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Power load demand response potential of secondary sectors in China: The case of western Inner Mongolia

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  • Pang, Yuexia
  • He, Yongxiu
  • Jiao, Jie
  • Cai, Hua

Abstract

Existing literature shows that understanding demand response potential plays a key role to promote renewable energy consumption and improve power system flexibility. However, our knowledge is limited in the power shifting technical demand response potential covering all the secondary sectors and the achievable demand response potential of secondary sectors. To address this gap, this study aims to: (1) design a questionnaire to assess electricity load shifting technical demand response potential and achievable demand response potential; (2) conduct a case study of western Inner Mongolia. Covering all the secondary sectors; and (3) study demand response potential using the methods of qualitative analysis based on power load curves and face to face interviews with local enterprises, and quantitative statistics of questionnaire responses. The results show that, in secondary sectors, over half of the enterprises’ power load shifting technical demand response potential is less than 10%. Additionally, approximately three-quarters of enterprises in secondary sectors will use other energy sources to replace electricity when electricity sale price increases by 30%. These results can help power companies and government to identify adjusted power load potential and corresponding main consumers for demand response strategies to promote the utility grid safety and renewable energy consumption.

Suggested Citation

  • Pang, Yuexia & He, Yongxiu & Jiao, Jie & Cai, Hua, 2020. "Power load demand response potential of secondary sectors in China: The case of western Inner Mongolia," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323643
    DOI: 10.1016/j.energy.2019.116669
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    References listed on IDEAS

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    2. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
    3. Lu, Qing & Zhang, Yufeng, 2022. "A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price," Energy, Elsevier, vol. 240(C).
    4. Xu, Qingyang & Sun, Feihu & Cai, Qiran & Liu, Li-Jing & Zhang, Kun & Liang, Qiao-Mei, 2022. "Assessment of the influence of demand-side responses on high-proportion renewable energy system: An evidence of Qinghai, China," Renewable Energy, Elsevier, vol. 190(C), pages 945-958.
    5. Xiao, Hui & Cao, Minhao, 2020. "Balancing the demand and supply of a power grid system via reliability modeling and maintenance optimization," Energy, Elsevier, vol. 210(C).

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