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Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China

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  • He, Yongxiu
  • Wang, Bing
  • Wang, Jianhui
  • Xiong, Wei
  • Xia, Tian

Abstract

Demand response to time-varying pricing of electricity is critical to a smart grid's efficient management of electrical resources. This paper presents a new approach to quantify residential demand responsiveness to (time-of-use) TOU rates, which does not entail an econometric estimation of TOU demand equations. Based on one of the four smart grid pilots in China, our approach uses the survey data collected in 2011 from 236 residents in Yinchuan to implement a Monte Carlo simulation to obtain the minimum, expected and maximum demand responsiveness to four TOU rate designs. We find that residents do not respond to TOU pricing when the TOU rate design only causes a 10% increase in their existing electricity bills under non-TOU rates. However, their estimated peak demand responsiveness is 8.41% (21.26%) when the peak-time price increases by 20% (40%). Based on these findings, we conclude that suitably designed TOU rates are useful to the efficient operation of a smart grid.

Suggested Citation

  • He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    • Handle: RePEc:eee:energy:v:47:y:2012:i:1:p:230-236
    DOI: 10.1016/j.energy.2012.08.046
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    9. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
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    16. Bo wang & Nana Deng & Wenhui Zhao & Zhaohua Wang, 2022. "Residential power demand side management optimization based on fine-grained mixed frequency data," Annals of Operations Research, Springer, vol. 316(1), pages 603-622, September.
    17. Rout, Auroshis & Sahoo, Sudhansu S. & Thomas, Sanju, 2018. "Risk modeling of domestic solar water heater using Monte Carlo simulation for east-coastal region of India," Energy, Elsevier, vol. 145(C), pages 548-556.
    18. Li, Lanlan & Gong, Chengzhu & Tian, Shizhong & Jiao, Jianling, 2016. "The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation," Energy, Elsevier, vol. 96(C), pages 48-58.
    19. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    20. Yu, Biying & Sun, Feihu & Chen, Chen & Fu, Guanpeng & Hu, Lin, 2022. "Power demand response in the context of smart home application," Energy, Elsevier, vol. 240(C).
    21. Yahia, Z. & Pradhan, A., 2018. "Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis," Energy, Elsevier, vol. 163(C), pages 15-26.
    22. Jeon, Chanwoong & Lee, Jeongjin & Shin, Juneseuk, 2015. "Optimal subsidy estimation method using system dynamics and the real option model: Photovoltaic technology case," Applied Energy, Elsevier, vol. 142(C), pages 33-43.
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    24. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
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