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Forecast value considering energy pricing in California

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  • Luoma, Jennifer
  • Mathiesen, Patrick
  • Kleissl, Jan

Abstract

In this study, production forecast value is investigated using day-ahead market (DAM) and real-time market (RTM) locational marginal prices (LMP) at 63 sites in California. Using the North American Mesoscale (NAM) Model, day-ahead global horizontal irradiance (GHI) forecasts are established and converted to power assuming that a 1MW solar photovoltaic plant is co-located at each observation site. Using this forecast, energy is hypothetically sold in the DAM. As the RTM occurs, deviations between forecast and observation are settled by hypothetically purchasing or selling energy at the RTM price. Total revenue is calculated by the sum of these two transactions.

Suggested Citation

  • Luoma, Jennifer & Mathiesen, Patrick & Kleissl, Jan, 2014. "Forecast value considering energy pricing in California," Applied Energy, Elsevier, vol. 125(C), pages 230-237.
    • Handle: RePEc:eee:appene:v:125:y:2014:i:c:p:230-237
    DOI: 10.1016/j.apenergy.2014.03.061
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    References listed on IDEAS

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    1. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
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    Cited by:

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    2. Lion Hirth, Falko Ueckerdt, and Ottmar Edenhofer, 2016. "Why Wind Is Not Coal: On the Economics of Electricity Generation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    3. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    4. Hirth, Lion, 2016. "The benefits of flexibility: The value of wind energy with hydropower," Applied Energy, Elsevier, vol. 181(C), pages 210-223.
    5. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    6. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    7. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    8. Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    9. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    10. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    11. Mills, Andrew D. & Wiser, Ryan H., 2015. "Strategies to mitigate declines in the economic value of wind and solar at high penetration in California," Applied Energy, Elsevier, vol. 147(C), pages 269-278.
    12. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    13. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).

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