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Incentive-based integrated demand response with multi-energy time-varying carbon emission factors

Author

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  • Ma, Siyu
  • Liu, Hui
  • Wang, Ni
  • Huang, Lidong
  • Su, Jinshuo
  • Zhao, Teyang

Abstract

The demand response, as an effective means of carbon reduction in the demand side, generally uses carbon emission factor (CEF) to evaluate the carbon emissions. However, in the existing research, the output of demand-side renewable energy units is neglected in CEF, which will lead to an inaccurate assessment of carbon emissions and is not helpful for the carbon reduction by the demand response. In this paper, a bi-level incentive-based integrated demand response (IBIDR) approach is proposed to reduce the carbon emission cost with multi-energy time-varying CEFs. In particular, the multi-energy time-varying CEFs model is proposed considering the demand-side proportion of solar output in the total energy consumption. To explore the role of CEF in IBIDR, the coupling effect of the multi-energy time-varying CEFs on the response willingness model is constructed to promote the interaction among different energy resources to reduce the carbon emission. Finally, the proposed bi-level IBIDR optimization model is transformed into a single-level nonlinear problem based on the backward induction method, which can obtain the optimal incentive strategy of IBIDR. Simulation results show the effectiveness of the proposed approach in reducing the incentive cost and carbon emission cost of multi-energy aggregators.

Suggested Citation

  • Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Su, Jinshuo & Zhao, Teyang, 2024. "Incentive-based integrated demand response with multi-energy time-varying carbon emission factors," Applied Energy, Elsevier, vol. 359(C).
  • Handle: RePEc:eee:appene:v:359:y:2024:i:c:s0306261924001466
    DOI: 10.1016/j.apenergy.2024.122763
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    References listed on IDEAS

    as
    1. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    3. Wenjin Chen & Jun Zhang & Feng Li & Ruoyi Zhang & Sennan Qi & Guoqing Li & Chong Wang, 2023. "Low Carbon Economic Dispatch of Integrated Energy System Considering Power-to-Gas Heat Recovery and Carbon Capture," Energies, MDPI, vol. 16(8), pages 1-19, April.
    4. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    5. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
    6. 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).
    7. Lei, Dayong & Zhang, Zhonghui & Wang, Zhaojun & Zhang, Liuyu & Liao, Wei, 2023. "Long-term, multi-stage low-carbon planning model of electricity-gas-heat integrated energy system considering ladder-type carbon trading mechanism and CCS," Energy, Elsevier, vol. 280(C).
    8. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    9. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
    10. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "Low-carbon economic dispatch and energy sharing method of multiple Integrated Energy Systems from the perspective of System of Systems," Energy, Elsevier, vol. 244(PA).
    11. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
    12. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    13. Wang, Shouxiang & Wang, Shaomin & Zhao, Qianyu & Dong, Shuai & Li, Hao, 2023. "Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand," Energy, Elsevier, vol. 269(C).
    14. Kim, Jin-Ho & Shcherbakova, Anastasia, 2011. "Common failures of demand response," Energy, Elsevier, vol. 36(2), pages 873-880.
    15. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    16. Wu, Min & Xu, Jiazhu & Shi, Zhenglu, 2023. "Low carbon economic dispatch of integrated energy system considering extended electric heating demand response," Energy, Elsevier, vol. 278(PA).
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