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Modelling weather effects for impact analysis of residential time-of-use electricity pricing

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  • Miller, Reid
  • Golab, Lukasz
  • Rosenberg, Catherine

Abstract

Analyzing the impact of pricing policies such as time-of-use (TOU) is challenging in the presence of confounding factors such as weather. Motivated by a lack of consensus and model selection details in prior work, we present a methodology for modelling the effect of weather on residential electricity demand. The best model is selected according to explanatory power, out-of-sample prediction accuracy, goodness of fit and interpretability. We then evaluate the effect of mandatory TOU pricing in a local distribution company in southwestern Ontario, Canada. We use a smart meter dataset of over 20,000 households which is particularly suited to our analysis: it contains data from the summer before and after the implementation of TOU pricing in November 2011, and all customers transitioned from tiered rates to TOU rates at the same time. We find that during the summer rate season, TOU pricing results in electricity conservation across all price periods. The average demand change during on-peak and mid-peak periods is −2.6% and −2.4% respectively. Changes during off-peak periods are not statistically significant. These TOU pricing effects are less pronounced compared to previous studies, underscoring the need for clear, reproducible impact analyses which include full details about the model selection process.

Suggested Citation

  • Miller, Reid & Golab, Lukasz & Rosenberg, Catherine, 2017. "Modelling weather effects for impact analysis of residential time-of-use electricity pricing," Energy Policy, Elsevier, vol. 105(C), pages 534-546.
    • Handle: RePEc:eee:enepol:v:105:y:2017:i:c:p:534-546
    DOI: 10.1016/j.enpol.2017.03.015
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    References listed on IDEAS

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    Cited by:

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    3. Lanlan Li & Xinpei Song & Jingjing Li & Ke Li & Jianling Jiao, 2023. "The impacts of temperature on residential electricity consumption in Anhui, China: does the electricity price matter?," Climatic Change, Springer, vol. 176(3), pages 1-26, March.
    4. Blaschke, Maximilian J., 2022. "Dynamic pricing of electricity: Enabling demand response in domestic households," Energy Policy, Elsevier, vol. 164(C).
    5. Goulden, Murray & Spence, Alexa & Wardman, Jamie & Leygue, Caroline, 2018. "Differentiating ‘the user’ in DSR: Developing demand side response in advanced economies," Energy Policy, Elsevier, vol. 122(C), pages 176-185.
    6. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego F., 2018. "Effect of stopping hydroelectric power generation on the dynamics of electricity prices: An event study approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 456-467.
    7. Kaneko, Nanae & Fujimoto, Yu & Hayashi, Yasuhiro, 2022. "Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand: Case study of the Japanese electricity market," Applied Energy, Elsevier, vol. 313(C).
    8. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    9. Su, Yongxin & Zhou, Yao & Tan, Mao, 2020. "An interval optimization strategy of household multi-energy system considering tolerance degree and integrated demand response," Applied Energy, Elsevier, vol. 260(C).

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