Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm. Issue 22 (29th October 2019)

• Record Type: Journal Article
• Authors: Muhammad, Munir Azam; Mokhlis, Hazlie; Amin, Adil; Naidu, Kanendra; Franco, John Fredy; Wang, Li; Othman, Mohamadariff
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 22(2019)
• Page Start: 5071
• Page End: 5082
• Publication Date: 2019-10-29
• Subjects: optimisation -- power distribution planning -- combinatorial mathematics -- distributed power generation; simultaneous network reconfiguration -- Hamming dataset approach -- firefly algorithm -- electrical industry -- utility companies -- power supply -- voltage deviation minimization -- power loss -- DNR -- distributed generation -- complex combinatorial problem -- DG sizing -- increased voltage deviation -- simultaneous optimal integration -- explicit radiality verification -- large‐scale distribution network -- implicit radiality verification; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.0264
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near‐optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large‐scale distribution network. The results show that the proposed technique is able to consistently attain near optimal‐solutions.