Screening Electric-Vehicle Charging Against Positive Model-to-Meter Renewable-Energy Residuals: Smart G2V Potential and a V2G Capacity Envelope

This study develops a deterministic annual screening model for matching electric-vehicle (EV) charging demand with the positive part of modelled minus metered renewable-energy production. Distribution-system-operator-supplied records for photovoltaic (PV), wind and hydropower installations in 2020–2025 were combined with a supplier-provided extract from the Central Register of Vehicles and Drivers (CEPiK) covering 30 models and 20,043 vehicles. The corrected calculation assigns each technology–year–capacity-class residual once across the full fleet, then applies energy, charger-power, connection and availability limits. Annual source-side demand is 54.389 GWh. In the 2025 large-source cases, Smart G2V covers 46.31% of demand for PV, 64.36% for hydropower and 100.00% for wind, compared with 16.21%, 22.53% and 98.97% under immediate charging. Wind offers 249.660 GWh of technically accessible energy, but only 21.79% is used after EV demand is saturated. Four model labels, representing 7109 vehicles, form a theoretical V2G grid-delivery envelope of 101.636 MWh per event and 9.274 GWh/year at 0.25 equivalent cycles/day. This envelope is not added to renewable-energy utilisation. The results are conditional annual bounds, not measured curtailment or an operational dispatch result; timestamped generation, charging availability, state of charge and network topology are required for deployment analysis.

Authors

Institutions

Publication Details

Journal
Energies
Published
2026-10-16
DOI
https://doi.org/10.3390/en19204900
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

Screening Electric-Vehicle Charging Against Positive Model-to-Meter Renewable-Energy Residuals: Smart G2V Potential and a V2G Capacity Envelope

Adam Koniuszy, Tomasz Kozakowski
Energies
Electric Vehicles and Infrastructure
article

Screening Electric-Vehicle Charging Against Positive Model-to-Meter Renewable-Energy Residuals: Smart G2V Potential and a V2G Capacity Envelope

Adam Koniuszy, Tomasz Kozakowski
article en

Abstract

This study develops a deterministic annual screening model for matching electric-vehicle (EV) charging demand with the positive part of modelled minus metered renewable-energy production. Distribution-system-operator-supplied records for photovoltaic (PV), wind and hydropower installations in 2020–2025 were combined with a supplier-provided extract from the Central Register of Vehicles and Drivers (CEPiK) covering 30 models and 20,043 vehicles. The corrected calculation assigns each technology–year–capacity-class residual once across the full fleet, then applies energy, charger-power, connection and availability limits. Annual source-side demand is 54.389 GWh. In the 2025 large-source cases, Smart G2V covers 46.31% of demand for PV, 64.36% for hydropower and 100.00% for wind, compared with 16.21%, 22.53% and 98.97% under immediate charging. Wind offers 249.660 GWh of technically accessible energy, but only 21.79% is used after EV demand is saturated. Four model labels, representing 7109 vehicles, form a theoretical V2G grid-delivery envelope of 101.636 MWh per event and 9.274 GWh/year at 0.25 equivalent cycles/day. This envelope is not added to renewable-energy utilisation. The results are conditional annual bounds, not measured curtailment or an operational dispatch result; timestamped generation, charging availability, state of charge and network topology are required for deployment analysis.

EnergiesVol. 19(20)
West Pomeranian University of Technology in Szczecin (PL)
Affordable and clean energy, Industry, innovation and infrastructure, Climate action
Openalex Percentile: Top 23%
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.