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Developing a module for estimating climate warming effects on hydropower pricing in California

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  • Guégan, Marion
  • Uvo, Cintia B.
  • Madani, Kaveh

Abstract

Climate warming is expected to alter hydropower generation in California through affecting the annual stream-flow regimes and reducing snowpack. On the other hand, increased temperatures are expected to increase hydropower demand for cooling in warm periods while decreasing demand for heating in winter, subsequently altering the annual hydropower pricing patterns. The resulting variations in hydropower supply and pricing regimes necessitate changes in reservoir operations to minimize the revenue losses from climate warming. Previous studies in California have only explored the effects of hydrological changes on hydropower generation and revenues. This study builds a long-term hydropower pricing estimation tool, based on artificial neural network (ANN), to develop pricing scenarios under different climate warming scenarios. Results suggest higher average hydropower prices under climate warming scenarios than under historical climate. The developed tool is integrated with California's Energy-Based Hydropower Optimization Model (EBHOM) to facilitate simultaneous consideration of climate warming on hydropower supply, demand and pricing. EBHOM estimates an additional 5% drop in annual revenues under a dry warming scenario when climate change impacts on pricing are considered, with respect to when such effects are ignored, underlining the importance of considering changes in hydropower demand and pricing in future studies and policy making.

Suggested Citation

  • Guégan, Marion & Uvo, Cintia B. & Madani, Kaveh, 2012. "Developing a module for estimating climate warming effects on hydropower pricing in California," Energy Policy, Elsevier, vol. 42(C), pages 261-271.
    • Handle: RePEc:eee:enepol:v:42:y:2012:i:c:p:261-271
    DOI: 10.1016/j.enpol.2011.11.083
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

    1. Gaudard, Ludovic & Madani, Kaveh, 2019. "Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?," Energy Policy, Elsevier, vol. 126(C), pages 22-29.
    2. Bortoluzzi, Mirian & Furlan, Marcelo & dos Reis Neto, José Francisco, 2022. "Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning," Renewable Energy, Elsevier, vol. 200(C), pages 1316-1326.

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