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Title: Optimal planning of RDS considering PV uncertainty with different load models using artificial intelligence techniques

Authors: Zia Ullah; M.R. Elkadeem; Shaorong Wang; Syed Muhammad Abrar Akber

Addresses: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China ' State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31521, Egypt ' State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China ' School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract: This article presents the optimised planning of RDS and proposes the artificial intelligence technique using hybrid optimisation combined with phasor particle swarm optimisation and a gravitational algorithm, called PPSO/GSA for optimal planning of RDS considering photovoltaic distributed generators in RDSs. The main objective is to maximise the RDS performance by optimally allocating the PV generators. The proposed PPSO/GSA is implemented and validated on 94-bus practical RDS located in Portuguese considering single and multiple scenarios of PV generators installation along with various loading conditions. The results reveal that the optimised planning of RDS enhance the system reliability in term of a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile. Moreover, the convergence characteristics, computational efficiency, and applicability of the proposed artificial intelligence technique are evaluated by comparative analysis and comparison with other optimisation techniques.

Keywords: radial distribution system; photovoltaic; distributed generators; artificial intelligence; distribution system planning.

DOI: 10.1504/IJWGS.2020.106126

International Journal of Web and Grid Services, 2020 Vol.16 No.1, pp.63 - 80

Received: 30 Sep 2019
Accepted: 05 Nov 2019

Published online: 25 Mar 2020 *

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