of wind power, harnesses the joint response capability of various types of loads on the demand side, and ensures the overall revenue of the load aggregator (LA). It underscores the importance of industrial loads in large-scale energy consumption control throughout the overall consumption response process, while residential load clusters exhibit quick response flexibility. A homogeneous energy consumption sorting unit response strategy is established from the perspective of a residential load variable-frequency air conditioning cluster unit. This strategy addresses the challenge faced by industrial electrolytic aluminum plants in coping with long-term response intervals amidst significant fluctuations in wind power consumption demand, which may lead to incomplete consumption. This study constructs a response model based on industrial and residential time-sharing tariffs, as well as the aggregator consumption penalty price, with the optimal load energy economy index serving as the evaluation criterion. A series of simulations are conducted to comprehensively evaluate the energy consumption of the two load clusters at all times and the total revenue of the aggregator in the response zone. The objective is to achieve a win–win situation for the total wind power energy consumption rate and the aggregator’s economy. The results of the simulations demonstrate that the response control strategy proposed in this study enhances the overall energy consumption rate by nearly 4 percentage points compared to a single industrial cluster. The total benefit of the load aggregator can reach CNY 941,732.09. The consumption response scheduling strategy put forward in this paper bolsters wind power consumption, triggers demand response, and significantly propels the comprehensive construction and development of the dual-high power grid.

Suggested Citation

  • Lantao Jing & Enyu Wei & Liang Wang & Jinkuo Li & Qiang Zhang, 2024. "A Multi-Type Dynamic Response Control Strategy for Energy Consumption," Energies, MDPI, vol. 17(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3092-:d:1420476
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3092/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/13/3092/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Woltmann, Stefan & Kittel, Julia, 2022. "Development and implementation of multi-agent systems for demand response aggregators in an industrial context," Applied Energy, Elsevier, vol. 314(C).
    2. Silvestri, Luca & De Santis, Michele, 2024. "Renewable-based load shifting system for demand response to enhance energy-economic-environmental performance of industrial enterprises," Applied Energy, Elsevier, vol. 358(C).
    3. Guerra, K. & Haro, P. & Gutiérrez, R.E. & Gómez-Barea, A., 2022. "Facing the high share of variable renewable energy in the power system: Flexibility and stability requirements," Applied Energy, Elsevier, vol. 310(C).
    4. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    5. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
    6. Tostado-Véliz, Marcos & Hasanien, Hany M. & Jordehi, Ahmad Rezaee & Turky, Rania A. & Jurado, Francisco, 2023. "Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue," Applied Energy, Elsevier, vol. 339(C).
    7. Liu, Yang & Mauter, Meagan S., 2020. "Assessing the demand response capacity of U.S. drinking water treatment plants," Applied Energy, Elsevier, vol. 267(C).
    8. Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(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. Xiang, Yue & Guo, Yongtao & Wu, Gang & Liu, Junyong & Sun, Wei & Lei, Yutian & Zeng, Pingliang, 2022. "Low-carbon economic planning of integrated electricity-gas energy systems," Energy, Elsevier, vol. 249(C).
    2. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    3. Brantley Liddle, 2024. "To What Extent Do Alternative Energy Sources Displace Coal and Oil in Electricity Generation? A Mean-Group Panel Analysis," Sustainability, MDPI, vol. 16(13), pages 1-11, June.
    4. Gutiérrez-Alvarez, R. & Guerra, K. & Haro, P., 2023. "Market profitability of CSP-biomass hybrid power plants: Towards a firm supply of renewable energy," Applied Energy, Elsevier, vol. 335(C).
    5. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    6. Ádám Sleisz & Dániel Divényi & Beáta Polgári & Péter Sőrés & Dávid Raisz, 2022. "A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation," Energies, MDPI, vol. 15(22), pages 1-18, November.
    7. Yang, Kuang & Liao, Haifan & Xu, Bo & Chen, Qiuxiang & Hou, Zhenghui & Wang, Haijun, 2024. "Data-driven dryout prediction in helical-coiled once-through steam generator: A physics-informed approach leveraging the Buckingham Pi theorem," Energy, Elsevier, vol. 294(C).
    8. Rodica Loisel & Lionel Lemiale & Silvana Mima & Adrien Bidaud, 2022. "Strategies for short-term intermittency in long-term prospective scenarios in the French power system," Post-Print hal-04568072, HAL.
    9. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Wang, Bowen & Wang, Dongzhe & Shang, Jingru & Zhao, Yiru, 2024. "Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition," Energy, Elsevier, vol. 302(C).
    10. Gyuhyeon Bae & Ahyun Yoon & Sungsoo Kim, 2024. "Incentive Determination for Demand Response Considering Internal Rate of Return," Energies, MDPI, vol. 17(22), pages 1-15, November.
    11. Jesús Fraile Ardanuy & Roberto Alvaro-Hermana & Sandra Castano-Solis & Julia Merino, 2022. "Carbon-Free Electricity Generation in Spain with PV–Storage Hybrid Systems," Energies, MDPI, vol. 15(13), pages 1-20, June.
    12. Loisel, Rodica & Lemiale, Lionel & Mima, Silvana & Bidaud, Adrien, 2022. "Strategies for short-term intermittency in long-term prospective scenarios in the French power system," Energy Policy, Elsevier, vol. 169(C).
    13. Sara Lumbreras & Jesús David Gómez & Erik Francisco Alvarez & Sebastien Huclin, 2022. "The Human Factor in Transmission Network Expansion Planning: The Grid That a Sustainable Energy System Needs," Sustainability, MDPI, vol. 14(11), pages 1-22, May.
    14. Luchnikov, I. & Métivier, D. & Ouerdane, H. & Chertkov, M., 2021. "Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation," Applied Energy, Elsevier, vol. 285(C).
    15. E. Ruben van Beesten & Daan Hulshof, 2022. "Economic incentives for capacity reductions on interconnectors in the day-ahead market," Papers 2210.07129, arXiv.org.
    16. Xie, Rui & Wei, Wei & Li, Mingxuan & Dong, ZhaoYang & Mei, Shengwei, 2023. "Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach," Energy, Elsevier, vol. 263(PA).
    17. van Beesten, E. Ruben & Hulshof, Daan, 2023. "Economic incentives for capacity reductions on interconnectors in the day-ahead market," Applied Energy, Elsevier, vol. 341(C).
    18. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Zhou, Yuekuan & Mansouri, Seyed Amir & Jurado, Francisco, 2024. "Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations," Renewable Energy, Elsevier, vol. 225(C).
    19. Helder Pereira & Bruno Ribeiro & Luis Gomes & Zita Vale, 2022. "Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    20. Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.

    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:13:p:3092-:d:1420476. 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.