Title :
Multi-layer feedforward neural network-based ATC estimator
Author :
Ying-Yi Hong ; Chien-Yang Hsiao
Author_Institution :
Chung Yuan Univ., Chungli
Abstract :
Deregulation has a great impact on the electric power industry around the world. Bilateral contract is one of the transaction modes in deregulated markets. The independent system operator (ISO), responsible for ensuring the secure, economic, and efficient dispatches, has to provide the information about the available transfer capability (ATC) for bilateral contract customers. However, a large number of calculations based on power flow approach is required to calculate the ATC. In this paper, a method based on multi-layer feedforward neural network (MFNN) and generation shift factor (GSF) as well as outage transfer distribution factor (OTDF) is used to estimate the ATC. The simulation results obtained from a 6-bus and the IEEE 30-bus system show the applicability of the proposed method.
Keywords :
electricity supply industry deregulation; load flow; multilayer perceptrons; power engineering computing; ATC estimator; IEEE 30-bus system; available transfer capability; bilateral contract; bilateral contract customers; deregulation; electric power industry; generation shift factor; independent system operator; multilayer feedforward neural network; outage transfer distribution factor; power flow approach; transaction modes; Contracts; Economic forecasting; Feedforward neural networks; ISO; Load flow; Multi-layer neural network; Neural networks; Power generation economics; Power system dynamics; Power system economics; Available Transfer Capability; Deregulation; Neural Network;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524347
