Integration of electric vehicles (EVs) into the smart grid has attracted considerable interest from researchers, governments, and private companies alike. Such integration may bring problems if not conducted well, but EVs can be also used by utilities and other industry stakeholders to enable the smart grid. This paper presents a systematic literature review of the topic and offer a research framework to guide future research and enrich the body of knowledge. The systematic literature review presented in this paper does not contain all the material available on this subject. It does, however, include most of the key publications readily available in a power-utility or technical-reference library together with some of the earlier papers in the field (the anchor papers). For this review, we selected appropriate digital sources (digital libraries and indexing systems; IEEE Xplore and Web of Science), determined the search terms, and conducted a broad automated search. This article also details the components of the research theme—EV integration into the smart grid—as well as its accompanying use cases. The analysis of the relevant papers indicated four types of key research concerns: power-grid, power-system, and smart-grid reliability and the impacts of changes on them. These results can help guide future research to further smart-grid development. Future research can also expand the reach of this research to address its limitations in scope and depth.
Introduction and research background
Integration of electric vehicles (EVs) into the smart grid can be leveraged by utilities and other industry stakeholders to bring several benefits and to enable the smart grid. Mwasilu et al. (2014) emphasized the importance of vehicle to grid (V2G), an example of services on the grid that will allow the shift of the static power system to the efficient virtual power grid. San Diego Gas and Electric (2015) reported that deploying networks of EV charging stations can stabilize and bring advantages to the grid in locations with excess power; EV charging can absorb mid-day solar overgeneration and alleviate wind curtailment at night. Sultan et al. (2017) highlighted how charging EVs when nondispatchable assets, such as solar and wind generators, are producing more energy can help flatten out the demand curve and reduce the extent to which supply suddenly escalates. All these characteristics reduce system costs, benefit ratepayers, and improve the profitability of generators (San Diego Gas and Electric 2015).
On the international stage, the EV industry has been prominently used as a tool by countries to meet their carbon-footprint-reduction goals. EVs also have spurred a potential new avenue of electricity sales while at the same time impacting maintenance costs by adding to peak loads and changing historical grid-load patterns. The integration of EVs with electrical grids is giving rise to the concept of smart grids. This integration can come from potential bidirectional charging (V2G), grid storage research, and innovative energy generation (Denholm et al. 2015).
EVs can potentially serve a dual purpose, an alternate form of grid storage offloaded to the public. It can allow the vehicle owners to be compensated for providing electric service when the vehicle is not in use, helping to reduce the cost of ownership. Also, the quality of life for large urban centers can increase due to the potential opportunity to move emissions from large population centers. This relocation of energy production can improve air quality and public health in metropolitan cities, while remaining emission production further decreases as remaining energy needs are met by renewable resources (Denholm et al. 2015).
However, it is important to note that the integration of many EVs into the electric power system is a major challenge which requires a thorough evaluation and examination in terms of economic impacts, operation, and control benefits at ideal circumstances (Mwasilu et al. 2014). Large-scale integration of EVs into the smart grid may bring a series of problems if EVs are not integrated carefully into the smart grid. According to Green et al. (2011), several works analyzed the impact of EVs on the power-distribution system. Examples of the anticipated adverse impacts are power transformers overheating and the need for new investments in distribution facilities (Mwasilu et al. 2014).
A major challenge is the impact of simultaneously charging many EV batteries on the power network, this could change the overall load profile of the grid significantly. The issue is that the charging behaviors of EVs are only regulated by the customer so it’s not within the control of the grid operators and the electric utilities. The risk of grid overload can lead to a degradation of the grid performance, bad power quality and/or voltage deviations, even a blackout of the whole power system if EV charging is not managed properly. However, time-of-use rates can guide an EV charging, and V2G benefits can be easily ramped up and down in response to the load on the system, improving voltage regulation and droop control.
The high cost of integration due to inadequate charging infrastructure and competition from other energy-storage technologies are additional challenges to EV integration. Pumped hydroelectric storage is considered, for example, to be much cheaper than the V2G option. According to Hernández-Moro et al. (2012) and Mullan et al. (2012), pumped hydro has greater efficiency (up to 99%) and can store energy for long periods as compared to the EV battery (Hernández-Moro et al. 2012).
On the other hand, EVs have advantages when operated in the V2G mode to feed power to the utility grid. The primary advantages stem from the EV battery’s ability to provide power when needed. EV technology can provide grid support by delivering ancillary services such as peak power shaving, spinning reserve, and voltage and frequency regulation (Ehsani et al. 2012).
Peak shaving means reducing the highest demand levels at the power plant. From a utility perspective, EVs can be viewed as both dynamic loads which may not be easy to predict, but also potential backup for the electric grid through the V2G technology (Mwasilu et al. 2014). So, EVs can respond to changes in demand, they provide a spinning reserve and can dramatically reduce the need to use expensive peaking plants, savings that utilities can pass on to their customers in the form of lower energy costs.
Another point is that mismatches between power supply and demand can lead to oscillations in the supplied voltage, phase angle, and frequency. These oscillations degrade power quality, possibly damaging utility customers’ sensitive electronic equipment. EVs can both provide and absorb power and energy so to help dampen both intra- and interarea power oscillations. Mwasilu et al. (2014) claim that EVs have the potential to assist in voltage and frequency regulation, thus enhancing electric-grid reliability and power quality.
A modern grid that is well equipped to handle the additional EV loads and reap the benefits from the intersection of the EVs and the electric power network could contribute to an enhanced grid with real environmental benefits for all customers.
Thus, more research is needed, and it is critical especially with the increasing penetration of EV charging into the electric power system. Unease about global warming, energy security, and the current health of the environment has caused more interest in EVs. The existing power grid suffers from unpredictable and intermittent supply of the electricity from wind and photovoltaic (PV) solar sources, so EV charging and V2G services are a promising solution to balance the generation from renewable energy sources (International Electrotechnical Commission 2012).
The intent of this paper is to present a systematic literature review of EV integration into the smart grid and develop a research framework to guide future research and enrich the body of knowledge. A systematic literature review is a particularly influential tool in research; it allows a scholar to gather and recap all the information about a specific field (Spanos and Angelis 2016).
This paper is organized as follows. In the “Introduction and research background”, we introduce and present the topic’s research background. In the “Methodology”, we delineated our research methodology. The “Results” describes the results of the review and offers a bibliography of anchor papers. The “Discussion” discusses the results. Finally, “Conclusion” concludes.
This paper’s systematic literature review follows the three stages defined by Kitchenham (2004) and Kitchenham et al. (2009): planning, conducting, and reporting. In the first stage, we create the research protocol. The second stage is the actions of reviewing literature based on the protocol, and the last stage manifests the article’s results section.
The research protocol established in the planning stage guided the systematic literature review. The first step is to identify the need for a systematic review. Although several studies try to investigate EV integration into the smart grid, no comprehensive review has so far summarized all these studies and offered a deeper insight. Therefore, the need for a systematic literature review providing solid foundations and equipping researchers with pertinent information is clear.
To develop the review protocol, we define the research questions, select the search strategy, establish the study’s inclusion and exclusion criteria, select quality-assessment criteria, and identify the data to be extracted from the studies. Three research questions guide our review.
What are the themes within research on EV integration into the smart grid?
What are the results of the studies?
What research methods are used?
To conduct the systematic literature review, we adopted a broad automated search, a method that includes the selection of the most appropriate digital sources (digital libraries and indexing systems) and the determination of the search terms (Spanos and Angelis 2016). We selected two digital libraries, IEEE Xplore and Web of Science, as they are the most relevant digital sources in electricity-infrastructure research. Our searches relied on the titles of the papers to avoid retrieving irrelevant papers. The search strings we used were.
IEEE Xplore Boolean Phrase:
("Document Title":electric vehicle) AND ("Document Title":smart grid)
and refined by
Content Type: Conferences, Journals, and Early Access Articles
Web of Science Boolean Phrase:
TOPIC: (electric vehicle AND smart grid)
DOCUMENT TYPES = (ARTICLE). Timespan = All years. Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI
Inclusion and exclusion criteria
According to Spanos and Angelis (2016), a systematic literature review’s inclusion and exclusion criteria must be distinct and clearly stated. We used three selection criteria and one exclusion criterion in our systematic review.
1.Full-article publication (not just an abstract)
1.Duplicate publications (to avoid double-counting of studies)
Once each paper has passed the relevancy test, we peruse the papers based on the quality-assessment criteria. These rigorous criteria ensure all studies included in the systematic literature review achieve an adequate level of quality. After consulting with domain experts and independent researchers, we decided to include each article for the final analysis if it satisfies three criteria:
The data description is available, and its existence can be verified.
Research methodology is clearly described.
Results presentation is clear and impactful.
Once the articles had passed the inclusion and quality-assessment criteria, we recorded the data features of the papers for the final analysis: (1) title, (2) authors, (3) publication year, (4) authors’ affiliations, (5) journal title, (6) number of papers citing the article, (7) abstract, (8) research questions, (9) research methodology, and (10) results.
For this survey, we analyzed results from 724 papers relevant to integration of EVs into the smart grid, 260 papers from the IEEE Xplore and 464 papers from the Web of Science. The analysis process considered the research results, as stated by the authors, and was mainly conducted throughout the abstract, conclusion, and results sections if required. Tables 1 and 2 present counts of document identifiers and publication years. Table 3 presents summary statistics.
The analysis of the relevant papers indicated four types of key research concerns.
• Assessing power-grid reliability considering EV integration.
• Improving power-system reliability considering EV integration.
• Planning for smart-grid reliability in preparation for EV integration.
• Evaluating the impacts of adding EVs and charging stations on grid reliability.
The research methodologies of the included papers can be classified into two categories, analytical and simulation. Analytical techniques “represent the system by mathematical models and evaluate the reliability indices from these models using mathematical solutions” (Faulin et al. 2010). Simulation views problems as a series of real experiments, such as Monte Carlo simulation which is used for the prediction of probability for various outcomes when dealing with random variables. Table 4 presents the statistics for the different research methods. Finally, 58 papers used the Monte Carlo simulation method.
Based on the results of the systematic literature review, we have constructed a smart-grid–EV-integration framework highlighting research components of EV integration into the smart grid, with goals, themes, use cases, and research tools.
Research contribution: framework
In this section, we propose the smart-grid–EV-integration framework with three main research domains (Fig. 1), solutions for charging, microgrids and distributed generation, and managing power demand. This framework recognizes these three EV-integration domains as cardinal research-movement drivers for studying the benefits and challenges of integrating EVs into a smart grid. We also propose technological and socioeconomic solutions. Next, based on our literature analysis, we present common themes within the domains. The first theme, integrating EVs, drives the enhancement of EV technologies. The second theme, advancement of the smart grid, drives the advancement of distributed generation. The third theme, smart-grid reliability with EV applications, drives power-demand management and smart-grid reliability in the context of EV integration.
The use cases in the research framework are classified based on under which research theme(s) they fall (see Fig. 1). Smart-charging infrastructure use cases deal with the reformation of current structures to achieve both physically and technically suitable charging solutions. “Apart from the technological innovation of EV, effective charging infrastructure plays a fundamental role in supporting the wider adoption of EV” (Chen et al. 2020). An important area of infrastructure adjustments is, for instance, planning for EV smart charging stations, involving both geographical research and charging-management system implementation.
According to Zhou et al. (2021), commercial buildings with EV charging stations and PV panels are common prosumers in the smart grid. Thus, “the energy management of commercial buildings has significant potential for electricity cost saving, load levelling, and distributed generation consumption.” Site-integrated EV charging solutions for workplaces and other business or commercial areas are interesting use cases, where growth in EV smart-grid penetration calls for improved charging load management. Algorithms, charging and discharging impacts on smart-grid peak loads, and cost benefits are worthy of investigation.
Residential-building use cases explore the impacts of EV integration on a household, very often in combination with implementation of photovoltaic or wind-energy appliances. Charging solutions can have economic benefits. Residential storage, on the other hand, like EVs, can be both flexible and time shiftable and can significantly increase residential-demand elasticity (Rassaei et al. 2015). The use cases explore how to manage smart-home energy in a residential smart grid and how energy stored in the EV can be used for distributed generation either for the household or for a larger residential area. This area also involves associated-risks investigation, including increased power losses, overloads, and voltage fluctuations, and how they influence the smart grid.
EV Cybersecurity use cases investigate EV integration into the smart grid from the perspectives of energy safety, usage, user information, and transactions. According to Sanghvi et al. (2021), EV integration can potentially leave the grid “vulnerable to cyberattacks from both legacy and new equipment and protocols, including extreme fast-charging infrastructure.” Since EV participation in the smart grid will unavoidably partially depend on accessing power and communication networks and systems, such a system might become a target of such attacks. Thus, cybersecurity and energy security are crucial areas of research around smart-grid–EV integration.
Technological enhancement of smart charging and discharging evaluates problems caused by advancements in charging technologies to identify strategies, benefits, and risks and evaluate and propose how EV charging and discharging can help improve a smart grid’s flexibility and effectiveness in response to energy fluctuations in the distribution area. According to Aghajan-Eshkevari et al. (2022), “it is essential to manage the charging and discharging of EVs [that] can be also considered sources of dispersed energy storage and used to increase the network’s operation efficiency with reasonable charge and discharge management.” Use cases may also include improvement of fast-charging technologies, battery technologies, wireless charging, and roadway electrification.
Utility-scale energy storage solutions help maintain a balance between energy generation and consumption in the smart grid. As the EV market grows, more degraded batteries can be further used for other purposes. “In particular, the repurposing of EV [lithium-ion batteries] in stationary applications is expected to provide cost-effective solutions for utility-scale energy storage applications” (Steckel et al. 2021). Use cases in this category involve addressing questions of battery recycling sustainability, degradation, participation of EVs in the load balancing through the dispatch of batteries, and other areas.
Demand-response management and pricing use cases focus on both technical and socioeconomic areas of energy supply and demand for smart-grid operators, charging-station operators, and EV users and their demand response during the peak time. While smart-grid–EV integration has a significant impact on energy demand, they also represent an energy resource. “Therefore, in smart grid, the consumer demand is expected to be controlled so as to coordinate with the electricity generation, which is the main objective of demand response management” (Yu et al. 2016). Power demand response can positively impact peak shaving and load balancing, and open new possibilities for energy market and energy trading through energy-aggregator demand-response programs (Ren et al. 2021).
Evaluation and maintenance throughout the addition of EVs can involve mathematical modeling for both technological and economic evaluation of EV deployment’s impacts on the smart grid, predictive maintenance solutions for distribution transformers under the increased EV load, energy-dispatch strategies, involved systems’ life cycles, charging behavior, and other methods to maintain a balanced smart grid with growing numbers of EVs.
Energy-management systems are crucial to the smart grid’s ecosystem. Integrated EVs can contribute to the important task of effectively maintaining the power supply–demand balance and decrease the peak load. Energy-management systems also handle sharing or exchanges among different energy sources, including EVs, to establish reliable and effective supply. Use cases in this category involve many topics like energy-quality management systems, optimization systems, energy-consumption control systems, and scheduling systems. Energy-management systems are also closely linked to demand management and response (Meliani et al. 2021).
Public safety in EV adoption must be considered. As countries around the globe strive to meet energy objectives while decreasing their climate impact, it is crucial to identify and regulate new EV-related technologies to protect the public from potential undesirable effects. A growing number of EVs increases risks from, for instance, disposed or damaged batteries. Therefore, proper risk-mitigation techniques—professional training, recycling policies, and standardization—must ensure public safety and environmental protection (Brown et al. 2010).
Mobility behavioral science explores the questions associated with human behavior’s impact on transportation and energy and is crucial for understanding of how future EV and smart-grid technologies should be implemented. According to Rames et al. (2021), “exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals.” Thus, research in this area involves modeling EV owners' usage and driving behavior for effective power planning and operations, drivers’ motivation to participate in peak shaving, and implementation of localization technologies to adjust charging behavior and manage demand.
We aim to help resolve problems in the EV-integration field using such tools as machine learning, deep learning, networking, distributed systems, middleware systems, embedded systems, optimization and control, databases and big data management, human–computer interfaces, behavioral information systems research, design-science research, and mixed-methods research. When applied to the EV-integration field, these tools have the potential to significantly increase knowledge in the field and solve some of the problems it faces.
Smart-grid–EV-integration research framework founded on the previous literature
In addition to our smart-grid–EV-integration perspective, Fig. 2 illustrates our smart-grid–EV-integration research framework founded on the previous literature and our view of the domain. The framework focuses on two main areas: EV integration and the smart grid. The first research focus involves systems connecting EVs, transportation infrastructure, power grid, buildings, and renewable energy sources (Meintz 2022). Adjusting infrastructure, charging solutions, and associated costs are all possible goals in the EV-integration research focus.
The smart grid (the second research focus) refers to the electric grid, a network of transmission lines, substations, transformers, and more that deliver electricity to a set location, integrated with digital technology allowing for sensing and two-way communication between the utility and its consumers (Department of Energy, Office of Electricity 2022). Adaptation of V2G systems in the smart grid, smart charging infrastructure, grid planning, and impacts of EV charging on the smart grid’s reliability are the possible goals within the smart-grid research focus.
In the context of a smart-grid–EV-integration research framework, scenarios and use cases can be classified into infrastructure addition, future technologies, policies, charging-demand conditions, changes driven by causes, operating protocols, cybersecurity, information and communication technologies, energy-management systems, energy redistribution, optimizing the smart grid for EV penetration, scheduling, V2G, and vulnerability.
Anchor papers on the integration of EVs into the smart grid
One way to ensure the grasp of the main core of a subject is to examine the references cited in the current articles and highlight repeatedly cited papers. In this literature review, papers cited more than one standard deviation above the average are considered anchor papers.
From the articles that passed the filter criteria, we identified anchor papers, highly cited and influential papers. To identify anchor papers, we used the same search string without an exclusion criterion (no year-range restriction) to pull all journal and magazine publications matching the search criteria from the database. We sorted the extracted articles (700 total) in descending order based on the number of articles citing each focal article, calculated the standard deviation for the articles’ number of citations, and identified the outliers (articles whose number of citations exceeded one standard deviation greater than the mean). The search string that we used for IEEE Xplore is ("Document Title":electric vehicle) AND ("Document Title":smart grid) and refined by Content Type: Conferences, Journals, and Early Access Articles. Meanwhile, for Web of Science, we used the search TOPIC: (electric vehicle AND smart grid) refined by DOCUMENT TYPES = (ARTICLE) Timespan = All years. Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI.
Based on this analysis, we found 61 anchor papers and passed them through a relevancy test to see if they are related to grid-reliability research. As each paper passed the relevancy test, we peruse it to apply quality-assessment criteria. After excluding the irrelevant papers and those not meeting the quality criteria, we identified 45 anchor papers (Table 5).
Table 6 presents a bibliography and analysis of anchor papers on the subject of the integration of EVs into the smart grid. Based on this analysis, Table 7 presents the distribution and percentages of anchor articles by research theme or question previously identified as follows. Optimizing grid usage to EV needs and the impacts on EV applications of smart-grid implementation or V2G communication have received the most attention. Environmental changes due to EV or grid applications show the lowest percentages of anchor articles.
Table 8 presents the research methods used by the authors of the anchor papers. Though simulation is the dominant research method considering the entire literature, articles using analytical approaches seem to get more attention based on articles citing them.
Bearing in mind the research themes and the methods illustrated in the anchor papers, we posit that simulation has been a popular topic in research and that there is need for more research in the area of environmental changes due to EV or grid applications, how to adjust the reliability and adequacy of charging stations and impacts on EV and grid applications.
Analytics using machine learning and big-data management would help the research community plan and improve EV integration into the smart grid. In future research, we intend to highlight the novel use of analytics to predict research themes such as environmental changes due to EV or grid applications, how to adjust the reliability and adequacy of charging stations and impacts on EV applications of grid applications. The goal is to offer an enhanced viewpoint for this research topic, while considering the impact of changes, EVs’ integration, and the potential benefits to the smart grid.
To enable more robust research on smart-grid–EV integration, we both plan our own research and invite other authors to submit original papers on topics including, but not limited to,
EV battery charging optimization
Mobility behavioral science
Location analytics for EV integration
Demand management and pricing for EVs in the electricity network
Internet of things and sensors for EV integration
Recent advancement in analytics for EV integration and microgrids
Innovative charging strategies for EVs
As described in the previous section, this systematic literature review provides a solid foundation to equip researchers with pertinent information.
The intent of this paper is to present a systematic literature review of smart-grid–EV integration and offer a research framework to guide future research and enrich the body of knowledge. Because the systematic literature review presented in this paper focuses on two digital libraries and searches only article titles, it does not contain all the material available on this subject. It does, however, include most of the key publications readily available in a power-utility or technical-reference library together with some of the earlier papers in the field (the anchor papers).
To conduct a systematic literature review, we completed a broad automated search, a method that includes the selection of appropriate digital sources (digital libraries and indexing systems) and the determination of the search terms. We selected the digital libraries IEEE Xplore and Web of Science for the systematic review. This article also details the components of our research theme and its accompanying use cases.
This review is limited to the IEEE Xplore and Web of Science digital libraries to facilitate an automated search of the literature. This limitation is reasonable because these sources are most likely to be available in a power-utility or technical-reference library. However, a broader search may find other research not analyzed here. Additionally, the search was restricted to article titles to reveal those research projects most closely related to the topics of interest. A broader search of abstracts and full text would certainly find more articles, but would likely involve considerably more false positives.
Analytics using machine learning and big-data management could help the research community plan and improve smart-gid–EV integration. Based on the four types of key research concerns (power-grid, power-system, and smart-grid reliability and the impacts of changes on them), we intend to highlight the novel use of analytics to predict research themes in future research. The goal is to offer an enhanced viewpoint for smart-grid–EV integration while considering the impact and the potential benefits to the smart grid of such changes and EVs’ integration.
Availability of data and materials
Additional supporting data is available from the first author.
Vehicle to grid
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This research is based on students’ course project work at the College of Business & Economics of California State University, Los Angeles, where VS and AA are faculty in the Department of Information Systems, HC and JK are students in the undergraduate program. No additional funding was used for this project.
Authors and Affiliations
California State University, College of Business and Economics, Los Angeles, CA, 90032, USA
VS and AA initiated the research project, conducted the broad automated search, selected the digital sources, determined the search terms, administered and led the analysis, reviewed the findings, and proofread/edited the final paper. HC and JK completed the literature review. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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