A Multi-Scheme Comparison Framework for Ultra-Fast Charging Stations with Active Load Management and Energy Storage Under Grid Capacity Constraints

Grid capacity constraints present a prominent challenge in the construction of ultra-fast charging (UFC) stations. Active load management (ALM) and battery energy storage systems (BESSs) are currently two primary countermeasures to address this issue. ALM allows UFC stations to install larger-capacity transformers by utilizing valley capacity margins to meet the peak charging demand during grid valley periods, while BESSs rely more on energy storage batteries to solve the gap between the transformer capacity and charging demand This paper proposes a four-quadrant classification method and defines four types of schemes for UFC stations to address grid capacity constraints: (1) ALM with a minimal BESS (ALM-Smin), (2) ALM with a maximal BESS (ALM-Smax), (3) passive load management (PLM) with a minimal BESS (PLM-Smin), and (4) PLM with a maximal BESS (PLM-Smax). A generalized comparison framework is established as follows: First, daily charging load profiles are simulated based on preset vehicle demand and predefined charger specifications. Next, transformer capacity, BESS capacity, and daily operational profiles are calculated for each scheme. Finally, a comprehensive economic evaluation is performed using the levelized cost of electricity (LCOE) and internal rate of return (IRR). A case study of a typical public UFC station in Tianjin, China, validates the effectiveness of the proposed schemes and comparison framework. A sensitivity analysis explored how grid interconnection costs and BESS costs influence decision boundaries between schemes. The study concludes by highlighting its contributions, limitations, and future research directions.

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Publication Details

Journal
World Electric Vehicle Journal
Published
2025-04-27
DOI
https://doi.org/10.3390/wevj16050250
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.73

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article

A Multi-Scheme Comparison Framework for Ultra-Fast Charging Stations with Active Load Management and Energy Storage Under Grid Capacity Constraints

Boqiang Ren, Qingyu Yin, Jian Zhang, Lili Li et al.
1 citations
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
0.73
article

A Multi-Scheme Comparison Framework for Ultra-Fast Charging Stations with Active Load Management and Energy Storage Under Grid Capacity Constraints

Boqiang Ren, Qingyu Yin, Jian Zhang, Lili Li, Xiaonan Liu
article en
1 citations

Abstract

Grid capacity constraints present a prominent challenge in the construction of ultra-fast charging (UFC) stations. Active load management (ALM) and battery energy storage systems (BESSs) are currently two primary countermeasures to address this issue. ALM allows UFC stations to install larger-capacity transformers by utilizing valley capacity margins to meet the peak charging demand during grid valley periods, while BESSs rely more on energy storage batteries to solve the gap between the transformer capacity and charging demand This paper proposes a four-quadrant classification method and defines four types of schemes for UFC stations to address grid capacity constraints: (1) ALM with a minimal BESS (ALM-Smin), (2) ALM with a maximal BESS (ALM-Smax), (3) passive load management (PLM) with a minimal BESS (PLM-Smin), and (4) PLM with a maximal BESS (PLM-Smax). A generalized comparison framework is established as follows: First, daily charging load profiles are simulated based on preset vehicle demand and predefined charger specifications. Next, transformer capacity, BESS capacity, and daily operational profiles are calculated for each scheme. Finally, a comprehensive economic evaluation is performed using the levelized cost of electricity (LCOE) and internal rate of return (IRR). A case study of a typical public UFC station in Tianjin, China, validates the effectiveness of the proposed schemes and comparison framework. A sensitivity analysis explored how grid interconnection costs and BESS costs influence decision boundaries between schemes. The study concludes by highlighting its contributions, limitations, and future research directions.

World Electric Vehicle JournalVol. 16(5)
Tsinghua Sichuan Energy Internet Research Institute (CN), Tianjin Research Institute of Electric Science (China) (CN), Tsinghua University (CN)
State Grid Tianjin Electric Power Company
Openalex Percentile: Top 29%
Electric Vehicles and Infrastructure
0.73
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