An artificial neural network based transmission loss allocation for bilateral contracts

The introduction of deregulation and the subsequent open access policy in electricity sector have opened up the door for power transactions between generators and bulk consumers under many different market-driven contractual forms including bilateral contracts. Long-term bilateral contracts are attractive to many parties who want to avoid price volatility. With bilateral contracts it becomes necessary to allocate transmission loss to respective transactions. An artificial neural network based transmission loss allocation method is presented in this paper. The method is computationally efficient and can provide solutions on a real-time basis. Most independent system variables can be used as inputs to this neural network which in turn makes the loss allocation process responsive to practical situations. Training and testing of this network have been done with the help of the IEEE 24-bus test system. A technique has been developed to expedite the convergence and to improve the accuracy of the results. Numerical examples on loss allocations for both peak and off-peak hours have been provided and compared with those obtained using another technique.