Integration of Distributed Generations and electric vehicles in the distribution system

被引:5
作者
Dubey, Pankaj Kumar [1 ]
Singh, Bindeshwar [1 ]
Singh, Deependra [1 ]
机构
[1] Kamla Nehru Inst Technol, Sultanpur, Uttar Pradesh, India
关键词
Coordinated control; Distributed generation; Distribution system; Electric vehicles; Hybrid techniques; Load models; Size; Types & location; ALGORITHM; OPTIMIZATION;
D O I
10.1016/j.engappai.2024.109036
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm- Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.
引用
收藏
页数:23
相关论文
共 28 条
[1]   A cost of ownership analysis of batteries in all-electric and plug-in hybrid vehicles [J].
Baek, Donkyu ;
Bocca, Alberto ;
Macii, Alberto .
ENERGY ECOLOGY AND ENVIRONMENT, 2022, 7 (06) :604-613
[2]   Experimental Determination of the ZIP Coefficients for Modern Residential, Commercial, and Industrial Loads [J].
Bokhari, Abdullah ;
Alkan, Ali ;
Dogan, Rasim ;
Diaz-Aguilo, Marc ;
de Leon, Francisco ;
Czarkowski, Dariusz ;
Zabar, Zivan ;
Birenbaum, Leo ;
Noel, Anthony ;
Uosef, Resk Ebrahem .
IEEE TRANSACTIONS ON POWER DELIVERY, 2014, 29 (03) :1372-1381
[3]   A novel approach for comparative analysis of distributed generations and electric vehicles in distribution systems [J].
Dubey, Pankaj Kumar ;
Singh, Bindeshwar ;
Kumar, Varun ;
Singh, Deependra .
ELECTRICAL ENGINEERING, 2024, 106 (03) :2371-2390
[4]   A multi-strategy boosted prairie dog optimization algorithm for global optimization of heat exchangers [J].
Gurses, Dildar ;
Mehta, Pranav ;
Sait, Sadiq M. ;
Kumar, Sumit ;
Yildiz, Ali Riza .
MATERIALS TESTING, 2023, 65 (09) :1396-1404
[5]   Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger [J].
Gurses, Dildar ;
Mehta, Pranav ;
Patel, Vivek ;
Sait, Sadiq M. ;
Yildiz, Ali Riza .
MATERIALS TESTING, 2022, 64 (09) :1325-1331
[6]   African vultures optimization algorithm for optimization of shell and tube heat exchangers [J].
Gurses, Dildar ;
Mehta, Pranav ;
Sait, Sadiq M. ;
Yildiz, Ali Riza .
MATERIALS TESTING, 2022, 64 (08) :1234-1241
[7]   Cost Functions for Generation Dispatching in Microgrids for Non-Interconnected Zones in Colombia [J].
Hoyos-Velandia, Cristian ;
Ramirez-Hurtado, Lina ;
Quintero-Restrepo, Jaime ;
Moreno-Chuquen, Ricardo ;
Gonzalez-Longatt, Francisco .
ENERGIES, 2022, 15 (07)
[8]   A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems [J].
Injeti, Satish Kumar ;
Kumar, N. Prema .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2013, 45 (01) :142-151
[9]   MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks [J].
Jiang, Bo ;
Chen, Si ;
Wang, Beibei ;
Luo, Bin .
NEURAL NETWORKS, 2022, 153 :204-214
[10]   An Overview of Parameter and Cost for Battery Electric Vehicles [J].
Koenig, Adrian ;
Nicoletti, Lorenzo ;
Schroeder, Daniel ;
Wolff, Sebastian ;
Waclaw, Adam ;
Lienkamp, Markus .
WORLD ELECTRIC VEHICLE JOURNAL, 2021, 12 (01) :1-29