Application of adaptive wavelet neural network to forecast operating reserve requirements in forward ancillary services market

Abstract

Operating reserve (OR) is a major portion of ancillary services (AS) in a competitive electricity market and need to be procured by independent system operator (ISO) , to achieve a high degree of power system reliability and security, following the major generation and transmission contingencies. Several ISOs have adopted deterministic methods to assess the OR requirements, however, such methods do not explicitly consider the unforeseen load swings and the probability of equipment outages. This paper proposes an adaptive wavelet neural network (AWNN) based two-stage approach to forecast OR requirements for both day-ahead and hour-ahead AS market in the California ISO (CAISO) controlled grid. The AWNN is a new class of feed-forward neural network with continuous wavelet function as the hidden layer node's activation function. The forecasting results for winter and summer seasons of the year 2007 are presented and compared with those obtained by feed-forward multi-layer perceptron neural network (MLPNN). It is found that AWNN based proposed method outperforms the MLPNN model.