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Analysis of the value for unit commitment of improved load forecasts

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6 Author(s)
Hobbs, B.F. ; Dept. of Geogr. & Environ. Eng., Johns Hopkins Univ., Baltimore, MD, USA ; Jitprapaikulsarn, S. ; Konda, S. ; Chankong, V.
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Load forecast errors can yield suboptimal unit commitment decisions. The economic cost of inaccurate forecasts is assessed by a combination of forecast simulation, unit commitment optimization, and economic dispatch modeling for several different generation/load systems. The forecast simulation preserves the error distributions and correlations actually experienced by users of a neural net-based forecasting system. Underforecasts result in purchases of expensive peaking or spot market power; overforecasts inflate start-up and fixed costs because too much capacity is committed. The value of improved accuracy is found to depend on load and generator characteristics; for the systems considered here, a reduction of 1% in mean absolute percentage error (MAPE) decreases variable generation costs by approximately 0.1%-0.3% when MAPE is in the range of 3%-5%. These values are broadly consistent with the results of a survey of 19 utilities, using estimates obtained by simpler methods. A conservative estimate is that a 1% reduction in forecasting error for a 10,000 MW utility can save up to $1.6 million annually

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Power Systems, IEEE Transactions on  (Volume:14 ,  Issue: 4 )