Analysis of the value for unit commitment of improved load forecasts

• Published in: IEEE transactions on power systems Vol. 14; no. 4; pp. 1342 - 1348
• Main Authors: Hobbs, B.F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K.A., Maratukulam, D.J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-1999
• Subjects: ACCURACY; Computer simulation; Cost engineering; Costs; Economic forecasting; ECONOMICS; ENERGY PLANNING AND POLICY; Environmental economics; Error correction; Errors; EXPERT SYSTEMS; FORECASTING; Fuel economy; LOAD ANALYSIS; Load forecasting; LOAD MANAGEMENT; Marketing; Mathematical models; NEURAL NETWORKS; Power generation economics; Power system analysis computing; Power system economics; Predictive models; Reduction; Unit commitment
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/59.801894

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.