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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Content Provider	Springer Nature Link
Author	Kaur, Simarjit Verma, Yajvender Pal Agrawal, Sunil
Copyright Year	2013
Abstract	In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.
Starting Page	468
Ending Page	478
Page Count	11
File Format	PDF
ISSN	20951701
Journal	Frontiers of Energy and Power Engineering in China
Volume Number	7
Issue Number	4
e-ISSN	20951698
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-12-06
Publisher Institution	Chinese Universities
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	artificial neural network (ANN) frequency prediction availability-based tariff (ABT) generation scheduling (GS) Energy Technology
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology

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