Cost-Efficient Energy Management for V2G and G2V Operations in AC/DC Microgrids

This paper introduces a hierarchical control strategy for energy management in a hybrid AC/DC microgrid, emphasizing the influence of electric vehicle (EV) charging patterns. The power flow and standard operating conditions within the AC and DC segments of the microgrid are controlled at both primary and secondary levels. An EV aggregator model is proposed to manage the EV converter based on a planned schedule, enabling efficient power distribution and improving microgrid stability. To optimize the EV charging process and reduce costs, a non-linear stochastic programming model is employed. The performance of both the microgrid controller and EV aggregator is thoroughly evaluated in a real-time lab setting, demonstrating its effectiveness in managing EV power, enhancing voltage and frequency stability, and optimizing scheduling in both grid-connected and islanded modes.

Authors

Institutions

Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/icast61769.2024.10856479
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cost-Efficient Energy Management for V2G and G2V Operations in AC/DC Microgrids

Anubha Gautam, Gulshan Sharma, Pitshou Ntambu Bokoro, Swati Sharma
Microgrid Control and Optimization
article

Cost-Efficient Energy Management for V2G and G2V Operations in AC/DC Microgrids

Anubha Gautam, Gulshan Sharma, Pitshou Ntambu Bokoro, Swati Sharma
article en

Abstract

This paper introduces a hierarchical control strategy for energy management in a hybrid AC/DC microgrid, emphasizing the influence of electric vehicle (EV) charging patterns. The power flow and standard operating conditions within the AC and DC segments of the microgrid are controlled at both primary and secondary levels. An EV aggregator model is proposed to manage the EV converter based on a planned schedule, enabling efficient power distribution and improving microgrid stability. To optimize the EV charging process and reduce costs, a non-linear stochastic programming model is employed. The performance of both the microgrid controller and EV aggregator is thoroughly evaluated in a real-time lab setting, demonstrating its effectiveness in managing EV power, enhancing voltage and frequency stability, and optimizing scheduling in both grid-connected and islanded modes.

University of Johannesburg (ZA), J.C. Bose University of Science & Technology, YMCA (IN), Jamia Millia Islamia (IN)
Affordable and clean energy
Openalex Percentile: Top 68%
Microgrid Control and Optimization
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.

Cost-Efficient Energy Management for V2G and G2V Operations in AC/DC Microgrids — Anubha Gautam, Gulshan Sharma, et al. · (2024) | TGRS Research Map | TGRS