Analysis of the value for unit commitment of improved load forecasts

Load forecast errors can yield suboptimal unit commitment decisions. The ec onomic cost of inaccurate forecasts is assessed by a combination of forecas t simulation, unit commitment optimization, and economic dispatch modeling for several different generation/load systems. The forecast simulation pres erves the error distributions and correlations actually experienced by user s of a neural net-based forecasting system. Underforecasts result in purcha ses of expensive peaking or spot market power; overforecasts inflate start- up and fixed costs because too much capacity is committed. The value of imp roved accuracy is found to depend on load and generator characteristics; fo r the systems considered here, a reduction of 1% in mean absolute percentag e 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 s impler methods. A conservative estimate is that a 1% reduction in forecasti ng error far a 10,000 MW utility can save up to $1.6 million annually.