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

    Authors

    M. Guegan; C. B. Uvo;K. Madani

    Comments

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    Abbreviated Journal Title

    Energy Policy

    Keywords

    Climate change; Hydropower; Artificial Neural Network; ARTIFICIAL NEURAL-NETWORKS; GLOBAL OPTIMIZATION; MODELS; PREDICTION; Energy & Fuels; Environmental Sciences; Environmental Studies

    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. (C) 2011 Elsevier Ltd. All rights reserved.

    Journal Title

    Energy Policy

    Volume

    42

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    261

    Last Page

    271

    WOS Identifier

    WOS:000301616000028

    ISSN

    0301-4215

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