Reserve Provision From Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386171" target="_blank" >RIV/68407700:21230/25:00386171 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPWRS.2025.3539863" target="_blank" >https://doi.org/10.1109/TPWRS.2025.3539863</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPWRS.2025.3539863" target="_blank" >10.1109/TPWRS.2025.3539863</a>
Alternative languages
Result language
angličtina
Original language name
Reserve Provision From Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control
Original language description
Controlled charging of electric vehicles, EVs, is a major potential source of flexibility to facilitate the integration of variable renewable energy and reduce the need for stationary energy storage. To offer system services from EVs, fleet aggregators must address the uncertainty of individual driving and charging behaviour. This paper introduces a means of forecasting the service volume available from EVs by considering several EV batteries as one conceptual battery with aggregate power and energy boundaries. Aggregation avoids the difficult prediction of individual driving behaviour. The predictability of the boundaries is demonstrated using a multiple linear regression model which achieves a normalised root mean square error of 20%-40% for a fleet of 1,000 EVs. A two-stage stochastic model predictive control algorithm is used to schedule reserve services on a day-ahead basis addressing risk trade-offs by including Conditional Value-at-Risk in the objective function. A case study with 1.2 million domestic EV charge records from Great Britain illustrates that increasing fleet size improves prediction accuracy, thereby increasing reserve revenues and decreasing an aggregator's operational costs. For fleet sizes of 400 or above, cost reductions plateau at 60% compared to uncontrolled charging, with an average of 1.8 kW of reserve provided per vehicle.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Power Systems
ISSN
0885-8950
e-ISSN
1558-0679
Volume of the periodical
40
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
4081-4092
UT code for WoS article
001560302400005
EID of the result in the Scopus database
2-s2.0-85217687377