naa a22 4500 510731716 CHVBK 20180411082955.0 cr unu---uuuuu 180411e20131201xx s 000 0 eng 10.1007/s11708-013-0282-6 doi (NATIONALLICENCE)springer-10.1007/s11708-013-0282-6 Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment [Elektronische Daten] [Simarjit Kaur, Yajvender Verma, Sunil Agrawal] 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. Higher Education Press and Springer-Verlag Berlin Heidelberg, 2013 artificial neural network (ANN) nationallicence frequency prediction nationallicence availability-based tariff (ABT) nationallicence generation scheduling (GS) nationallicence Kaur Simarjit Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut Verma Yajvender Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut Agrawal Sunil Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut Frontiers in Energy Springer Berlin Heidelberg 7/4(2013-12-01), 468-478 2095-1701 7:4<468 2013 7 11708 https://doi.org/10.1007/s11708-013-0282-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11708-013-0282-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kaur Simarjit Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut NATIONALLICENCE 700 1- Verma Yajvender Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut NATIONALLICENCE 700 1- Agrawal Sunil Department of Electrical & Electronics Engineering, UIET, Panjab University, 160014, Chandigarh, India aut NATIONALLICENCE 773 0- Frontiers in Energy Springer Berlin Heidelberg 7/4(2013-12-01), 468-478 2095-1701 7:4<468 2013 7 11708 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer