Probabilistic short-term transmission-loss forecasting based on a two-point estimate method

In a competitive electricity market, system operators are required to procure certain ancillary services, which may among others include compensation of active-power losses. The compensation is most often done as a yearly, monthly and daily purchase of energy. The yearly and monthly bulk purchases do not take daily variations into account and daily purchases are required to cover the discrepancies between them. This requires an accurate and fast short-term forecasting method that has to be efficiently applicable in day-ahead markets. This paper presents a novel probabilistic short-term transmission-loss forecast method. Specifically, the method includes deterministic short-term load, generation and power transit forecasts as well as network configuration forecasts, which can be used for deterministic power-flow calculations to forecast transmission losses. However, the uncertainty of system loading conditions and inherent nonlinearities in power systems may cause inaccurate transmission-loss forecasts. By using deterministic forecasts, no additional information as to the possible forecast deviations can be given, as transmission losses do not show a clear correlation with these uncertainties. To account for the uncertainties, probabilistic power flow approach is proposed to define the probability distribution of the forecasted losses, which may help system operators to decide on the most efficient strategy on the day-ahead market. Hong´s point-estimate method is used to solve the probabilistic power flow problem. The proposed approach has been verified by using real data for the ENTSO-E interconnection and tested for the Slovenian power system. The forecasting results demonstrate the usefulness of the proposed approach.