Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Blog Article

In the open-access power market environment, the continuously varying loading and accommodation of various bilateral and multilateral transactions, sometimes leads to congestion, which is not desirable.In a day ahead or spot power market, generation rescheduling (GR) moen finney is one of the most prominent techniques to be adopted by the system operator (SO) to release congestion.In this paper, a novel hybrid Deep Neural Network (NN) is developed for projecting rescheduled generation dispatches at all the generators.The proposed hybrid Deep Neural Network is a cascaded combination of modified back-propagation (BP) algorithm based ANN as screening module and Deep NN as GR module.

The screening module segregates the congested and non-congested loading scenarios resulting due to bilateral/multilateral transactions, efficiently and accurately.However, the GR module projects the re-scheduled active power dispatches at all the generating units at minimum congestion cost for all unseen congested loading scenarios instantly.The present approach provides a ready/instantaneous solution to manage congestion in a spot power market.During the training, the Root Mean Square Error (RMSE) is evaluated and minimized.

The effectiveness of the proposed method has been demonstrated here on the IEEE 30-bus system.The maximum error incurred during the testing phase is found 1.191% which is within the acceptable accuracy limits.