A Virtual Power Plant Framework for Dynamic Power Management in EV Charging Stations

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users.

Authors

Institutions

Publication Details

Journal
World Electric Vehicle Journal
Published
2025-12-25
DOI
https://doi.org/10.3390/wevj17010014
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Virtual Power Plant Framework for Dynamic Power Management in EV Charging Stations

G. M. Shafiullah, Al Amin, Md Shoeb, S. M. Ferdous
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
article

A Virtual Power Plant Framework for Dynamic Power Management in EV Charging Stations

G. M. Shafiullah, Al Amin, Md Shoeb, S. M. Ferdous
article en

Abstract

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users.

World Electric Vehicle JournalVol. 17(1)
Murdoch University (AU)
Industry, innovation and infrastructure
Openalex Percentile: Top 42%
Electric Vehicles and Infrastructure
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Virtual Power Plant Framework for Dynamic Power Management in EV Charging Stations — G. M. Shafiullah, Al Amin, et al. · World Electric Vehicle Journal (2025) | TGRS Research Map | TGRS