Evidence map›Paper›PMID 42325551›Full record

ArticleiScience2026

User-driven potential for scalable, cost-effective, and flexible vehicle-to-grid resources in China.

Xiaoli Han, Bo Wang, Suyang Zhou, Bin Lu, Jiucun Yuan, Jingjing Zhang, Jing Li, Zhaohua Wang

Abstract read
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xiaoli HanSchool of Management, Beijing Institute of Technology, Beijing 100081, China.
Bo WangSchool of Management, Beijing Institute of Technology, Beijing 100081, China.
Suyang ZhouSchool of Electrical Engineering, Southeast University, Nanjing 210096, China.
Bin LuDigital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology, Beijing 100081, China.
Jiucun YuanDigital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology, Beijing 100081, China.
Jingjing ZhangEnergy and Resources Group, University of California, Berkeley, Berkeley, CA 94720, USA.
Jing LiSchool of Management, Beijing Institute of Technology, Beijing 100081, China.
Zhaohua WangDigital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vehicle-to-grid (V2G) technology is widely recognized for enhancing power system flexibility and supporting renewable energy integration. However, assessments of its cost effectiveness based on user willingness and its response capability under real-world conditions remain limited. This study evaluates the flexibility potential and economic performance of V2G in China by incorporating user preferences and regional heterogeneity. Using a discrete choice experiment (DCE) with 1,412 electric vehicle (EV) owners, we estimate user willingness to accept (WTA) and quantify V2G potential. Results show that V2G could provide up to 348 GW of flexibility, exceeding 11 times the current national energy storage capacity. Under emergency demand response scenarios, V2G is cost-competitive, with WTA around half that of conventional storage. However, WTA varies substantially ($0.14-$0.85 per kWh) and is sensitive to temperature, with winter conditions reducing participation by 21% and increasing costs by 17%. These findings highlight the need for behavior-aware policy and market design.

Indexed as

Applied sciencesElectrical engineeringElectrical system

Identifiers

PMID42325551
PMCPMC13277529

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.