A risk assessment on local demand forecast uncertainty is presented. The aim is to highlight high-risk periods over different lengths of time and daily value-at-risk (VAR) due to load forecast errors. A number of load forecasts have been performed, and the load forecast is based on ARIMA models and ANN structures. With the residuals from load forecasting, the risk indexes over different time periods and seasons are formed. Moreover, a new methodology using the standard deviation of load increment on evaluating the risk is proposed. In contrast with the standard forecasting method that relies on a sophisticated forecast procedure, the new approach provides a useful and fast method to evaluate the risk due to load forecast uncertainty for a variety of local demand profiles. Finally, the VAR methodology combined with the NETA system is applied to a local electricity supplier in the UK.
|Number of pages||9|
|Journal||IEE Proceedings Generation Transmission and Distribution|
|Publication status||Published - Sep 2003|
- load forecasting
- neural nets
- power system analysis computing
- risk management