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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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    6. Meredith Fowlie & Michael Greenstone & Catherine Wolfram, 2018. "Do Energy Efficiency Investments Deliver? Evidence from the Weatherization Assistance Program," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1597-1644.
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    9. 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.
    10. 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.
    11. 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.
    12. Cécile Hediger, 2022. "Rebound effects in residential heating: How much does an extra degree matter?," IRENE Working Papers 22-05, IRENE Institute of Economic Research.
    13. 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.
    14. 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.
    15. Yehui Li & Dalin Qin & H. Vincent Poor & Yi Wang, 2024. "Introducing edge intelligence to smart meters via federated split learning," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    16. 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).
    17. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    18. 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.
    19. 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.
    20. 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).

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