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 mel axolotl to congestion, which is not desirable.In a day ahead or spot power market, generation rescheduling (GR) 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 here 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 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.