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WEVJ
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Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability
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Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 July 2025 | Viewed by 8204

Special Issue Editors

Dr. Chun Sing Lai

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Brunel University, London UB8 3PH, UK
Interests: power system optimization; energy system modeling; data analytics; electric vehicle systems; hybrid powertrains optimization; energy economics for renewable energy and storage systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xi Chen

E-Mail Website
Guest Editor
GEIRI North America, 250 W Tasman Dr., Ste 100, San Jose, CA 95134, USA
Interests: smart grids; Internet of Things; EV smart charging

Special Issue Information

Dear Colleagues,

This Special Issue, "Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability", aims to provide a platform for researchers and practitioners to present and discuss the latest developments, challenges, and opportunities related to the integration of electric vehicles (EVs) into smart grids. EVs have become increasingly popular due to their environmental benefits and potential for cost savings. However, the integration of EVs into the smart grid presents new challenges, including the need for charging infrastructure, communication protocols, and strategies to mitigate the impact of EVs on the grid. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Integration of EVs into the smart grid: this topic covers the challenges and opportunities of integrating EVs into the smart grid, including vehicle-to-grid (V2G) technology, charging infrastructure, and communication protocols.
  • Optimization of EV charging: this topic focuses on the development of optimal charging strategies for EVs that consider factors such as user preferences, grid conditions, and renewable energy sources.
  • Grid impact of EVs: this topic covers the impact of EVs on the grid, including their potential to increase peak demand and affect grid stability. It also includes the development of strategies to mitigate these impacts and ensure the reliability of the grid.
  • Sustainability of EVs and smart grids: this topic covers the environmental and economic benefits of EVs and smart grids, including the reduction in greenhouse gas emissions and the potential for cost savings.
  • Policy and regulation: this topic covers the policy and regulatory frameworks that support the integration of EVs into smart grids, including charging infrastructure requirements, incentives for EV adoption, and grid interconnection standards.

Overall, this Special Issue seeks to provide a comprehensive understanding of the integration of EVs into smart grids and the potential for this technology to promote sustainability and energy efficiency.

Dr. Chun Sing Lai
Dr. Xi Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicles
  • smart grid
  • vehicle-to-grid (V2G)
  • charging infrastructure
  • optimization
  • sustainability
  • grid impact
  • renewable energy
  • policy and regulation
  • energy efficiency

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Published Papers (6 papers)

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Research

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20 pages, 1348 KiB  
Article
Impacts of Electric Vehicle Penetration on the Frequency Stability of Curaçao’s Power Network
by Daniela Vásquez-Cardona, Sergio D. Saldarriaga-Zuluaga, Santiago Bustamante-Mesa, Jesús M. López-Lezama and Nicolás Muñoz-Galeano
World Electr. Veh. J. 2025, 16(5), 264; https://doi.org/10.3390/wevj16050264 - 10 May 2025
Viewed by 186
Abstract
Assessing the impact of electric vehicle (EV) integration on power systems is crucial, particularly regarding frequency stability, which often remains largely unaddressed, especially in developing countries. This paper examines the effects of EV penetration on the frequency stability of Curaçao’s power network, an [...] Read more.
Assessing the impact of electric vehicle (EV) integration on power systems is crucial, particularly regarding frequency stability, which often remains largely unaddressed, especially in developing countries. This paper examines the effects of EV penetration on the frequency stability of Curaçao’s power network, an aspect not previously studied for the island. As a key contribution, we present a representative model of Curaçao’s power network, adjusting the dynamic models of the speed governors of synchronous machines, using data available to the academic community. Additionally, we analyze the impacts of EVs on the grid’s frequency stability under different EV participation scenarios. To achieve this, simulations were conducted considering various EV participation scenarios and different types of chargers to assess their impact on grid stability. The study evaluates key frequency stability metrics, including the rate of change of frequency (RoCoF) as well as the highest and lowest frequency values during the transient period. The results indicated that higher EV penetration can significantly impact frequency stability. The observed increase in the RoCoF and frequency zenith values suggests a weakening of the grid’s ability to withstand frequency disturbances, particularly in high-EV-penetration scenarios. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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29 pages, 1264 KiB  
Article
User Cost Minimization and Load Balancing for Multiple Electric Vehicle Charging Stations Based on Deep Reinforcement Learning
by Yongxiang Xia, Zhongyi Cheng, Jiaqi Zhang and Xi Chen
World Electr. Veh. J. 2025, 16(3), 184; https://doi.org/10.3390/wevj16030184 - 19 Mar 2025
Viewed by 314
Abstract
In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across [...] Read more.
In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across large-scale distribution networks and the charging costs for users, this paper proposes an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aims to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divides the charging process into two stages: charging station selection and in-station charging scheduling. In the first stage, a Load Balancing Matching Strategy (LBMS) is employed to assist users in selecting a charging station. In the second stage, we use the DRL algorithm. In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. Case study results demonstrate the effectiveness of the proposed strategy in a multi-distribution network environment. Moreover, even when faced with varying levels of EV user participation, the strategy continues to demonstrate strong performance. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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22 pages, 2075 KiB  
Article
Unlocking Grid Flexibility: Leveraging Mobility Patterns for Electric Vehicle Integration in Ancillary Services
by Corrado Maria Caminiti, Luca Giovanni Brigatti, Matteo Spiller, Giuliano Rancilio and Marco Merlo
World Electr. Veh. J. 2024, 15(9), 413; https://doi.org/10.3390/wevj15090413 - 9 Sep 2024
Cited by 1 | Viewed by 1432
Abstract
The electrification of mobility has introduced considerable challenges to distribution networks due to varying demand patterns in both time and location. This underscores the need for adaptable tools to support strategic investments, grid reinforcement, and infrastructure deployment. In this context, the present study [...] Read more.
The electrification of mobility has introduced considerable challenges to distribution networks due to varying demand patterns in both time and location. This underscores the need for adaptable tools to support strategic investments, grid reinforcement, and infrastructure deployment. In this context, the present study employs real-world datasets to propose a comprehensive spatial–temporal energy model that integrates a traffic model and geo-referenced data to realistically evaluate the flexibility potential embedded in the light-duty transportation sector for a given study region. The methodology involves assessing traffic patterns, evaluating the grid impact of EV charging processes, and extending the analysis to flexibility services, particularly in providing primary and tertiary reserves. The analysis is geographically confined to the Lombardy region in Italy, relying on a national survey of 8.2 million trips on a typical day. Given a target EV penetration equal to 2.5%, corresponding to approximately 200,000 EVs in the region, flexibility bands for both services are calculated and economically evaluated. Within the modeled framework, power-intensive services demonstrated significant economic value, constituting over 80% of the entire potential revenues. Considering European markets, the average marginal benefit for each EV owner is in the order of 10 € per year, but revenues could be higher for sub-classes of users better fitting the network needs. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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28 pages, 5486 KiB  
Article
Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization
by Lijia Duan, Gareth Taylor and Chun Sing Lai
World Electr. Veh. J. 2024, 15(8), 337; https://doi.org/10.3390/wevj15080337 - 27 Jul 2024
Cited by 4 | Viewed by 1555
Abstract
The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key [...] Read more.
The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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Review

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13 pages, 723 KiB  
Review
Relocation Optimization for Shared Electric Vehicles: A Literature Review
by Ye Zou, Qian Yu, Dongming Jiang and Youjun Deng
World Electr. Veh. J. 2025, 16(2), 108; https://doi.org/10.3390/wevj16020108 - 18 Feb 2025
Viewed by 437
Abstract
Car sharing has become an increasingly popular mode of travel. This paper provides a comprehensive literature survey on relocation optimization for shared electric vehicles. The literature is reviewed and categorized based on two types of relocation: static and dynamic relocation. Static relocation is [...] Read more.
Car sharing has become an increasingly popular mode of travel. This paper provides a comprehensive literature survey on relocation optimization for shared electric vehicles. The literature is reviewed and categorized based on two types of relocation: static and dynamic relocation. Static relocation is analyzed in terms of operator-based relocation and user-based relocation, while dynamic relocation is analyzed in terms of four methodologies: optimization models, simulations, multi-stage methods, and deep reinforcement learning. The paper finally provides some interesting future research topics, such as considering the nonlinear charging process of electric vehicles in the process of constructing relocation optimization models and designing algorithms for shared electric vehicles. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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28 pages, 3648 KiB  
Review
Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles
by Sameer Qazi, Bilal A. Khawaja, Abdullah Alamri and Abdulrahman AlKassem
World Electr. Veh. J. 2024, 15(11), 487; https://doi.org/10.3390/wevj15110487 - 27 Oct 2024
Cited by 2 | Viewed by 3380
Abstract
The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will [...] Read more.
The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption. Full article
(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)
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