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    Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

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    Authors
    Martínez-Arellano, Giovanna
    Nolle, Lars
    Cant, Richard
    Lotfi, Ahmad
    Windmill, Christopher
    Affiliation
    Nottingham Trent University
    Issue Date
    2014-08-21
    
    Metadata
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    Abstract
    Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution numerical weather prediction models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power output that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using genetic programming and quantile regression forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events.
    Citation
    Martínez-Arellano, G., Nolle, L., Cant, R., Lotfi, A. and Windmill, C., (2014). 'Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach'. KI-Künstliche Intelligenz, 28(4), pp. 239-253.
    Publisher
    Springer Science and Business Media LLC
    Journal
    KI - Künstliche Intelligenz
    URI
    http://hdl.handle.net/10545/625223
    DOI
    10.1007/s13218-014-0322-3
    Additional Links
    http://irep.ntu.ac.uk/id/eprint/4093/
    https://link.springer.com/article/10.1007%2Fs13218-014-0322-3
    Type
    Article
    Language
    en
    ISSN
    0933-1875
    EISSN
    1610-1987
    ae974a485f413a2113503eed53cd6c53
    10.1007/s13218-014-0322-3
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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