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Demand response evaluation of RIES based on improved matter-element extension model

Author

Listed:
  • Wang, Yongli
  • Li, Fang
  • Yang, Jiale
  • Zhou, Minhan
  • Song, Fuhao
  • Zhang, Danyang
  • Xue, Lu
  • Zhu, Jinrong

Abstract

With the revolution of traditional economic and social patterns based on concentrated fossil energy intensive consumption, regional integrated energy systems (RIES) have gradually become an important trend for future energy consumption. As an important technical means of integrated energy demand side management, demand response helps to improve the smooth and reliable operation of the integrated energy system (IES). Meanwhile, it is of great significance to improve national energy security and the environment. This paper focuses on the demand response evaluation of RIES based on improved matter-element extension model. Firstly, with the introduction of the RIES demand response evaluation mechanism, the demand response index system of RIES is established from five dimensions: user load, user energy usage, user energy reliability, user energy consumption, and user energy habits. Based on the demand response evaluation index system, the potential demand response users of the integrated energy system are graded into four levels: rewarded user (I), encouraged user (II), general user (III), and non-encouraged user (IV).Secondly, based on the analytic hierarchy process and entropy method to obtain more accurate comprehensive weights, the RIES demand response evaluation model is established by the matter-element extension method. Finally, in order to illustrate the advantages of this method, 10 objects were selected from the regional integrated energy systems demand response project of a Chinese city for evaluation and analysis. The results show that the demand response abilities of the 10 evaluation objects are all at level I, indicating that the automatic demand response (ADR) of 10 evaluation objects is good.

Suggested Citation

  • Wang, Yongli & Li, Fang & Yang, Jiale & Zhou, Minhan & Song, Fuhao & Zhang, Danyang & Xue, Lu & Zhu, Jinrong, 2020. "Demand response evaluation of RIES based on improved matter-element extension model," Energy, Elsevier, vol. 212(C).
  • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220312287
    DOI: 10.1016/j.energy.2020.118121
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    1. Suzuki, Soushi & Nijkamp, Peter, 2016. "An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis," Energy Policy, Elsevier, vol. 88(C), pages 100-112.
    2. Wang, Yu, 2010. "The analysis of the impacts of energy consumption on environment and public health in China," Energy, Elsevier, vol. 35(11), pages 4473-4479.
    3. Shuyu Li & Rongrong Li, 2019. "Evaluating Energy Sustainability Using the Pressure-State-Response and Improved Matter-Element Extension Models: Case Study of China," Sustainability, MDPI, vol. 11(1), pages 1-20, January.
    4. Xiaomin Xu & Dongxiao Niu & Jinpeng Qiu & Meiqiong Wu & Peng Wang & Wangyue Qian & Xiang Jin, 2016. "Comprehensive Evaluation of Coordination Development for Regional Power Grid and Renewable Energy Power Supply Based on Improved Matter Element Extension and TOPSIS Method for Sustainability," Sustainability, MDPI, vol. 8(2), pages 1-17, February.
    5. 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).
    6. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Energy and uncertainty management through domestic demand response in the residential building," Energy, Elsevier, vol. 192(C).
    7. Klaassen, E.A.M. & van Gerwen, R.J.F. & Frunt, J. & Slootweg, J.G., 2017. "A methodology to assess demand response benefits from a system perspective: A Dutch case study," Utilities Policy, Elsevier, vol. 44(C), pages 25-37.
    8. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2019. "Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique," Energy, Elsevier, vol. 169(C), pages 762-775.
    Full references (including those not matched with items on IDEAS)

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