Coordinated electric vehicles dispatch for multi-service provisions: A comprehensive review of modelling and coordination approaches

Chutian Su, Yi Wang and Goran Strbac

Renewable and Sustainable Energy Reviews, 2025, vol. 223, issue C

Abstract: As one of the demand-side technologies, electric vehicles (EVs) have been widely applied in modern power systems to provide ancillary services due to their mobility and flexibility. Large-scale EV integration facilitates the transition towards a low-carbon future while enhancing system stability and security. Consequently, research in EV routing and scheduling in coupled power and transport networks towards multi-service provisions has gained considerable attention. Given the inherent complexity and low-inertia nature of distribution systems-characterised by high penetration of renewable energy sources, various uncertainties, and dynamic operation conditions-there is an urgent need to develop intelligent and automated control strategies to coordinate multiple EVs for ancillary service provisions effectively. In this paper, various ancillary services are first classified into three types: energy imbalance service, security-related services, and social welfare services, covering demand response, frequency response, voltage regulation, resilience, carbon intensity service, and economic benefits. As far as the EV coordination strategies in multi-service provisions are concerned, four types of control approaches are appropriately reviewed: centralised, hierarchical, distributed, and decentralised control. Specifically, this paper provides insights into utilising reinforcement learning (RL), a model-free online learning method that can capture uncertainties and adapt to different state conditions in real-time, to address coordinated EV dispatch problems in a decentralised manner. Finally, research challenges are identified, and potential future research directions are discussed, covering different perspectives, e.g., market incentives, privacy and safety, uncertainty handling, realistic EV modelling, and cybersecurity.

Keywords: Electric vehicles; Ancillary services; Multi-service provision; Control approaches; Reinforcement learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032125006501
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:223:y:2025:i:c:s1364032125006501

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2025.115977

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().