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Using smart meter data to estimate demand response potential, with application to solar energy integration

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  • Dyson, Mark E.H.
  • Borgeson, Samuel D.
  • Tabone, Michaelangelo D.
  • Callaway, Duncan S.

Abstract

This paper presents a new method for estimating the demand response potential of residential air conditioning (A/C), using hourly electricity consumption data (“smart meter” data) from 30,000 customer accounts in Northern California. We apply linear regression and unsupervised classification methods to hourly, whole-home consumption and outdoor air temperature data to determine the hours, if any, that each home׳s A/C is active, and the temperature dependence of consumption when it is active. When results from our sample are scaled up to the total population, we find a maximum of 270–360MW (95% c.i.) of demand response potential over a 1-h duration with a 4°F setpoint change, and up to 3.2–3.8GW of short-term curtailment potential. The estimated resource correlates well with the evening decline of solar production on hot, summer afternoons, suggesting that demand response could potentially act as reserves for the grid during these periods in the near future with expected higher adoption rates of solar energy. Additionally, the top 5% of homes in the sample represent 40% of the total MW-hours of DR resource, suggesting that policies and programs to take advantage of this resource should target these high users to maximize cost-effectiveness.

Suggested Citation

  • Dyson, Mark E.H. & Borgeson, Samuel D. & Tabone, Michaelangelo D. & Callaway, Duncan S., 2014. "Using smart meter data to estimate demand response potential, with application to solar energy integration," Energy Policy, Elsevier, vol. 73(C), pages 607-619.
    • Handle: RePEc:eee:enepol:v:73:y:2014:i:c:p:607-619
    DOI: 10.1016/j.enpol.2014.05.053
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    8. Neves, Diana & Pina, André & Silva, Carlos A., 2018. "Assessment of the potential use of demand response in DHW systems on isolated microgrids," Renewable Energy, Elsevier, vol. 115(C), pages 989-998.
    9. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    10. Mathieu, Johanna L. & Dyson, Mark E.H. & Callaway, Duncan S., 2015. "Resource and revenue potential of California residential load participation in ancillary services," Energy Policy, Elsevier, vol. 80(C), pages 76-87.
    11. Jack, M.W. & Suomalainen, K. & Dew, J.J.W. & Eyers, D., 2018. "A minimal simulation of the electricity demand of a domestic hot water cylinder for smart control," Applied Energy, Elsevier, vol. 211(C), pages 104-112.
    12. Ayu Washizu & Satoshi Nakano & Hideo Ishii & Yasuhiro Hayashi, 2019. "Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo," Sustainability, MDPI, vol. 11(17), pages 1-20, September.
    13. Soares, N. & Martins, A.G. & Carvalho, A.L. & Caldeira, C. & Du, C. & Castanheira, É. & Rodrigues, E. & Oliveira, G. & Pereira, G.I. & Bastos, J. & Ferreira, J.P. & Ribeiro, L.A. & Figueiredo, N.C. & , 2018. "The challenging paradigm of interrelated energy systems towards a more sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 171-193.
    14. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    15. Jack, M.W. & Mirfin, A. & Anderson, B., 2021. "The role of highly energy-efficient dwellings in enabling 100% renewable electricity," Energy Policy, Elsevier, vol. 158(C).
    16. Gordon Rausser & Wadim Strielkowski & Dalia Å treimikienÄ—, 2018. "Smart meters and household electricity consumption: A case study in Ireland," Energy & Environment, , vol. 29(1), pages 131-146, February.
    17. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
    18. Song, Zhaofang & Shi, Jing & Li, Shujian & Chen, Zexu & Jiao, Fengshun & Yang, Wangwang & Zhang, Zitong, 2022. "Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads," Applied Energy, Elsevier, vol. 307(C).
    19. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.

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