A modified hybrid prediction method for spinning reserve requirement

Ancillary services is used to refer to a variety of operations beyond generation and transmission which are requested to maintain grid stability, security and reliability of power system. These services generally consist, frequency control, Spinning Reserves (SR) and operating reserves. Accordingly, an accurate day ahead forecast of SR requirement helps the Independent System Operator (ISO) to manage a reliable and economic operation of the power system. This prediction model needs a strong and accurate method to tackle the complexity, non-stationarity and volatility due to the load profile or equipment fault. Hence, a new hybrid forecasting model is proposed in this paper, to solve the SR requirement. The proposed structure consists of three stage Neural Network (NN) based forecast engine with different learning algorithms. Also, the input signal of this forecast engine is filtered by a new Improved Fuzzy Clustering Mechanism (IFCM) to find the high relevancy and low redundancy of features. The proposed strategy is implemented and tested on real data from Pennsylvania-New Jersey-Maryland (PJM) and is compared with other techniques. The resulting numerical results demonstrate the validity of proposed method.