Electronics 2022, 11, 3221. https://doi.org/10.3390/electronics11193221 www.mdpi.com/journal/electronics Article Comparison of Grid Reactive Voltage Regulation with Reconfiguration Network for Electric Vehicle Penetration Farrukh Nagi 1, *, Aidil Azwin 2 , Navaamsini Boopalan 2, *, Agileswari K. Ramasamy 2 , Marayati Marsadek 1 and Syed Khaleel Ahmed 3 1 Institute of Power Engineering, Universiti Tenaga Nasional,4300 Kajang, Selangor, Malaysia 2 Department of Electrical & Electronic Engineering, Universiti Tenaga Nasional, 4300 Kajang, Selangor, Malaysia 3 C3, Alsa Towers, EVR Periyar Salai, Chetpet, Chennai 600010, India * Correspondence: farrukh@uniten.edu.my (F.N.); navaamsini@gmail.com (N.B.) Abstract: Renewable energy sources and EV growth brings new challenges for grid stabilization. Smart grid techniques are required to reconfigure and compensate for load fluctuation and stabilize power losses and voltage fluctuation. Numerical tools are available to equip the smart grid to deal with such challenges. Distribution Feeder reconfiguration and reactive voltage injection to the disturbed grid are some of the techniques employed for the purpose. However, either reconfiguration or injection alone is used commonly for this purpose. In this study, both techniques are applied to EV penetration as load and compared. A balanced IEEE 33 Radial network is used in this study and selected branches with high power losses are targeted for the reactive voltage injection and Minimum Spanning tree techniques (MST). EV charging loads are usually modelled with time base distribution which requires times base power flow analysis for reactive power injection. A comparison between coordinated, reconfiguration, and reactive voltage injection shows differences in power losses, voltage distortion, and cost saving. The analysis is carried out with an integer linear programming technique for coordinated charging, a minimum spanning tree for network reconfiguration, and genetic optimization for reactive power injection. Besides, all power flow analyses are carried out with the Backward/Forward sweep method. The information would help lowering power losses, grid stabilization, and charging station infrastructure planning. Keywords: distribution feeder reconfiguration; minimum spanning tree; reactive power injection; forward/backward sweep power analysis; IEEE 33 radial bus network; mixed integer linear and GA optimization 1. Introduction The power demand for new emerging electric vehicle technology adds load on power utility companies. Environmental regulated Renewable Energy (RE) sources also play a major role as Distributed Generator (DG) connected to the distribution networks. Energy management of the network ensures the demand and supply unit commitment cost-effectively. EV charging aggregators and stations both for residential and commercial purposes make use of demographic and economic activity at the network nodes. Power stability techniques of the network are employed by the energy management system. Among the most common are (i) Flexible voltage levels (ii) Network reconfiguration topology [1] (iii) Feeder capacitor bank [2] (iv) Load balancing techniques with an On-load tap changer (OLTC) of the main transformer are used to overcome the losses in network buses rather than on bus nodes. Reactive power injection is also used for power stability, the synchronous motor excitation method at affected nodes was used by Kotenev et al. [3]. A key node reactive power injection for voltage optimization is presented by Meng and Gao [4]. Zechun and Mingming [5] inject reactive power at nodes Citation: Nagi, F.; Azwin, A.; Boopalan, N.; Ramasamy, A.K.; Marsadek, M. Comparison of Grid Reactive Voltage Regulation with Reconfiguration Network for Electric Vehicle Penetration. Electronics 2022, 11, 3221. https://doi.org/10.3390/ electronics11193221 Academic Editors: Zita Vale, João Soares and Hugo Morais Received: 19 August 2022 Accepted: 23 September 2022 Published: 8 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).