Перейти к основному содержанию
AkademIndex

Продукты

Для разработчиков

AkademBaseОткрытый API экосистемы
Статья

Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Rakesh KumarGLA University,Department of Computer Engineering & Applications,MathuraSheerin BegamE.G.S. Pillay Engineering College,Department of Computer Science and Business System,Nagapattinam,Tamilnadu,IndiaDev Prakash SinghTuychiev Komiljon LazizovichTashkent State University of Economics,Department of Marketing,UzbekistanS. MuthubalajiCMR College of Engineering & Technology,Department of Electrical and Electronics Engineering,Hyderabad,Telangana,India,501401Mohammed Al‐FarouniThe Islamic University,Najaf,IraqSrinivas Varanasi
2023en
ABI

Аннотация

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness.

Перевод пока недоступен

Темы

Идентификаторы

Цитирования и источники