A Two-stage Optimal Dispatch Strategy of Microgrid Considering Electric Vehicles in V2G Mode

Driven by the "dual carbon" goal, achieving efficient coordination between electric vehicles (EV) and microgrids (MG) is the core of the construction of new power systems. This paper proposes a two-stage optimization model under the V2G mode and introduces a hydrogen energy circulation system. In the first stage, mixed integer programming is used to determine the EV charging and discharging strategy to maximize user satisfaction and minimize load variance; in the second stage, the NSGA-II algorithm and TOPSIS method are combined to reduce the operating cost and carbon emissions of the microgrid. The example shows that compared with disordered charging, the optimization strategy reduces the peak-to-valley difference by 75.97%, the load standard deviation by 77.01%, the MG operating cost by 50.15%, and the carbon emissions by 61.5%, providing a feasible solution for low-carbon collaborative scheduling.

Authors

Institutions

Publication Details

Published
2025-06-20
DOI
https://doi.org/10.1109/icesep66633.2025.11155769
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 Two-stage Optimal Dispatch Strategy of Microgrid Considering Electric Vehicles in V2G Mode

D.S. Yang, Md Sipon Miah, Mingbo Niu, Ren Ji
Electric Vehicles and Infrastructure
article

A Two-stage Optimal Dispatch Strategy of Microgrid Considering Electric Vehicles in V2G Mode

D.S. Yang, Md Sipon Miah, Mingbo Niu, Ren Ji
article en

Abstract

Driven by the "dual carbon" goal, achieving efficient coordination between electric vehicles (EV) and microgrids (MG) is the core of the construction of new power systems. This paper proposes a two-stage optimization model under the V2G mode and introduces a hydrogen energy circulation system. In the first stage, mixed integer programming is used to determine the EV charging and discharging strategy to maximize user satisfaction and minimize load variance; in the second stage, the NSGA-II algorithm and TOPSIS method are combined to reduce the operating cost and carbon emissions of the microgrid. The example shows that compared with disordered charging, the optimization strategy reduces the peak-to-valley difference by 75.97%, the load standard deviation by 77.01%, the MG operating cost by 50.15%, and the carbon emissions by 61.5%, providing a feasible solution for low-carbon collaborative scheduling.

Chang'an University (CN), Universidad Carlos III de Madrid (ES)
Openalex Percentile: Top 76%
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.