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

1. Introduction

Electric vehicles (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. 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. 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 involved in 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, considering 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 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.

2. Short Presentation of the Papers

Guerreo-Silva et al. [1] presented a systematic review which examined recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), the authors identified key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study revealed a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. The authors provided a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesized practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions.

Qazi et al. [2] presented a survey paper to explore the key research challenges in smart grid operation from an engineering design perspective, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. The authors investigated the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, the authors discussed policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, the authors addressed emerging issues regarding smart grid technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on High Voltage Alternating Current (HVAC) and High Voltage Direct Current (HVDC) technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based smart grids architectures. Finally, future trends are discussed, including the impact of increased EV penetration on smart grids, advancements in vehicle-to-grid technologies, load-shaping techniques, dynamic pricing mechanisms, and artificial intelligence-based stability enhancement measures in the context of widespread smart grid adoption.

Zou et al. [3] provided 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 provided 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.

Caminiti et al. [4] employed 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 involved 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 EUR 10 per year, but revenues could be higher for sub-classes of users better fitting the network needs.

Duan et al. [5] proposed 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 employed a non-cooperative game theory model for each individual EV charging station to minimize capital and operational costs. The second level used a cooperative game framework with an internal management system to optimize energy transactions among multiple EV charging stations while considering EV owners’ economic interests. A Markov decision process modeled uncertainties in EV charging times, and Monte Carlo simulations predicted charging demand. Real-time electricity pricing based on the dual theory enabled demand-side management strategies like peak shaving and valley filling. Case studies demonstrated 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.

Xia et al. [6] proposed an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aimed to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divided 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, the authors used the DRL algorithm. In the DRL algorithm, the authors designed 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 demonstrated 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 continued to demonstrate strong performance.

Vásquez-Cardona et al. [7] examined 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, the authors presented 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, the authors analyzed 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 evaluated key frequency stability metrics, including the rate of change of frequency (RoCoF) and 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.

Kovačević et al. [8] analyzed the impact of electromobility on distribution grids and voltage stability. In line with current legislation and the European Commission’s plans for the future of electromobility, the aim is to increase the share of electric vehicles to 50% by 2050. However, achieving this target can be challenging due to the characteristics and features of the electric vehicle charging stations and the associated charging methods, which can lead to constraints within the network. The analysis included the integration of single-phase and three-phase chargers on a radial feeder, as well as the determination of the maximum number of vehicles that can be accommodated on a given feeder without compromising voltage stability. Five scenarios are evaluated to gain a better understanding of the impact of electromobility on the distribution grid.

Saeseiw et al. [9] investigated a multi-port conversion system that connects Photovoltaic (PV) arrays, Battery Energy Storage (BES), and an EV to a single-phase grid, offering a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, optimizing usage, improving grid stability, and supplying backup power. The proposed four-port converter consists of an interleaved bidirectional Direct Current (DC)-DC converter for high-voltage BES, a bidirectional buck–boost DC-DC converter for EV charging and discharging, a DC-DC boost converter with maximum power point tracking for PV, and a grid-tied inverter. Its non-isolated structure ensures high efficiency, compact design, and fewer switches, making it suitable for residential applications. A State-of-Charge (SoC)-based power management strategy coordinates operation among PV, BES, and EV in both on-grid and off-grid modes. It reduces reliance on EV energy when supporting V2G and V2H, while SoC balancing between BES and EV extends lifetime and lowers current stress. A 7.5 kVA system was simulated to validate feasibility. Two scenarios were studied: PV, BES, and EV with V2G supporting the grid and PV, BES, and EV with V2H providing backup power in off-grid mode. Tests under PV fluctuations and load variations confirmed the effectiveness of the proposed design. The system exhibited a fast transient response of 0.05 s during grid-support operation and maintained stable voltage and frequency in off-grid mode despite PV and load fluctuations. Its protection scheme disconnected overloads within 0.01 s, while harmonic distortions in both cases remained modest and complied with EN50610 standards.

Fantin et al. [10] analyzed the impact of Battery Electric Bus (BEB) charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility geographic information system data into OpenDSS simulation models. The study utilized Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed PV generation and V2G operation. Results showed that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offered a replicable approach for low-carbon urban mobility planning in similar developing regions.