Available Transfer Capability Computation and its Augmentation Using Intelligent Techniques in Deregulated Electricity Markets

Abstract

Aim: The aim of this research is to calculate accurate and commercially viable ATC faster and to enhance ATC by optimal placement of TCSC in the power system. Materials and Methods: MLP ensemble (EMLP) is a collective effort of many MLP to answer a problem. This work proposes an EMLP to estimate the ATC using a set of truly trained MLPs, which includes best MLP, and an inferior set of MLP obtained as a byproduct during training. To the best of our knowledge, ATC estimation using ensemble is unaddressed in the literature. Proposed hybrid GWO and PSO (HGWOPSO) combines the strength of both basic PSO (exploitation) and GWO (exploration) algorithms. A novel GWO-based methodology is proposed for ATC enhancement by optimal placement of TCSC with its optimal compensation. Results and Discussion: Results shows that performance offered by EMLPAPC and TLBOEN is better than EMLP-N. However, APC-based MLP selection is the computationally expensive whereas; TLBOEN requires very few calculations as compared to EMLPAPC. The ATC results demonstrate that HGWOPSO is significantly better able to provide optimized exploration and exploitation capability compared to stand alone PSO and GWO with better and improved results in addition to better convergence rate. ATC enhancement by optimal placement of one and two TCSC device with its optimal compensation has been studied using GWO. Results obtained are competitive and show the usefulness and the fitness of the proposed GWO methodology for placement of TCSC device. Conclusions: The performance based MLP selection method is a handy tool, whereas APC-based method is most accurate. However, TLBOEN performs well, considering computation, when numbers of MLP are more. The results demonstrate that proposed HGWOPSO is significantly more able to provide optimized exploration and exploitation capability compared to stand alone PSO and GWO with competitive results. Results obtained are competitive and shows the usefulness and the fitness of the proposed TCSC placement using GWO methodology