Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market
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) is one of the most prominent te...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9730881/ |
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| Summary: | 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) 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 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. |
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| ISSN: | 2169-3536 |