期刊
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
卷 61, 期 2, 页码 251-260出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEM.2013.2284386
关键词
Electricity demand; k-means clustering; simulations; wind generation
资金
- Alan Howard Charitable Trust
- Engineering and Physical Sciences Research Council, via the Supergen Flexnet Consortium [EP/E04011X/1]
We use a k-means clustering algorithm to partition national electricity demand data for Great Britain and apply a novel profiling method to obtain a set of representative demand profiles for each year over the period 1994-2005. We then use a simulated dispatch model to assess the accuracy of these daily profiles against the complete dataset on a year-to-year basis. We find that the use of data partitioning does not compromise the accuracy of the simulations for most of the main variables considered, even when simulating significant intermittent wind generation. This technique yields 50-fold gains in terms of computational speed, allowing complex Monte Carlo simulations and sensitivity analyses to be performed with modest computing resource.
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