Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

Abstract

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

Keywords:

• Network reconfiguration
• shuffled frog leaping algorithm
• reliability
• distributed generators

Disclosure Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This research has not been funded by any individual or organization.

Notes on contributors

Benyamin Katanchi

Benyamin Katanchi received B.Sc. and M.Sc. degrees from Islamic Azad University of Bojnourd, in 2009 and 2014. He is currently Ph.D. student in Islamic Azad University of Neyshabur. His main research interests are renewable energy sources, power system reliability and stability.

Ali Asghar Shojaei

Aliasghar Shojaei received a B.Sc degree from Islamic Azad University of, Iran, in 2007. Then he pursued and received the M.Sc. and Ph.D. degrees in electrical engineering both from University Technology Malaysia in 2009 and 2014, respectively. Since 2010, he has been a Researcher in the Center for Artificial Intelligence and Robotics, University Technology of Malaysia. Currently, he is an assistant professor at Islamic Azad University, Neyshabur branch. His research areas include artificial intelligence applications on power system stability and power electronics converters as well as designing the neural network for power converter control in islanded and grid-connected modes

Mahdi Yaghoobi

Mahdi Yaghoobi received the Ph.D. M.Sc. and degrees from Ferdowsi University of Mashhad and Islamic Azad University،Science and Research Branch in 1993 and 2007. He is currently a retired professor in the electrical engineering department of Azad University of Mashhad. His main research interests are Fuzzy System, Evolutionary Algorithm, Predictive Control.