The Use of Electric Vehicles to Support the Needs of the Electricity Grid: A Systematic Literature Review

Abstract

The integration of electric vehicles (EVs) into the electricity grid through vehicle-to-grid (V2G) technology represents a promising opportunity to improve energy efficiency and stabilize grid operations in the context of building sustainable cities. This paper provides a systematic review of the literature to assess the status of the research and identify the road ahead. Using bibliometric analysis and systematic assessment, the critical factors that influence the charging behavior of electric vehicles, the adoption of V2G, and the effective use of EVs as dynamic energy resources are identified. The focus is particularly on the ecological transitions toward sustainability, travel characteristics, technical specifications, requirements, and barriers in real use, and the behavioral and psychological aspects of stakeholders. The results lay the foundation for accurate forecasts and the strategic implementation of V2G technology to support the needs of the electric grid. They emphasize the importance of considering the psychological and behavioral aspects of users in the design of V2G strategies and define the key factors to predict the demand for electric vehicle charging. Furthermore, they highlight the main barriers to V2G adoption, which are primarily related to concerns about battery degradation and economic issues. Privacy and security concerns, due to data sharing with electric vehicle aggregators, also limit the adoption of V2G. Addressing these challenges is essential for the successful integration of electric vehicles into the grid.

1. Introduction

Urbanization is a major global trend, with the United Nations [1] forecasting that 68% of the world population will reside in urban areas by 2050, up from 30% in 1950. This shift highlights the need for efficient urban planning, particularly in the transportation sector, which is vital for urban development. Private vehicles, a primary mode of transport, contribute significantly to CO, CO₂, and NH₃ emissions [2]. To improve urban sustainability and to push toward greener mobility, the European Commission has implemented initiatives such as the Sustainable Development Goals (SDG) and the “Fit for 55 in 2030” project. In particular, they aim to reduce these emissions, to promote decarbonization of the transportation sector, and to encourage the transition to electric vehicles (EVs).

In the decarbonization perspective, the introduction of electric vehicles will play an increasingly important role, creating a growing challenge to the energy supply in the appropriate areas. However, the vision of electric vehicles as the only consumers of energy is limiting. In fact, the growing demand of electricity can cause issues to grid stability, in particular, in the peak hours. The vehicle-to-grid (V2G) technology of EVs has thus recently gained interest as a clean and green energy and has opened new opportunities for EV owners [3]. The new challenges are to enable electric vehicles to participate in the electricity market as active customers who can feed the electricity stored in EV batteries back to the power grid during peak hours to help balance the growing demand for electricity and avoid the need for grid investments. In fact, the V2G paradigm proposes the use of vehicle batteries not only as energy accumulators but also as energy suppliers according to the needs of the entire electricity grid, making electric vehicles active participants in energy management [4,5]. This approach is of particular interest if it is considered that electric vehicles, predominantly used for systematic short daily trips, such as home-to-work or home-to-school commutes, often store and keep significant amounts of electricity unused during long parking periods [6,7,8,9,10]. Therefore, the current research is focused on exploring how to exploit this unused energy and its potential contribution to the power grid. It aims to consider electric vehicles not only as passive accumulators but also as active energy providers, integrated into the dynamic and bidirectional management of electricity.

As a result, the purpose of this work is to explore the current literature in order to point out the status of the research and the fields that need to be further developed in order to support the real implementation of V2G. It will focus on the ecological transitions to sustainability, travel characteristics, technical specifications, requirements, and barriers in real use, and the behavioral and psychological aspects of stakeholders. In fact, although some review studies have been proposed on this topic, they mainly focus on technological aspects and barriers with little emphasis on user needs and expectations [11,12,13,14].

The rest of the paper is organized as follows. Section 2 introduces the systematic literature review methodology, while Section 3 presents the application of the methodology to the research field identified above and the results obtained. Finally, the conclusions and the road ahead are drawn in Section 4.

2. The Proposed Methodology

Researchers can use bibliometric analysis, a quantitative technique to look back and describe published works, to assess academic studies on a particular topic [15,16,17,18,19]. Bibliometric analysis employs secondary data to investigate the data obtained from digital databases from a quantitative and impartial point of view. Consequently, it can establish a methodical, transparent, and repeatable evaluation procedure thus increasing the dependability of the review.

Thus, a systematic review is a review of the literature that employs a methodical approach to identify, synthesize, and assess scientific evidence in a specific field with precision and rigor. It follows a well-defined protocol designed to minimize cognitive bias during the process, ensuring the reliability of the conclusions drawn. The key features of a systematic review include clear objectives with predetermined eligibility and relevance criteria for the studies, a thorough search to identify all eligible studies, transparent and reproducible methods, and a systematic presentation with a synthesis of the included studies [20]. The advantages of this bibliometric technique over traditional literature analysis include the following: (1) the ability to quantitatively analyze a large number of peer-reviewed studies in different disciplines [21]; (2) the use of visual network analysis that helps identify key patterns in selected studies and groups of existing ones. Temporal analysis of citation bursts and co-occurrence examination of principal keywords can pinpoint emerging themes and future trends. These insights are crucial to identify hotspots and understand evolving trends in this area of study, shedding light on emerging research domains [22].

In general, the methodology consists of two core procedures [15], namely performance analysis and science mapping. However, according to the PRISMA methodology [23], these stages are anticipated by the following two further stages: identification, and screening and eligibility.

The methodology developed and used is shown in Figure 1 and consists of the following steps:

• initialization; it allows for the most suitable scientific datasets for the purposes of the study to be built according to the main defined criteria for the inclusion or exclusion of the literature studies, as reported in

  Table 1. Inclusion criteria for work selection.

  Dataset	WoS and Scopus
  Analyzed field	Title, abstract, and keywords
  Document type	Papers in journals, conference proceedings, book chapters, and reviews.
  Language	English
  Source type	Peer-reviewed
  Time interval	2000–2024

  ;
• identification; the selection of the relevant keywords and implementation of the search using Boolean operators (OR, AND, NOT) to combine the strategically chosen keywords as well as to optimize search results; the search can be performed within different fields, e.g., title, abstract, keywords, or full text;
• screening and eligibility, and inclusion; the preliminary selection of potentially relevant papers by configuring the appropriate filters (language, year of publication, topic) for removing duplicates or inappropriate ones; then the collection of papers to review is obtained;
• data analysis; at this stage, the review of works included in the built dataset is developed.

3. The Results

As mentioned above, the systematic review allows researchers to understand modern and innovative approaches, guide research discussions, assess impact over time, and discover if there are gaps in the study. Then, by synthesizing data from search engines, a network based on authors, countries, or keywords can be built. This network allows researchers to create and visualize links between these attributes and the identification of clusters according to the research lines promoted in different countries or over the years. Then, according to the methodology plotted in Figure 1, the literature review is developed according to the foreseen stages, as described below.

3.1. Initialization

As plotted in Figure 1, the field of knowledge to be investigated was identified, and then the scientific dataset selected for the purposes of this work was initially identified. Subsequently, the relevant keywords related to the study topic, namely the support that electric vehicles can provide to the power grid, were selected. The scientific datasets used for the research were Scopus and WoS. The choice to use both databases for more complete research due to the complementary characteristics of them as WOS provides greater coverage in years, whilst Scopus includes a wider base of academic journals. Research can be performed according to various criteria, such as the author’s name and keywords. Then, it can be further refined by date, document type, subject area, languages, or most recent updates. The main criteria for the inclusion and exclusion of the scientific work investigated have been identified and are shown in Table 1.

3.2. Identification

After identifying the relevant keywords, the data collection is enhanced using Boolean operators (OR, AND) to strategically combine the terms and optimize the search results within the used datasets (Scopus and WoS). The keyword search was carried out within the title, abstract and keywords as summarized in

Table 2. Summary of selected studies to review.

Search Phrase	Selected Papers to Review
(KEY (“vehicle to grid”) OR KEY (“V2G”) OR KEY (“bidirectional charging”)) AND (TITLE-ABS-KEY (“assessment”) OR TITLE-ABS-KEY (“evaluation”) OR TITLE-ABS-KEY (“forecast*”)) AND (LIMIT-TO (LANGUAGE, “English”))	1352

.

3.3. Screening and Eligibility, and Inclusion

A preliminary selection was made, considering only studies in English published after the year 2000. Subsequently, the irrelevant work was identified and excluded through an analysis of the abstracts. To merge the datasets obtained from Scopus and WoS search results for bibliometric analysis and to remove duplicates, EndNote 21© software was used. In Table 2, the results of the search are summarized. A total of 1352 papers were identified and selected as worthy of review.

The final phase focused on reviewing the studies included in the analysis to perform the bibliometric analysis, which included the performance analysis and science mapping. Performance analysis often uses citation counts to gauge the relative importance and influence of publications, authors, journals, institutions, and countries, and it may also involve calculating the average citations per work. Science mapping typically involves co-citation analysis and evolution analysis. Co-citation analysis helps researchers identify prominent authors and related journals within a research area by examining how often they are cited. On the basis of understanding the structure of the research, an evolution analysis trough can be performed to explore the development process in this field.

The reviewed documents belong to various types (e.g., journal or conference proceedings papers), and Figure 2 shows the papers per year, divided by type. As the graphs indicate, no relevant studies were found for the years 2000–2004 through the searches performed, confirming that the research interest is quite recent. The diagram also illustrates temporal trends, indicating increased research activity in recent years, and differentiates between source types, showing a dominance of journal papers over books.

3.4. Results of the Literature Review

For its meticulousness in analysis and literature mapping, the software chosen for bibliometric analysis is CiteSpace 6.3. R3 (64 bit) Advanced© [24], an open-source Java-based application. To correctly interpret the data generated by the bibliometric analysis software, the following key evaluation parameters are provided:

• the centrality of the interactions (w); according to graph theory, it is an indicator used to assess the importance of nodes within a network [25]; it measures how often a node acts as an intermediary between two other nodes in the network [16];
• burst strength (BS); this is an indicator that quantifies the citation burst, which refers to the time window during which an author or paper suddenly experiences a sharp increase in citations [26];
• mean silhouette (S) and modularity (Q); these are used to measure the overall structural properties of the network.

The results obtained from the bibliometric analysis of the selected literature are presented and discussed below, with reference to the presentation of clusters and the citation burst detected from the analysis of keywords.

The discussion focuses on the main trends and challenges in the field of V2G that have emerged from the classification of clusters, with the aim of providing a comprehensive overview of the key attributes and variables essential for understanding and modeling the charging and usage behaviors of EVs in a V2G context, as they directly influence the ability to respond to dynamic energy demand and maximize benefits for both users and the electrical system.

The process of cluster creation is a crucial phase for the analysis of citation networks, as it allows for the identification and understanding of the underlying structures within a research field. Initially, the full library is loaded and saved in a reference directory to be used later as input for a newly created project. The dataset has to contain sufficient information, such as citations and references, to enable CiteSpace 6.3. R3 (64 bit) Advanced© to build a meaningful network.

The citation information within the dataset was then used to build a network where the nodes represent the reviewed studies and the connections represent the citations between these documents. The connections between the nodes are often based on co-citations, which indicate when two studies are cited together in one or more subsequent documents. Once the network is built, clustering algorithms are implemented to identify groups of nodes (documents) that are strongly interconnected. Each cluster represents a group of documents that share a common theme, a research line, or a similar topic.

After identification, the clusters need to be interpreted and characterized. There are different approaches to use to identify the representative labels for each group. Labels can be based on noun phrases extracted from the titles of articles, their abstracts, or keyword lists. The algorithms mostly used for this purpose include Latent Semantic Indexing (LSI) and Log-Likelihood Ratio (LLR). The latter is often preferred for its ability to identify unique and representative labels [24]. In addition, it is useful to explore the quality of homogeneity within each cluster. It can be performed through different metrics, such as modularity (Q) and silhouette values (S). Therefore, they are used to measure the internal cohesion and separation between clusters. A high modularity value indicates that the network is well divided into distinct clusters, while a high silhouette value suggests that nodes within a cluster are strongly correlated with each other.

Finally, the clusters and their connections are visualized on a network map. This visualization allows researchers to explore the clusters, see how they are interconnected, and interpret the citation dynamics and research trends within the field of study.

In summary, identifying clusters involves building a citation network, identifying thematic groups, labeling them, and evaluating their internal coherence. This process provides a structured and in-depth view of the dynamics within a research field, facilitating the identification of emerging trends and key work.

In this research, 181 groups were identified, representing groups of scientific documents that share a high degree of similarity in terms of citations, keywords, or other attributes. Displaying too many clusters could make the map too complex and difficult to interpret, so only those that significantly contribute to understanding research trends or citation dynamics are represented. In this case study, there are 10 identified thematic clusters. Thematic clusters, represented in Figure 3 and Figure 4, are those with high silhouette scores and internal coherence because they are easier to interpret and represent groups of articles with well-defined themes. In Figure 3, the cluster view is represented, showing a hybrid network of increasing citation terms and highly cited references. Nodes represent individual publications within the clusters, and links represent the connections between them. The labels and annual citations of each cited reference are turned off in this image, as it aims to represent the most significant clusters, each in a different color. The most significant groups are represented, considering those obtained using the LLR algorithm, as it is preferable for its ability to identify unique and meaningful labels from a thematic perspective [17]. Significant clusters are those with the most publications. Clusters are numbered in the descending order of the cluster size, starting from the largest cluster #0. The cluster label reflects the main content of the publications it encompasses. In Figure 4, a temporal representation of the most significant clusters is plotted; it describes a profile of each cluster over time. For each cluster, the evolution of publications over time can be observed, and the peaks represent the highly active study within the given cluster.

Table 3. Summary of the largest 10 clusters.

ClusterID	Size	Silhouette	Label (LLR)	Average Year
0	51	0.869	Comprehensive review	2019
1	38	0.871	Increasing electric vehicle	2019
2	37	0.914	Charging behavior	2017
4	23	0.986	Integrating electric vehicle	2022
6	19	0.982	Considering battery energy wear	2018
8	17	1.000	Stochastic connection	2012
9	16	0.987	To-grid ancillary service	2013
10	14	0.984	V2G uncertainty	2021
12	13	1.000	Integrity	2013
18	7	1.000	Multi-level charging station	2020

shows the major identified groups along with their silhouette values, the number of studies, and the average year. The silhouette column shows the homogeneity of a cluster. The higher the silhouette score, the more consistent the cluster members are, provided that the clusters in comparison have similar sizes. If the cluster size is small, then high homogeneity does not mean much. The average year of publication of a cluster indicates whether it is formed by generally recent papers or old papers.

The global landscape of V2G research is characterized by contributions from numerous journals and academic institutions from all over the world, reflecting the international nature of this topic. In Figure 5, the countries with the highest number of V2G studies and the corresponding amounts are shown.

Geographic analysis clearly highlights the level of collaboration between countries and institutions in research. Setting “Country” as the type of nodes, a network of nodes and edges was generated, as shown in Figure 6. The largest nodes represent countries with a large number of studies, while the different colors of the edges indicate collaboration relationships in various years of publication.

Leading journals have played fundamental roles in advancing the technological application of V2G and, consequently, in the research to define the attributes/variables necessary to predict the charging demand of EV [27]. The analysis highlights the significant contributions of various researchers.

Table 4. Authors with the highest number of citations.

Author	Institution	Country	Citation	Year	Centrality
Kempton, W	University of Delaware	USA	212	2008	0.10
Sortomme, E	University of Washington	USA	95	2012	0.10
Zhang, Y	Tsinghua University	China	89	2018	0.02
Wang, Y	Georgia State University	USA	86	2017	0.02
Clement-Nyns, K	K.U. Leuven	Belgium	75	2010	0.07
Han, S	The University of Tokyo	Japan	72	2012	0.06

lists the six authors with the highest number of citations who have made significant contributions to V2G research. The central year of the citations is also given. Taking into account their countries of origin, it is evident that the results are consistent with the geographical analysis previously presented, highlighting the roles of the United States and China as the main actors in the V2G studies.

Regarding the country analysis, the review of the literature follows the trend of incentives and policies implemented by different governments. In fact, the findings indicate that EVs are increasingly being promoted as a key solution to reducing greenhouse gas and pollutant emissions. Numerous countries are supporting the adoption of EVs through incentives and technological advancements, with more than 17 nations aiming to achieve 100% zero emission vehicles by 2050 [28]. The global EV market is growing rapidly, and technology is improving for both personal and commercial transport. In general, there are many pilot projects, with a significant percentage in Europe [29]. The projects vary significantly in scale and learning objectives, but more and more pilot projects are including service experimentation and user interaction. In Italy, there are currently no active V2G pilot projects. This underscores the importance of the theoretical exploration of V2G technology and the creation of predictive models for its implementation in Italy. Therefore, a significant starting point for researchers is the analysis of scientific studies carried out in leading countries on the ecological transition and adoption of EVs.

Based on this analysis, it is therefore possible to focus on the need to investigate the use of electric vehicles, as well as their parking, to assess the potential to participate in V2G. After identifying the main clusters, the analysis focused on the clusters with the highest “silhouette” index, and within each, the studies with the highest number of citations have been reviewed. Subsequently, based on the other parameters discussed in Section 3.4, papers with the highest “centrality” index have been examined, as these papers often serve as key references for the study in question and are connected to other works that could further deepen the research. The “burst” index was also crucial, as it guided the analysis to periods when the topic gained sudden relevance, leading the research to identify the challenges, techniques, impacts, and perspectives of V2G. Subsequently, it was understood that the demand for parking in V2G technology is characterized by a series of key attributes and variables, which are essential to understand and model the charging and usage behaviors of EVs in a V2G context. Among the main ones, the following thematic clusters have been identified:

• parking and charging time;
• parking location;
• vehicle usage model;
• charging energy and power;
• user behaviors and preferences;
• sociodemographic factors;
• environmental conditions.

• Parking and charging time

The concept of “parking and charging time” refers to the duration and dynamics of the EV charging process in the V2G mode. This approach not only optimizes the use of EV batteries but also contributes to the stability of the electrical grid. Recent studies have examined the necessary parking time for effective charging and the implications of the V2G system for the integration of renewable energy sources. Numerous studies have been developed to analyze the charging and parking time in V2G contexts, focusing primarily on optimizing charging strategies and their impact on the electrical grid and the end-user. Most of these studies have taken place in pioneering countries in electric mobility, such as Japan, Germany, and the United States. For example, research by the University of Delaware examined the effect of V2G charging on grid performance and environmental sustainability [29]. These studies found that the average parking time for electric vehicles varied significantly depending on vehicle usage and available infrastructure, with times ranging from a few hours to whole nights. Furthermore, it was observed that the benefits of V2G increased significantly with favorable policies and a well-developed charging infrastructure.

Future research should focus on more advanced predictive models to manage charging and on developing optimization algorithms that account for grid variables and user behaviors. Furthermore, it is crucial to develop a smart and interconnected charging infrastructure that facilitates the seamless integration between electric vehicles and the electrical grid.

• Parking location

This concerns the optimization of electric vehicle parking locations to maximize the benefits of V2G systems. This includes selecting strategic parking areas for both charging and energy return to the electrical grid. The location of parking spaces can significantly influence the efficiency of the V2G system, as areas with high availability of electric vehicles and easy access to charging infrastructure are crucial to the effective management of energy resources. Studies in this field analyze how different parking locations affect charging distribution, grid reliability, and the costs associated with the implementation of V2G systems.

Specific studies on this topic are still limited but growing, with research focused on cities and urban areas. Countries such as Germany, with an advanced electric mobility infrastructure and a strong commitment to sustainable energy, are at the forefront of this field. Recent studies have examined the optimization of V2G parking in German urban areas, exploring how the choice of locations can improve grid efficiency and reduce management costs. Research has highlighted that the optimal location for V2G parking tends to be in densely populated urban areas or near critical nodes of the electrical grid, such as shopping centers, office buildings, and residences with an advanced charging infrastructure [30]. These areas not only offer greater availability for V2G but also provide a more significant interaction with the grid, contributing to its stability and resilience.

Future research should investigate the analysis of V2G parking distribution models that consider not only the density of electric vehicles but also the accessibility requirements and local grid capacity. It is essential to develop spatial optimization algorithms that take into account variables such as urban traffic, the availability of charging infrastructure, and the variability of energy demand. The standardization and integration of the parking management and monitoring systems of V2G will be crucial to facilitate large-scale implementation and ensure the effectiveness of the V2G systems. Furthermore, the collaboration between local authorities, grid operators, and technology developers will be vital to designing parking solutions that are economically sustainable and technologically advanced.

• Vehicle usage model

The “Vehicle Usage Model” is a way to analyze how often EVs travel and the distances they cover during these trips and how these factors impact the effectiveness and implementation of V2G systems. The frequency and distance of trips are crucial to determining the availability of stored energy in vehicles and their ability to return this energy to the grid. Recent studies have shown that higher frequency and longer trips can improve the efficiency of V2G systems as vehicles tend to store and return energy more frequently thus contributing to a more dynamic management of energy resources.

Studies on this topic are increasing, with a growing body of research focused on how travel habits influence the potential of V2G. Countries such as the United Kingdom, Germany, and the United States have been at the forefront of this research. In the UK, the “V2G Innovation” project analyzed the frequency and distance of EV trips, highlighting how these factors can affect the effectiveness of V2G systems and their integration with the grid.

Future research should focus on developing algorithms that can dynamically adapt to changes in users’ travel patterns and optimize charging and discharging strategies. Furthermore, it is necessary to improve the collection and analysis of data related to vehicle trips to refine predictions and improve the efficiency of V2G systems.

• Charging energy and power

This topic concerns the analysis of the amount of energy transferred between EVs and the electrical grid, as well as the power with which this exchange occurs in vehicle-to-grid mode. This aspect is crucial for understanding the efficiency of V2G, its ability to support the electrical grid during peak demand periods, and its impact on the life expectancy of vehicle batteries.

The energy and power required for charging determine how quickly and effectively a vehicle can be charged, while the capacity of the battery defines how much energy can be stored and subsequently provided to the grid. The residual charge, or the remaining energy in the battery after a trip, affects the vehicle’s ability to participate in V2G operations, impacting both grid stability and the optimization of energy resources.

Research in this area is expanding, with significant contributions from countries such as Germany, Japan, and the United States. Research has shown that higher battery capacities and optimized charging power can significantly improve the effectiveness of V2G systems by increasing the amount of energy available for grid support and improving overall system efficiency. Studies have also highlighted the importance of managing the residual charge to ensure that vehicles can participate in V2G operations without compromising their performance for daily use.

Future research should focus on developing advanced algorithms to optimize energy management based on battery capacity and charging power. It is necessary to explore how different charging strategies and battery technologies impact the performance of V2G. Furthermore, improving the methods for monitoring and predicting residual charge will be crucial to improving the reliability of V2G systems [27]. Research should also address how various charging infrastructures can be adapted to support efficient V2G operations, considering both residential and commercial settings. Collaboration between researchers, industry stakeholders, and policy makers will be essential for developing and implementing solutions that maximize the benefits of V2G technology.

• User behaviors and preferences

This topic explores how EV owners interact with V2G systems and the factors that influence their decisions to participate in and use these technologies. This includes analyzing the motivations of users to join or avoid V2G programs, such as economic considerations, convenience, and concerns about battery longevity. User behavior models significantly impact the effectiveness of V2G systems [31] as high and well-managed participation can optimize the benefits of the grid and improve energy efficiency.

Recent studies have identified that economic incentives, ease of integration, and transparency of benefits are crucial in promoting user participation in V2G programs. Research on user behavior and preferences in V2G systems has been conducted predominantly in countries such as the United Kingdom, Germany, China, and the United States, where electric mobility and V2G technologies are more widespread. Research indicates that most users are attracted by the potential economic savings and environmental benefits of V2G but often express concerns about battery wear and the complexity of charging operations. In addition, the ease of use and availability of an adequate infrastructure are key factors in the adoption of V2G.

Studies show a significant variability in preferences, and some users are more willing to participate in V2G if incentivized by dynamic pricing or reward programs. To improve the adoption of V2G, future research should focus on a deeper understanding of user motivations, developing predictive models that anticipate long-term behaviors and preferences. It is crucial to explore communication strategies that educate and inform users about the benefits of V2G, as well as to improve the user experience through more intuitive interfaces and reduced operational complexities. Furthermore, the implementation of V2G will require policies that encourage active participation, such as discounted charging and energy return rates, along with the development of a widely available and user-friendly charging infrastructure. This holistic approach could help overcome barriers to V2G adoption and promote a more broad and more informed participation.

• Socio-demographic factors

This topic explores how the sociodemographic and psychological characteristics of the users influence the adoption and use of V2G technology. These factors include age, income, education level, and attitudes toward technology and the environment, all of which shape the users’ decisions to participate in V2G programs.

Numerous studies have been developed primarily in countries of advanced electric mobility, such as Germany, the United States, and in the Nordic region, to examine the impact of sociodemographic and psychological factors on the adoption of V2G [32]. Research has highlighted that younger users, those with higher education levels, and those with higher income are generally more inclined to adopt innovative technologies such as V2G. Psychological aspects, such as the perception of control over one’s energy and environmental concerns, play a significant role in motivating participation. However, skepticism towards technology and lack of knowledge can hinder adoption, especially among older demographics or those with lower educational levels.

To improve the adoption of V2G, it is necessary to deepen our understanding of the sociodemographic and psychological variables that influence user behaviors. Future research should develop a more precise market segmentation based on these factors to create targeted communication and marketing strategies. It is also crucial to explore educational approaches that increase awareness and confidence in V2G, tailored to different demographic needs. Additionally, to promote greater adoption, it is important to develop specific incentive programs for less inclined demographic groups, such as the elderly or less educated individuals. An inclusive and personalized approach could help overcome sociodemographic and psychological barriers, accelerating the large-scale adoption of V2G.

• Environmental conditions

This topic examines how climatic and environmental conditions influence the effectiveness and functionality of V2G technology. Environmental factors such as temperature, humidity, and exposure to extreme weather events can affect the charging process, the lifetime of EV batteries, and the stability of the power grid.

Several studies, carried out primarily in countries with significant climatic variations, such as the United States of America, Japan, Canada, and Germany, have investigated how environmental conditions impact the implementation of V2G. These studies have revealed that extreme temperatures, both hot and cold, can reduce battery efficiency and increase charging times, negatively affecting the vehicle’s ability to deliver energy back to the grid. Furthermore, high humidity and weather events, such as storms or floods, can damage the charging infrastructure, reducing the availability and reliability of V2G in certain regions [33].

To ensure the effectiveness of V2G under various environmental conditions, future research should focus on developing more resilient and adaptable charging technologies and batteries for extreme climates. Strategies to protect the charging infrastructure from extreme weather events, such as the implementation of protected charging stations and advanced energy management systems, should also be explored. Furthermore, integrating climate models into energy demand and supply forecasts could improve V2G management, adapting it to variable environmental conditions. Adopting these approaches will contribute to making V2G more robust and sustainable, facilitating its implementation in a wide range of environmental contexts.

3.5. Discussion

Research has highlighted the importance of considering the psychological and behavioral aspects of users in the design and implementation of V2G strategies.

Key factors in predicting EV charging demand include the number of trips, distance traveled, and energy consumption, as well as the location of the parking lot, power, and duration. Accurate demand forecasting requires an understanding of the temporal (relative to time) and spatial (relative to place) usage patterns, which can be identified through surveys and operational data. However, the need to identify strategies and policies for aggregate V2G participants becomes relevant, given that it is not possible to participate in the electricity market individually [34].

Additionally, to fully achieve success of V2G services, it is necessary to combine the energy storage characteristics and the traffic characteristics of electric vehicles and fully consider the user behavior to achieve a more accurate description of the V2G model in order to further analyze the role of the control of the energy storage of the EV cluster and study the impact of V2G on the grid load. The majority of existing studies solely take into account traffic and travel statistics; they do not carry out a thorough assessment of a variety of criteria, including user behavior, willingness preferences, charging and discharging methods, and the effect of charging on the grid [35]. It is challenging to point out users’ acceptance. Thus, a pilot survey carried out in Italy where about 300 people were interviewed can be recalled. Up to 74% of interviewees declared themselves to be interested in participating in V2G, and half of them (50.4%) indicated that they would like to maintain, at the end of the day, a residual level of the battery not less than 60%. Finally, only 74.8% were willing to receive compensation equal or higher to the energy cost paid for charging, although 2.9%, pushed by environmental feelings, were willing to receive less than what they paid for. The remaining ones preferred not to give an evaluation.

Concerns about battery degradation and economic issues are the main barriers to the widespread adoption of V2G. The frequent charging and discharging required for V2G services can lead to reduced battery capacity, which discourages users. Privacy and security concerns also limit the adoption of V2G, as users must share detailed data with EV aggregators, exposing themselves to potential risks. Addressing these issues is crucial for the successful integration of electric vehicles into the grid.

4. Conclusions

The presented literature review on forecasting models for the aggregate capacity provided by EVs in support of the electricity grid highlights several critical input attributes that impact the effectiveness and accuracy of these forecasts. Analyzing these attributes is crucial to optimizing the integration of electric vehicles into the grid, improving energy management, and ensuring a proper response to fluctuations in energy demand and supply.

The key input attributes to forecast aggregate capacity include battery capacity, charging time, and available residual energy, as well as travel patterns and user charging habits. Battery capacity directly affects the amount of energy that can be stored and subsequently delivered back to the grid, while travel patterns and charging habits determine the frequency and amount of energy available at any given time. In addition, the residual charge in vehicles, which represents the energy left in the battery after a trip, is crucial to accurate aggregate capacity forecasts.

Studies highlight that environmental variables, such as extreme temperatures and humidity, as well as user behavior factors, such as frequency and distance of travel, significantly impact aggregate capacity. Climatic conditions can affect battery performance and availability, while user habits determine when and how often vehicles are available to supply energy to the grid.

The effectiveness of the forecasting models is also influenced by the technologies and charging infrastructure available. The implementation of smart charging stations and advanced energy management is essential to optimize the use of the aggregate capacity of electric vehicles. Furthermore, the electricity grid management systems must be able to integrate and coordinate the energy resources provided by electric vehicles effectively.

Despite the progress, there are still significant challenges in accurately forecasting aggregate capacity. Variability in user behavior and environmental conditions requires the development of more sophisticated and adaptable predictive models. Improving the collection and analysis of data on charging behaviors and environmental conditions and developing advanced algorithms that can anticipate and manage fluctuations in energy availability are essential.

To optimize the use of the aggregate capacity of electric vehicles, supportive regulatory and policy frameworks are needed to promote the adoption of advanced charging technologies and the integration of vehicles into the grid. Policies should encourage user participation in V2G programs and invest in infrastructure that facilitates vehicle–grid interaction.

Therefore, the transition to a decarbonized transportation system is seen as difficult in comparison to other economic sectors. Decarbonizing urban mobility can be achieved through a variety of technological and regulatory approaches. But a decarbonized urban transportation system cannot be achieved only by technical advances. They have to be supplemented with actions that target travel patterns and cause a change in the daily mobility behaviors of people. To achieve climate neutrality, reduce traffic, air pollution emissions, noise pollution, and other negative consequences of excessive reliance on fossil fuel-based transportation, organizations in the public sector (local and regional authorities) and the private sector (businesses, organizations, and institutions) must develop urban mobility management plans that include such challenges in mobility strategy.

Such a literature review can be seen as a first step to guiding future research to include a manifold picture of the user into V2G research, and the findings guide its further advancement. How users’ requirements and demand characteristics are included in innovative business schemes should be addressed in the review, resolving stakeholders’ concerns and establishing supportive regulatory frameworks. In fact, business schemes do not only have to rely on the advancements in technical and transportation services but also need to introduce commercial and operational issues, including the requirements of EV owners.