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Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City

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  • Forbes, Kevin F.

Abstract

The increasing utilization of distributed energy resources such as behind-the-meter solar has given rise to a grid-supplied load profile conditional on solar conditions that are difficult to forecast using conventional methods. As a result, the accuracy of the load forecasts is reduced, representing a possible operational challenge to system operators. This paper presents a method to resolve this challenge using New York City electricity zone data. The analysis presented in this paper demonstrates that the load forecasting challenges associated with behind-the-meter solar can be successfully addressed using econometric time-series methods and better forecast information.

Suggested Citation

  • Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:juipol:v:80:y:2023:i:c:s0957178722001114
    DOI: 10.1016/j.jup.2022.101447
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    References listed on IDEAS

    as
    1. Forbes, Kevin F. & Zampelli, Ernest M., 2019. "Wind energy, the price of carbon allowances, and CO2 emissions: Evidence from Ireland," Energy Policy, Elsevier, vol. 133(C).
    2. Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Kevin F. Forbes and Ernest M. Zampelli, 2014. "Do Day-Ahead Electricity Prices Reflect Economic Fundamentals? Evidence from the California ISO," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    5. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    6. Forbes, Kevin F. & Zampelli, Ernest M., 2020. "Accuracy of wind energy forecasts in Great Britain and prospects for improvement," Utilities Policy, Elsevier, vol. 67(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Solar energy forecasting; Load energy forecasting; Duck curve; Electricity markets; Distributed energy resources; Behind-the-meter solar generation;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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