A New Framework of Vehicle-to-Grid Economic Evaluation: From Semi-Systematic Review of 132 Prior Studies

Abstract

Vehicle-to-Grid (V2G) technology enables electric vehicles (EVs (Unless otherwise specified, Electric Vehicles (EVs) in this study refer to the totality of BEVs, PHEVs, and other battery-equipped vehicles that have the potential to participate in V2G)) to interact with renewable energy sources, positioning it as a key driver of energy system decentralization. While V2G holds significant potential for enhancing grid stability and economic efficiency, its large-scale deployment requires a robust economic assessment. However, existing research predominantly focuses on technical feasibility, lacking comprehensive economic evaluations due to the complexity of V2G system architectures. To bridge this gap, we propose the BSTP (Business-Stakeholders-Technology-Policy) V2G economic evaluation framework and the VRR (Value Realization Rate) methodology, employing a Semi-Systematic Co-Design Approach. This framework systematically characterizes the evolution of V2G business models, the interactions among key stakeholders, the influence of technological and policy factors, and the criteria for economic feasibility assessment. Furthermore, we identify a “Big Models, No Trials” issue in V2G economic research, where large-scale theoretical models lack empirical validation. To address this challenge and ensure the practical applicability of our framework, we define six core challenges that must be resolved for a rigorous economic evaluation of V2G. Our findings provide a structured foundation for future research and policy development, offering insights that could accelerate the transition to decentralized energy systems.

1. Introduction

1.1. Motivation and Objectives

Technological advancements in the energy and transportation sectors are key to achieving sustainable social development [1]. Renewable energy and electric vehicles (EVs) are at the forefront of this progress. However, concerns are emerging about the instability of renewable energy sources [2] and the debated environmental benefits of EVs [3]. Vehicle-to-Grid (V2G) technology [4] offers a solution by enabling EVs to interact with the power grid through bidirectional charging. This technology allows EVs not only to draw electricity from the grid but also to return stored energy to the grid during periods of high demand or shortages [5]. As a result, EVs become mobile energy storage units, contributing to grid stability and load balancing. Crucially, V2G can be integrated with renewable energy sources, enhancing their stability and maximizing the environmental value of EVs [6].

The promotion and application of V2G will bring significant changes to the current energy system, with decentralization being its most notable feature [7]. Unlike traditional energy systems, where energy production and distribution are centralized, V2G systems place individual users at the center, with energy production and use happening on a personal level [8]. This characteristic means that V2G represents more than just a foundational shift; it redefines the roles of various stakeholders in the energy system, making individual users the most important actors [9]. A comprehensive evaluation of the economic value of V2G for different stakeholders will be a key challenge for its implementation and promotion [10,11]. Here, employing a Semi-Systematic Co-Design Approach, we propose a BSTP framework (Business models, Stakeholders, Technological Routes, as well as Policies and Regulations) to assess the economic value of V2G systems. This framework examines the evolution of business models, the interactions among stakeholders, and the impact mechanisms of technological roadmaps and policy regulations. By providing a structured approach, the BSTP framework enables a comprehensive assessment of the economic feasibility of V2G applications, thereby enhancing their contribution to sustainable development.

1.2. Why Economic Value Analysis of V2G Is Crucial

The landscape of V2G research demonstrates notable strengths in its conceptual coherence and robust technical focus (Appendix A offers a more in-depth analysis of overall V2G research trends) yet reveals significant gaps in addressing social dimensions. The foundational aspects of V2G, its system configurations, interface topologies, and market mechanisms have been well-established [12]. Furthermore, the environmental, technological, and economic benefits of V2G, alongside the recognized challenges such as battery degradation, high investment costs, user behavior uncertainties (e.g., anxiety and unpredictability), and societal impacts, have reached a consensus among researchers [8]. V2G research spans a wide range of disciplines, penetrating fields from engineering to sociology. While significant advancements have been made in technical areas, such as power electronics [13], energy management strategies [14], bidirectional charger topologies [15,16], and EV battery degradation [17], studies have consistently highlighted a research gap in the social aspects of V2G [18], including economic and business models, user behavior, and regulatory frameworks [19].

The economic value analysis of V2G is not just a research gap; key challenges associated with V2G implementation, as discussed above, are fundamentally tied to economic issues [8,18]. Battery degradation and investment costs are direct economic barriers, while user behavior uncertainties can largely be addressed through appropriate economic incentives [20,21]. Similarly, privacy and security concerns translate to increased maintenance costs. These observations underscore that the core challenges to V2G adoption and promotion are predominantly economic in nature.

While numerous studies have analyzed the economic value of V2G systems using simulations or hypothetical scenarios, they often rely on overly simplistic assumptions and lack a comprehensive framework to guide their analyses. Some comprehensive studies explore the roles of stakeholders and business models within V2G systems [9], yet they often fail to address the interconnections between stakeholders, business models, and their dynamic interactions. This disconnect highlights the need for an integrated analytical framework.

Building on these insights, this paper aims to propose a holistic framework for evaluating the economic value of V2G systems. Specifically, the framework addresses three key research questions:

• How can specific V2G business models be effectively developed and implemented to maximize economic benefits?
• What are the relationships among V2G stakeholders, and how do these relationships influence the economic outcomes?
• What are the key factors, and how do they influence the economic value for different stakeholders?

By addressing these questions, the proposed framework provides a structured and comprehensive approach to guiding future research and practical applications in the economic evaluation of V2G systems. More importantly, it enables decision-makers and investors to derive more actionable and insightful conclusions from the evaluation, rather than relying solely on the theoretical superiority of systems depicted through complex algorithms or technical details.

1.3. Structure

Following the Introduction in Section 1, Section 2 outlines our research methodology and provides an overview of the fundamental aspects of the core literature reviewed. Section 3 presents the primary outcome of this study—the BSTP framework—along with a detailed breakdown of its key components. Section 4 explores the application of the BSTP framework and highlights the gaps in existing studies on V2G economic linkages. Finally, Section 5 concludes the paper with a summary of key findings.

2. Methodology

This study adopts a Semi-Systematic approach to review academic literature to develop a framework for evaluating the economic value of V2G systems. Given that research on V2G economic analysis often integrates multidisciplinary technical assessments or is dispersed across various sociological studies, a fully systematic quantitative meta-analysis is not feasible. Therefore, we applied the Semi-Systematic approach, which offers the flexibility to identify key insights across disciplines while maintaining a balance between systematicity and breadth. This approach is particularly well-suited for V2G research, as the field remains emerging and highly complex.

Our methodology is informed by a range of studies [22,23,24,25] and incorporates a co-design methodology to iteratively construct, review, and refine the proposed framework. Our co-author team brings together diverse expertise across multiple disciplines, ensuring a comprehensive approach to our research. With expertise in energy economics, the team brings critical insights into market dynamics and policy implications. Combining backgrounds from both academia and industry, the team integrates strong technical knowledge in electrical engineering and communication security with practical experience in vehicle engineering and complex energy systems. This collaboration bridges theoretical research with real-world applications, enabling a well-rounded evaluation of the economic potential of Vehicle-to-Grid technology. A detailed description of the methodological process is provided in Figure 1 and the paper selection process was conducted using a machine-based filtering method, as detailed in Algorithm 1.

Algorithm 1. Machine Filtering Algorithm for V2G Research Papers Based on Criteria: Economy Association Group C2

Input:

A folder containing Excel files exported from Web of Science, with a total of N1 research papers, each containing multiple data fields including abstracts.

Output:

A new sheet in each Excel file listing titles and abstract of papers whose abstracts contain economic-related keywords, with a total of N2 filtered papers.

Step 1: Initialize Economic Keywords

Define the set C2 as follows:

{“economy”, “value”, “cost”, “benefit”, “price”, “profit”, “revenue”, “income”, “expense”, “investment”, “return”, “tariff”, “subsidy”, “funding”, “capital”, “expenditure”, “payback”, “depreciation”, “transaction”, “market”, “trade”, “finance”, “liquidity”, “budget”, “incentive”, “credit”, “tax”, “rate”, “interest”, “margin”, “dividend”, “yield”}

Step 2: Iterate Through Files in the Folder

For each Excel file in the specified folder: Open the Excel file. Iterate through all sheets in the file.

Step 3: Identify Relevant Columns

For each sheet: Identify the "Abstract" and "Title" columns based on their headers. If both columns exist, proceed to filtering.

Step 4: Filter Papers Based on Keywords

For each row in the sheet: Convert the abstract text to lowercase. Check if any economic keyword is present in the abstract. If a match is found, store the corresponding paper title.

Step 5: Save Filtered Titles

If any titles are selected: Create a new sheet "Filtered Titles" in the same Excel file. Write the filtered titles and their corresponding abstracts into this new sheet.

Step 6: Complete Processing Repeat for all Excel files in the folder. Print summary: number of processed files and filtered titles.

Fundamental Aspects of the Core Literature Reviewed

We have compiled the fundamental details of the N* core reviewed papers [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157], along with the classification and coding process employed during the framework construction, into Supplementary Data S1. Fundamental details of the 132 core reviewed papers on V2G economic evaluation can be found in Figure 2. As shown in Figure 2a, the majority of the 132 reviewed papers were published from 2018 onward, with over 15 papers published annually since 2019. The citation distribution of these papers is illustrated in Figure 2b, where 75% of the papers have fewer than 50 citations, with a median of 21. While a small number of papers exceed 100 citations, most remain within a lower citation range.

Figure 2c,d present the distribution of journals in which these papers were published. Energies, Energy, and Applied Energy are the most frequent publication venues, each featuring more than eight relevant papers, whereas over 40 journals contain only a single related publication.

Figure 2e highlights the most frequently used author-defined keywords, with “battery degradation”, “frequency regulation”, and “smart charging” appearing most often (each occurring more than eight times). Finally, Figure 2f illustrates the distribution of Web of Science (WOS) research categories, showing that most studies fall under “Energy & Fuels”, and “Engineering, Electrical & Electronic”. This suggests that existing economic value analyses of V2G remain predominantly within technical and engineering domains.

3. A BSTP Framework

To analyze the economic value of V2G systems, we propose the BSTP framework (Business model, Stakeholders, Technological Routes, as well as Policies and Regulations). As illustrated in Figure 3, the framework is a three-layer structure. At its core, the innermost layer represents the Business model, the central element of the economic framework. Surrounding it is the second layer, Stakeholders, which includes direct stakeholders (up-down) and indirect stakeholders (left-right) as participants in the business model. The outermost layer consists of Influencing Factors, divided into Policies and Regulations (red, left) and Technological Routes (blue, right), which directly shape stakeholder behavior and influence the business model’s formation. This framework effectively summarizes the key elements of V2G economic evaluation and clarifies their logical relationships. Next, we will systematically explain the meaning of these elements layer by layer.

3.1. Business Model

To systematically characterize the business models that may emerge from V2G, we propose a four-phase evolutionary framework. To the best of our knowledge, this represents the first comprehensive generalization of V2G business models based on our focused research and analysis. The evolution across these phases is primarily driven by changes in societal EV ownership rates; however, due to space limitations, further descriptions and definitions of separated phases are provided in Appendix B. Next, we will describe each phase in detail.

3.1.1. Phase 1-Initial Deployment

In Phase 1, the societal EV ownership rate is less than 6%. During this phase, establishing the business model primarily relies on the promotion and awareness of the V2G concept, alongside the development of necessary infrastructure. The business model at this stage is largely driven by government initiatives.

As illustrated in Figure 4, the government, positioned at the top of the model, provides subsidies to the Core V2G Participants (CVPs). These CVPs include EV users (both individual and collective owners), parking lot owners (both individual and collective), and concurrent stakeholders. One group supplies energy, while the other provides charging and discharging equipment.

The Energy Purchase Model (EPM) serves as the foundational business model in this phase. CVPs not only earn revenue from electricity sales (with Grid Operators) but also receive government subsidies, similar to the Feed-in Tariff (FIT) system. These subsidies provide strong incentives for users to participate in V2G. However, as the number of participants grows, the subsidies will gradually decrease. In addition to EPM, CVPs will also realize local V2G operation through V2B (Vehicle-to-Building) or V2H (Vehicle-to-Home) at the same time.

In addition to directly supporting CVPs, the government also provides subsidies and support to Technology and Infrastructure Providers at the bottom of the model. This support encourages the emergence of companies specializing in infrastructure development and service provision in related fields. These providers deliver essential equipment and technical support to the CVPs.

The key to maximizing business value in this stage lies in effectively setting subsidy levels and dynamic adjustment rules. This approach aims to promote and popularize the V2G concept while laying the groundwork for future development.

3.1.2. Phase 2-Integration of Operations

When social EV penetration reaches 6% or more, the business model will progressively transition into Phase 2, building on the initial development of V2G to enhance system operations and unlock value beyond simple arbitrage. As shown in Figure 5, this phase remains primarily driven by the government sector. A notable development in this stage is the inclusion of a new stakeholder in the CVPs—Energy Suppliers. These suppliers include regional conventional power plants or discrete renewable energy providers, which may overlap with the original CVP or be introduced later.

In addition to the original V2B or V2H approaches, spinning reserve functions can now be implemented within the CVPs group in a form like a microgrid or a distribution network. These functions help stabilize the intermittent operation of renewable energy sources by providing immediate power supply adjustments, ensuring grid reliability. Additionally, spinning reserves can act as a backup power supply during emergencies, such as earthquakes, and support innovative applications like V2G-enabled emergency power systems in facilities such as multi-level parking structures. This maximizes the use of the V2G’s fast response characteristics.

Another critical stakeholder introduced in Phase 2 is the V2G Service Provider (VSP). VSPs will lead the coordination between CVPs and grid operators, taking over the role of managing the scale of CVP groups. VSPs regulate the behavior of CVPs during specific timeframes to improve operational efficiency and economic value. These providers may take the form of specialized V2G operating companies, aggregators under the virtual power plant (VPP) concept, or entities that own a substantial number of charging and discharging units. Under their leadership, individual and collective users can better integrate with energy suppliers.

The primary business models in Phase 2 are the Individual Subscription Model (ISM) and the Fleet Operation Model (FOM):

• The ISM could evolve by increasing the options available to participants in a liberalized wholesale electricity market, offering diverse subscription packages for engaging with V2G services.
• The FOM involves contracting fleets of vehicles (e.g., taxis, buses, rental cars, or used cars) to provide V2G services when idle.

A significant characteristic of Phase 2 is the structured and organized expansion of V2G systems, necessitating effective management and coordination by VSPs. Additionally, the automotive industry emerges as a new stakeholder in this phase. Automakers can enrich the ISM by offering V2G-ready options when selling EVs, such as higher-capacity batteries to support V2G services, thereby enabling users to generate more revenue. In summary, the success of the business model in Phase 2 hinges on the development and effectiveness of VSPs. As the central facilitator of internal and external CVP connections, VSPs stimulate market activity, enhance system efficiency, and drive the expansion of the CVP community through positive feedback loops.

3.1.3. Phase 3-Grid Regulation Maturity

When social EV ownership exceeds 18%, the business model is expected to enter the third stage (Figure 6), marked by the maturation and completeness of the V2G business model. At this stage, government involvement in operations will cease entirely, transitioning to a fully market-driven system. Energy market operators will replace the government as the primary driver of this phase. The government’s role will shift to regulatory oversight, ensuring the market operates fairly and efficiently.

While the roles of Core V2G Participants (CVPs) remain unchanged, their scale will expand significantly. Correspondingly, V2G Service Providers (VSPs) will diversify and form a matrix structure, leading to the emergence of a partially decentralized energy system (PDES) within some CVPs.

Phase 3 introduces a single new business model, the Grid Support Model (GSM). In this model, the arbitrage function will no longer play a role; instead, V2G will focus on supporting grid operations through services such as frequency regulation, voltage adjustment, supply and demand balancing, and participation in intraday or day-ahead power trading markets.

With the transition to a fully market-oriented system, banks and other financial institutions will play an important role in financing operations. Simultaneously, the expansion of the V2G ecosystem will necessitate the involvement of Regulatory, Standardization, and Certification Bodies. These entities will be responsible for regulating operations, establishing laws and regulations, and safeguarding user privacy. In summary, the core focus for maximizing business value in this stage lies in the development and enforcement of rules and regulations. These frameworks are essential for ensuring a fair market environment, transparent operating rules, and robust privacy protections.

3.1.4. Phase 4-Full Decentralization

When the EV retention rate exceeds 50%, the business model will transition into the final stage, representing a fully decentralized energy system, as illustrated in Figure 7. In this stage, the Core V2G Participants (CVPs) will integrate both power market operators and grid operators, operating under a completely decentralized framework. This system will leverage blockchain and related technologies to ensure transparency, security, and efficiency in energy transactions. The existing energy system architecture will be fundamentally transformed, with decentralized rules replacing centralized control. In this scenario, the government’s role will be fully limited to regulatory oversight and the establishment of guiding policies, as operational responsibilities will be entirely relinquished to market mechanisms.

Unlike traditional analyses that focus on incremental improvements, this stage envisions the realization of the Environmental Impact Model (EIM) and the Industrial Integration Model (IIM) as critical components of the V2G ecosystem. These models represent the culmination of V2G’s potential, extending its influence beyond energy management to broader societal and industrial contributions.

Environmental Impact Model (EIM), the EIM, and V2G systems are actively integrated with environmental organizations to enhance environmental value. Through this model, EVs equipped with V2G capabilities can support carbon offset initiatives, renewable energy promotion, and emissions reductions. For example:

• Carbon Offsetting: EVs could provide surplus electricity to grids during peak generation periods, utilizing hydrogen energy or other emerging low-emission distributed generation methods, further reducing reliance on fossil fuels.
• Monitoring and Reporting: Advanced technologies embedded within the V2G infrastructure could monitor real-time carbon savings and provide transparent environmental impact reports. In a decentralized energy system, EVs function as small-scale terminals, responsible for data collection and are seamlessly integrated into the network for continuous monitoring and analysis.

Industrial Integration Model (IIM), IIM extends the V2G ecosystem beyond the energy sector, fostering integration with other industries to create a mutually beneficial network. Key aspects of this model include:

• Partnerships with Smart City Initiatives: V2G systems could integrate with smart grids, urban planning, and intelligent transportation systems, supporting smart city goals like reducing energy wastage, improving traffic flow, and enhancing energy reliability.
• Synergies with Telecommunications: The IIM could incorporate telecommunications infrastructure for real-time data exchange, enabling seamless coordination between energy providers, grid operators, and end-users.
• Emergency Preparedness and Resilience: V2G capabilities could support disaster recovery efforts by providing backup power during natural disasters, ensuring energy availability in critical scenarios.

3.2. Stakeholders

We have identified and summarized 13 key stakeholders in V2G business models. These stakeholders have been categorized based on their degree of participation in V2G-related activities.

Table 1. Stakeholders in Business model with Corresponding Index, Icons, and Definitions.

Index	Symbols	Description
$A_1$		EV User, this group encompasses individual users of electric vehicles (EVs), corporate collective users, and owners of EVs or collectives they form.
$A_2$		Parking Lot Owners, this group includes individuals or entities owning parking lots where charging and discharging equipment can be installed and connected to the grid. These may encompass dedicated commercial parking lot owners, public parking lot operators, private parking lot owners, and owners of dedicated charging and discharging stations.
$A_3$		Energy Suppliers, this category includes local conventional and renewable energy providers as well as decentralized renewable energy providers. Beyond entities supplying electricity, it may also encompass hydrogen power stations, gas fuel stations, and other energy resource providers.
$B_1$		V2G Service Providers (VSPs) are integrated service providers that consolidate EV resources and charging/discharging facility capabilities to facilitate the sale of V2G power. This category may include, but is not limited to, specialized V2G integrators, aggregators, virtual power plant operators, or entities functioning as a division or extension of established grid operators. Additionally, they may arise from or collaborate with other stakeholders such as A1, A2, or A3.
$C_1$		Grid Operators (TSOs/DSOs), Grid operators include Transmission System Operators (TSOs) and Distribution System Operators (DSOs), who are responsible for managing the transmission and distribution of electricity. TSOs oversee high-voltage networks, ensuring large-scale energy transfer between regions, while DSOs manage lower-voltage networks that deliver electricity to end-users.
$C_2$		Technology and Infrastructure Providers, this group includes providers of charging and discharging equipment and other V2G-related power electronics. It also encompasses entities offering communication solutions, protocols, and security services, as well as those specializing in system scheduling, optimization, and algorithm development. These stakeholders form the backbone of V2G technology, ensuring the seamless operation, efficiency, and security of integrated systems.
$C_3$		Automotive Industry sector includes manufacturers and developers of electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and their associated components, such as batteries and powertrain systems.
$C_4$		Energy Market Operators, this group encompasses entities managing energy trading platforms and markets, which may vary by country and region. These include, but are not limited to, long-term and short-term electricity trading markets, capacity markets, supply and demand adjustment markets, renewable energy trading markets, and carbon trading markets. Energy market operators also have the potential to facilitate peer-to-peer (P2P) energy trading, which could offer a decentralized approach to energy exchange. For V2G systems, P2P trading represents a complementary mechanism, enabling EV users to directly trade surplus energy, enhancing flexibility, and further integrating V2G into localized energy ecosystems.
$C_5$		Financial Sector, this sector includes banks, insurance companies, investment firms, and other financial institutions that support the development and deployment of V2G systems.
$D_1$		Regulatory, Standardization, and Certification Bodies, these entities are responsible for establishing regulations, developing standards, and certifying compliance to ensure the safe, reliable, and efficient operation of V2G systems. They set technical specifications for charging and discharging protocols, grid compatibility, and cybersecurity requirements, fostering interoperability among stakeholders. Examples of such entities include international organizations like IEC and ISO, regional bodies like the CEN, and national regulators such as the FERC in the U.S. and ANRE in Japan.
$D_2$		Government, Governments play a pivotal role in the development and adoption of V2G systems by acting as policy makers, regulators, and financial supporters.
$D_3$		Environmental Organizations, Environmental organizations entities work to raise public awareness, influence policy decisions, and foster collaborations among stakeholders to align V2G technology with environmental goals. For example, organizations like Greenpeace and World Resources Institute (WRI).
$D_4$		Related Industries, this category includes industries that indirectly support the development and implementation of V2G systems by providing complementary technologies and services. For example, energy storage battery manufacturers, smart grid technology developers, renewable energy equipment manufacturers, and logistics and fleet management companies.
Index Not Applicable (Indicates Stakeholder Relationships Only)		Black lines represent energy flow.
		Pink lines represent monetary flow.
		Blue lines represent service provision.
		Black dashed-line box indicates the Core V2G Participants (CVPs) within.

Note: All stakeholders in business model are summarized from 132 prior studies.

outlines the definitions of the 13 key stakeholders involved in the business model, alongside their icons and the connections depicted in the business model phase diagram in Section 3.1.

It is important to note the following:

• Index A represents the Core V2G Participants (CVPs), referring to entities that provide direct energy for the V2G system and are formed at different phases of the business model.
• Index B denotes the V2G Service Providers (VSPs), who act as integrators of V2G services.
• Index C refers to stakeholders directly involved in V2G service transactions.
• Index D represents affiliates of V2G service transactions.

The progression from Index A to Index D reflects a gradual weakening of V2G affiliation, illustrating the varying levels of involvement and influence within the system.

In a pioneering effort, we have also analyzed the relationships among these stakeholders within the context of the V2G business model. These relationships significantly influence the outcomes of economic value analyses for V2G systems. We classify the complex stakeholder relationships into three main categories: Fusion Interactions, Transactional Dynamics, and Value Chain Gaming. It is important to note that peer competition (e.g., competition between different VSP companies, individual EV users, or groups with competing interests) or other simple relationships are excluded from this analysis. This is because such competition pertains only to ordinary business dynamics, whereas our primary focus is on the formation of business models and the evaluation of the economic value of V2G systems.

3.2.1. Fusion Interactions

Fusion Interactions refers not to a simple merger, cooperation, or upstream/downstream process, but to the overlapping roles of different stakeholders or the gradual formation of integration interactions. This relationship is most likely to occur within Core V2G Participants (CVPs), as shown in Figure 8, particularly when the business model evolves to Phase 2 and Phase 3. During these phases, fusion may occur within CVPs, with A12 and A13 being the most common groups. Examples include communities with public parking lots or those that have adopted solar power extensively. A23, on the other hand, represents a future scenario where EV charging stations rely on renewable energy inputs. If such charging stations were integrated within a community, they would evolve into A123, forming a more comprehensive CVP.

Unlike simple interest alignment or cooperation, Fusion Interactions allow for better integration of resources, maximizing the value of V2G systems. This is considered one of the essential processes for decentralizing the energy system. Fusion Interactions also support the formation of localized, self-sufficient energy ecosystems, enabling the community to take a more active role in energy production, storage, and consumption.

The enablers of integration are also crucial in driving Fusion Interactions. During Phase 1, government advocacy is indispensable in creating the necessary regulatory and infrastructural environment. Governments can incentivize community-level CVPs by providing subsidies, policy support, and educational initiatives to promote the adoption of renewable energy and V2G-compatible technologies. As the business model transitions to Phase 2 and Phase 3, VSPs take on more responsibility as enablers, driven by commercial interests. They act as integrators, facilitating the alignment of technology, services, and market incentives to create profitable and scalable V2G solutions. When the business model evolves to Phase 4, $C_1$ and $C_4$ stakeholders (e.g., Grid Operators and Energy Market Operators) will also integrate into the CVP group. This process will largely depend on the evolution of the technological landscape, including advancements in blockchain technology, smart grid management, and renewable energy storage solutions.

Despite their benefits, Fusion Interactions also pose significant challenges, as they occur among a large number of heterogeneous entities, which is also one of the most distinctive features of the V2G system. Firstly, Fusion Interactions often involve stakeholders with differing priorities, making it difficult to align goals and manage conflicts, especially within CVPs where resources and responsibilities overlap. Secondly, the formation of CVPs may marginalize smaller, independent stakeholders or entities unable to integrate, potentially reducing inclusivity in the V2G ecosystem. To address these challenges, it is essential to establish clear regulatory frameworks and monitoring mechanisms to ensure fairness and transparency in CVP operations. Encouraging collaboration among stakeholders and providing incentives for smaller entities to participate in V2G systems can also enhance inclusivity and mitigate the risks associated with Fusion Interactions.

3.2.2. Transactional Dynamics

Transactional Dynamics, on the other hand, represents a common but complex relationship within business models. These relationships can be classified into two main types, as illustrated in Figure 9.

The first type, depicted on the left side of Figure 9. involves symmetric Transactional Dynamics, where value exchange is reciprocal, and both parties receive proportional benefits. For example:

• A bank invests in a VSP enterprise and earns a corresponding financial return.
• The VSP provides services to user communities and receives a commission in return.
• The VSP trades electricity with grid operators and receives payment.
• The VSP engages with equipment service providers and acquires technology and equipment in exchange for monetary compensation.

These relationships are categorized into three common types of symmetric exchanges: Money-Money, Money-Energy, and Money-Services.

The second type, shown on the right side of Figure 9, involves asymmetric Transactional Dynamics, which are often centered on governments. In these scenarios, one party provides money, technology, or services without directly receiving anything in return. For example:

• Governments may provide subsidies to users or technology companies during Phases 1 and 2 of the business models.
• Governments may regulate the electricity market or provide system support through rule-based entities.
• Such contributions aim to achieve broader objectives, such as the long-term stability of the energy system, rather than immediate or direct returns.

Unlike Fusion Interactions, which reflect highly individualized and complex stakeholder roles, Transactional Dynamics are characterized by non-linearities and uncertainties, which are also key features of the V2G system. These challenges are particularly evident in:

• Energy Supply Prediction: The integration of renewable energy sources introduces variability and unpredictability in energy supply.
• Electricity Market Uncertainty: Fluctuations in electricity prices and market conditions make it difficult to forecast revenue streams.
• User Behavior Variability: Changes in user preferences and usage patterns add complexity to modeling economic outcomes.
• Technological Route Divergence: Competing technological pathways can create bifurcations, making it challenging to determine the most economically viable route.
• Policy Uncertainty and Quantification: Policy changes and difficulties in quantifying their impacts can further complicate revenue and value predictions.

While many of these issues are inherent to the energy system as a whole, the overlay of V2G-specific transactional relationships amplifies these complexities. This underscores the need for robust modeling and adaptation.

3.2.3. Value Chain Gaming

Value Chain Gaming is a relationship that arises under the V2G business model, as illustrated in Figure 10. In this context, four main stakeholders are shown in the V2G interest chain. To maximize their benefits, VSPs aim to attract more EV users with stable power supply. However, EV users may prioritize their personal transportation needs, particularly during peak travel times, leading to unpredictability in their willingness to sell electricity. This forces VSPs to negotiate incentives or develop strategies to stabilize energy contributions while respecting user flexibility leading to a dynamic interplay or “gaming” scenario between these stakeholders.

A more apparent example is the strategy employed by VSPs in participating in power trading across different markets to maximize their profits. However, these trading strategies are not always conducive to grid stability. For instance, peak-valley arbitrage, while profitable for VSPs, can conflict with grid requirements such as frequency regulation or capacity reserve maintenance, creating a game between VSP interests and grid stability.

Furthermore, grid stability and the realization of environmental value also involve gaming dynamics. For example, during summer sunny peaks, when solar energy generation is at its maximum, VSPs and the grid may aim to trade and distribute more electricity to balance supply and demand. However, environmental objectives may prioritize storing surplus electricity for later use. These conflicting goals create a three-way game among VSPs, grid operators, and environmental priorities.

Additionally, grid operators and government policies introduce further complexity. While grid operators focus on short-term operational stability and cost efficiency, governments may enforce renewable energy quotas or prioritize decarbonization, creating tension between immediate operational goals and long-term sustainability mandates.

The 4-Phase business model was specifically designed to mitigate the impacts of Value Chain Gaming. This progressive model introduces increasing complexity in Value Chain Gaming relationships:

• P1 focuses solely on CVP arbitrage, where participants optimize their own benefits.
• P2 incorporates the profitability of VSPs, reflecting their role in integrating services.
• P3 adds grid stability considerations, balancing operational efficiency with system reliability.
• P4 ultimately includes environmental value as a key driver, aligning all stakeholders toward sustainability goals.

Unlike other studies, this stakeholder analysis adopts a clearer and more structured approach, avoiding the pitfalls of combining all complex games into a single framework or focusing on isolated transactions. Instead, we advocate for placing Value Chain Gaming at the forefront of V2G economic research. By addressing it through truncated assumptions and focusing on specific scenarios, researchers can achieve more accurate and actionable insights while accounting for the dynamic and interdependent nature of these relationships.

3.3. Influencing Factors

We categorize Influencing Factors into two main groups, as illustrated in Figure 3: policies and regulations (PR) on the left side of the outer circle and technical routes (TR) on the right.

Table 2. Influencing Factors of V2G Systems Economic Value Evaluation.

Index	Title	Description	Corresponding Stakeholders	Impact
Technical Routes
TR1	Battery	The battery acts as the primary energy storage unit in EVs and serves as the main power source for V2G systems. Its economic impact manifests in two fundamental factors. First, battery cost and storage capacity influence both the upfront investment in V2G infrastructure and its feasible operating duration. Second, the cyclic charging and discharging in V2G accelerates battery degradation, thereby increasing long-term maintenance and replacement costs. Details on various battery types are summarized in Table A2.	$A_1$	Positive
			$C_3$	Dual
TR2	Charging and Discharging Power Electronics	Charging and discharging power electronics are primarily responsible for electrical energy conversion between AC and DC, including stepping down high-voltage AC from the grid to low-voltage DC in G2V mode and stepping up DC to AC in V2G mode. This incurs additional costs for EV users and parking facility operators engaging in V2G operations. The development and deployment of this technology depends on regional policies and project-specific requirements, while technology and infrastructure providers oversee its implementation. Figure A5 and Figure A6 depict the classification and topology of charging and discharging systems.	$A_1$,$A_2$	Negative
			$C_2$	Dual
TR3	Communication and Protocols	Communication technologies and protocols encompass data transmission and storage mechanisms that facilitate secure interactions among the VSP, users, charging and discharging equipment, and key grid stakeholders. Figure A7 illustrates this architecture and its associated stakeholders. Communication protocols constitute the backbone of the system, establishing a structured framework for secure interactions. Within this framework, software components such as TLS/SSL, PKI, and blockchain facilitate authentication and encryption for secure data exchange. Meanwhile, wireless and wired transmission devices form the physical backbone of the communication infrastructure. Users benefit from secure services through the communication framework, parking lot owners are responsible for investing in physical infrastructure, and VSPs oversee the deployment, data management, and maintenance of communication systems.	$A_1$	Positive
			$A_1$,$B_1$	Negative
TR4	System Scheduling and Optimization	Energy scheduling and optimization, also known as macro-energy management and micro-control strategies, aim to optimize the economic returns of individual EVs in V2G operations. Beyond individual optimization, it extends to coordinating the collective behavior of EV fleets by accounting for individual usage patterns, renewable energy integration, grid reliability and stability, power market mechanisms, and environmental value. Long-term optimization strategies, leveraging model-based approaches and machine learning techniques, enable predictive analytics and real-time optimization to manage uncertainties in demand, grid fluctuations, and market dynamics. As the central entity in scheduling, the VSP incurs the main economic costs associated with implementing this technology.	$B_1$	Dual
Policies and Regulations
PR1	Incentive Policies	Incentives are compensatory mechanisms transferred from one stakeholder to another in the form of monetary or service-based compensation. They are essential for facilitating V2G adoption and user engagement, particularly in the P1 and P2 phases of the business model, where they function as the main driving force. Government entities are the primary providers of incentives, while private companies may additionally offer service-based incentives to users. Table A4 categorizes incentive policies, detailing their mechanisms and applicability across different business model phases.	$A_1$,$A_2,B_1,C_2,C_3$	Positive
			$C_1,D_2$	Dual
PR2	Environmental and Sustainability Policies	Environmental and sustainability policies require V2G systems to contribute to environmental and carbon value. This study suggests that this value is expected to materialize in the P4 phase, as the business model reaches full maturity. These policies are formulated and regulated by environmental agencies, while energy market operators oversee carbon trading platforms, generating revenue through associated fees. Additionally, the battery recycling industry plays a role in economic value creation through battery repurposing, while VSPs act as passive participants, adhering to mandated standards.	$C_4,D_3,D_4$	Positive
			$B_1$	Negative
PR3	Technical and Standardization Regulations	Technical and standardization regulations require all technical components of the system, including batteries, charging and discharging equipment, grid access, communications, and dispatch, to be designed and operated in accordance with regional regulations. Existing and potential regulations applicable to V2G systems are outlined in Table A5. These regulations are formulated and enforced by regulatory, standardization, and certification authorities, which collect fees or compensation for overseeing compliance. The majority of system stakeholders must comply with these regulations.	$D_1$	Positive
			$A_1$-$A_3$,$B_1$,$C_1$-$C_3$	Negative
PR4	Legal Regulations and National Energy Strategy	Legal regulations and national energy strategies encompass national legislation that ensures the full implementation of Environmental and Sustainability Policies as well as Technical and Standardization Regulations. Additionally, laws such as the Electricity System Reform Act, the Electricity Market Act, and Data Privacy and Cybersecurity Regulations impose restrictions on the actions of certain stakeholders. The overall direction of the national energy strategy determines the macro-level positioning and application prospects of V2G, indirectly influencing its economic valuation. This corresponds to the impact of large-scale scenarios in the simulation analysis.	$D_1,D_3$	Positive
			$B_1,C_4,C_5$	Negative

Note: Corresponding stakeholders are listed in Table 1 as index counterparts. The ‘Impact’ column reflects the influence on the economic value (including service value) for the respective stakeholders under specific technological or policy developments. ‘Positive’ indicates clear benefits, ‘Negative’ signifies clear losses, and ‘Dual’ suggests the need for further exploration, such as a comprehensive analysis of the costs and benefits of developing a particular technology. All influencing factors are summarized from 132 prior studies.

provides a detailed breakdown of each influencing factor, along with the stakeholders most affected by them. Additionally, Appendix C outlines the additional information on influencing factors. In our analysis, we classify the impact of influencing factors on stakeholders into three categories: positive, negative, and dual. A positive classification indicates that the presence of certain policies or technologies enhances the economic value of the corresponding stakeholders. Conversely, a negative classification implies that these factors hinder economic value acquisition. The dual category represents more complex cases where the impact of influencing factors is dynamic and requires further simulation and validation, as it may change over time and across different scenarios.

For Technical Routes, it should be noted that, except for TR1, we did not find independent economic evaluation studies explicitly focusing on other technical routes. For TR2, we provide a relative cost evaluation in Figure A5 and Figure A6, where values in parentheses represent estimated cost ranges (higher and lower bounds). The diversity of power electronic devices necessitates trade-offs in equipment selection, depending on project-specific requirements and regional factors such as infrastructure and regulatory conditions. Economic evaluations of power electronic device selection remain underexplored. However, overall cost evaluations of power electronics often consider factors such as the number of components, system complexity, and operational efficiency. For instance, a system using two unidirectional converters incurs significantly higher costs than one employing a bidirectional converter due to increased hardware requirements and control complexity (as Figure A6). Wireless charging and discharging systems generally incur the highest costs. However, high-cost systems often offer advantages such as improved efficiency, better compatibility, and enhanced stability. At this stage of V2G development, the integration of power electronic optimization with economic considerations is particularly crucial.

For TR3, since communication devices and encrypted data transmission are extensively adopted in various industries, including IT security and smart grids, the economic cost of this component primarily relies on empirical baseline data, along with projections of technological advancements in the V2G domain. The economic cost of TR3 implementation primarily depends on the following factors: the scale of the system, which determines the required hardware and affects maintenance complexity; capital investment in hardware and software, including communication security modules, server infrastructure costs, and V2G platform development; operation and maintenance (O&M) costs, covering communication fees, certificate management, cybersecurity maintenance, and regular server/software updates; and the system’s operational lifespan.

TR4 is marked by a substantial rise in complexity with the evolution of business models, leading to a proportional increase in economic costs. The economic cost is projected to increase in tandem with this rising complexity.

Table A3. Functions Enabled by System Scheduling and Optimization Techniques at Different Business Model Phases, Including Applicable Algorithms.

Business Model Phase	System-Level							Control Algorithm-Level
	Coordinated	Centralized	Mobility Aware	RES Integrated	Ancillary Services	Market Mechanisms	Environmental Value	Model-Based	Rule-Based	Learning-Based
1	X	X	X	X	X	X	X	X	√	X
	√	X	X	X	X	X	X	X	√	X
2	√	X	X	√	X	X	X	X	√	X
	√	√	√	√	X	X	X	√	√	X
3	√	√	√	√	√	X	X	√	√	X
	√	√	√	√	√	X	X	√	√	√
4	√	√	√	√	√	√	X	√	X	√
	√	√	√	√	√	√	√	√	X	√

Note: System-Level: “X” or “√” indicates the implementation of certain functions or algorithms under system scheduling and optimization techniques in each business model phase. Refers to the functions that can be implemented in system planning, such as: Coordinated: Ensuring synchronization between EVs, grid operations, and other resources. Centralized: Managing all EV decisions and operations from a central control point. Mobility Aware: Incorporating EV mobility patterns and user behavior into system planning. RES Integrated: Optimizing the integration of Renewable Energy Sources (RES) with V2G operations. Ancillary Services: Supporting grid stability through services like frequency regulation and load balancing. Market Mechanisms: Utilizing pricing strategies and dynamic market participation for economic optimization. Environmental Value: Promoting sustainability by leveraging V2G to reduce carbon emissions and enhance energy efficiency. Control Algorithm: Refers to the types of algorithms applied, including: Model-Based: Using mathematical models to simulate and optimize system operations. Rule-Based: Implementing predefined rules and thresholds for decision-making. Learning-Based: Employing machine learning and AI to adapt to dynamic environments and uncertainties. All information is based on independent synthesis by the authors’ team.

outlines key system planning strategies applicable at different phases of business model evolution.

With the exception of PR1, policies have no direct impact on stakeholders’ economic value assessment. As noted in Appendix A, there is a substantial lack of research on the sociological aspects of V2G compared to its technological aspects. For PR1, we refer to examples from other domains, such as the energy sector and industrial applications. Regarding V2G, we summarize how incentives are structured and implemented across different categories and mechanisms (see

Table A4. Incentive Policy Types, Mechanisms, and Potential Application Scenarios Across Business Model Phase.

Content	Description	Business Model Phase
		P1	P2	P3	P4
Incentive Types
Purchase Subsidy	Financial reimbursement provided to users, parking lot owners, or other stakeholders to offset the initial costs of purchasing electric vehicles (EVs) and installing compatible charging and discharging equipment. This subsidy aims to reduce the upfront investment barrier and encourage the adoption of V2G-compatible infrastructure	√	√	X	X
Tax Reduction	Tax incentives provided to various stakeholders to promote the adoption and development of EV and V2G technology. These include tax breaks for users purchasing or maintaining electric vehicles, tax reductions for corporations owning parking lots with V2G infrastructure, and tax incentives for companies involved in the EV industry, Vehicle Service Providers (VSP), and V2G technology and equipment manufacturing or deployment	X	√	√	X
Electricity Purchase	Subsidies provided to consumers for selling electricity back to the grid, modeled after mechanisms such as Feed-in Tariffs (FIT) or Feed-in Premiums (FIP). These subsidies incentivize users to participate in V2G programs by offering financial compensation for the electricity discharged from their EVs, ensuring a profitable return for their contribution to grid stability and energy supply	X	√	√	√
Electricity Price Incentive	Discounts on electricity costs for EV charging offered to customers participating in V2G programs. This may include preferential charging rates during off-peak hours or special incentive tariffs applied at the point of sale, designed to encourage user engagement and optimize grid efficiency by aligning charging behavior with grid demand	√	X	X	X
Service Subsidy	Special incentives provided to users for utilizing services related to V2G participation, such as discounts or reimbursements for parking fees at public parking lots equipped with V2G-compatible infrastructure. These subsidies may also include priority access to charging stations, reduced membership fees for V2G-related platforms, or other service-based benefits designed to enhance user convenience and promote V2G adoption	√	√	√	√
Technical Assistance	Subsidies provided to enterprises involved in the development, production, or deployment of V2G-compatible technology and equipment. These subsidies aim to reduce the financial burden of innovation and infrastructure expansion, encouraging technological advancements and accelerating the adoption of V2G systems	√	√	X	X
Incentive Mechanisms
One-Time Subsidy	A flat-rate financial incentive provided as a one-time payment upon the occurrence of a specific event, such as the purchase of an EV, installation of V2G-compatible charging equipment, or the initial enrollment in a V2G program.	√	√	X	X
Phased Subsidy	A subsidy mechanism where the amount of financial support changes over defined time periods or based on specific milestones, such as increasing V2G penetration levels, technological advancements, or market maturity. This approach allows for gradual adjustment of incentives to align with market development and policy goals, ensuring a sustainable transition as reliance on subsidies decreases over time	X	√	√	X
Price Difference Subsidy	A subsidy mechanism similar to the Feed-in Tariff (FIT) or Feed-in Premium (FIP) system, where the difference between the cost of electricity sold by participants and the prevailing market price is covered through financial support. This mechanism can also extend to subsidizing the purchase or installation of V2G-compatible infrastructure by offsetting the cost difference between standard and advanced systems, encouraging both energy contributions and technological adoption	X	√	X	X
Dynamic Subsidy	A flexible subsidy mechanism where the amount of financial support fluctuates based on short-term changes in market conditions, such as supply and demand, grid capacity, or electricity prices. This approach ensures that subsidies are responsive to real-time market dynamics, incentivizing participants to adjust their behavior in alignment with grid needs and economic efficiency	X	X	√	√
Guaranteed Minimum Income Subsidy	A subsidy mechanism designed to ensure that participants, whether individuals or corporations, receive a baseline level of income over the long term. This approach aims to mitigate financial risks and prevent losses associated with market fluctuations or unforeseen circumstances, providing stability and encouraging sustained participation in V2G programs or related activities	X	X	X	√

Note: “X” or “√” indicates the possible application of a specific incentive type or mechanism in each business model phase. All information is based on independent synthesis by the authors’ team.

).

We examine two key environmental policies (PR2) that impact the economic valuation of V2G: Carbon Emissions Trading (or carbon pricing mechanisms), which directly influences the quantification of users’ carbon emissions and facilitates offset mechanisms, and Battery Recycling, as a measure to address battery degradation exacerbated by V2G usage. These policies are analyzed under the staged business model theory usually in Phase 4.

Broader environmental policies, such as the integration of renewable energy, are excluded from this discussion as their direct impact on V2G is limited. While the design of environmental policies related to V2G and the evaluation of its environmental value are crucial topics, they fall beyond the scope of this study. Therefore, we do not extend our analysis to additional environmental policies with economic implications. The role of environmental policies in the economic valuation of V2G is a subject for future investigation.

In PR3, we summarize existing standards developed for V2G and those that may be extended to this domain. It should be noted that the standards outlined in PR3 encompass the communication protocols mentioned in TR3. Our findings indicate that standards typically have a broader scope than protocols, often encompassing multiple protocols within their framework. While standards are usually established by regulatory bodies and may be mandatory in certain regions, protocols primarily define technical specifications and operational procedures. Protocols can be compatible with multiple standards, but their implementation is typically constrained by the specific standard adopted within a given regulatory framework. Furthermore, while existing standards cover certain aspects of V2G operation, there is no unified global standard particularly focusing on user behavior and system scheduling rules. Addressing these gaps represents an important research direction for advancing V2G.

Due to significant differences in national energy strategies and legal frameworks (PR4), our discussion is primarily based on selected content from certain countries. This information also contributes to our analysis of the policies and regulations discussed above. Further research into the energy strategy and legal aspects of V2G is beyond the scope of this study. Consequently, our discussion in this area is limited to selected examples, and the impact of energy strategies and legal frameworks on V2G business models warrants further in-depth analysis within specific analytical contexts.

Mechanisms of Influencing Factors

Most importantly, we find that the mechanisms through which influencing factors operate follow a Hierarchical Progressive structure, as shown in Figure 11. While Figure 3 provides a macro-level overview of the BSTP framework, including its hierarchical structure and overall components, Figure 11 specifically illustrates how the outer-layer influencing factors interact with and impact the middle-layer stakeholders. At the top level, the Government and PR4_Legal Regulations and National Energy Strategy function as the Decision-Making and Regulatory Group, establishing checks and balances that vary depending on geographical and institutional contexts. This group is responsible for setting policies, driving their implementation, and legislating regulations that ensure V2G compliance with appropriate operational frameworks. Additionally, they regulate the financial and energy markets, including market operations and reforms.

Under the influence of the Decision-Making and Regulatory Group, several key entities form the Rule-Making Group, including PR1_Incentive Policies, Environmental Organizations and their PR2_Environmental and Sustainability Policies, Regulatory Standardization and Certification Bodies and their PR3_Technical and Standardization Regulations.

Within this framework, PR1 and some PR2 policies, such as battery recycling regulations, directly impact the economic value of specific stakeholders, which we define as direct stakeholders. Meanwhile, PR3 influences direct stakeholders indirectly through the Technical Group (TR1–4, see Table 2), while PR2 acts through the Market and Financial Group. These interactions create a structured pathway for how policies and technical standards shape economic outcomes.

The direct stakeholders referred to here are the same as those defined in Figure 3. In contrast, other stakeholders—who are not directly affected—may benefit through other mechanisms such as developing standards and regulations and collecting licensing fees, providing trading platforms and financial services that act as intermediaries, or technology entities that seek to integrate V2G applications. This differentiation highlights the layered impact of policy and technical decisions on various actors within the V2G ecosystem.

3.4. Conceptual Modeling of the BSTP Framework and an Illustrative Example

3.4.1. Conceptual Modeling of the BSTP Based on Value Realization Rate

So far, we have established the theoretical foundation of the BSTP framework. To enhance its practical applicability and analytical robustness, we now proceed to develop a mathematical model.

We define a space $S$ representing the domain in which the BSTP framework operates under any spatio-temporal or contextual setting as $S=(\mathcal{I}, \mathcal{T}, \mathcal{P})$ where: $\mathcal{I}=\{A_1, A_2, B_1, ..., D_3, D_4\}$ denotes the set of stakeholders involved in the system, $\mathcal{T}=\{TR1, TR2, TR3, TR4\}$ represents the available technological pathways, $\mathcal{P}=\{PR1, PR2, PR3, PR4\}$ denotes the policy and regulatory conditions that influence the system.

For a specific scenario operating under a given spatio-temporal context, we define a subspace $s\subseteq S$, which consists of a selected subset of elements from S. For example, the Phase 1 scenario described in Section 3.1.1, denoted by $s_1$, is defined as: $s_1=\{A_1, A_2, C_1, C_2, D_2, TR1, TR2, PR1, PR4\}$.

This configuration indicates that the Phase 1 analysis considers the following actors and elements: users, parking lot owners, transmission and distribution system operators (TDSOs), infrastructure providers, and governmental agencies; battery technology and charging/discharging power electronics; as well as economic incentives and national energy strategies within the legal and regulatory framework.

By integrating these foundational elements, the economic value of the simplest energy trading business model under Phase 1 can be systematically evaluated.

For a given stakeholder $i$ operating within a specific subspace $s\subseteq S$, we define a set of abstract functions to characterize both the changes in its economic value and its interactions with other stakeholders $j\in \mathcal{I}$.

To begin with, we define the function ${\alpha }_i$, which represents the benefit accrued to stakeholder $i$ at a specific time $t$, as shown in Equation (1):

$${\alpha }_i=f\left({{\sum }_j{R}_{i,j}\mathcal{T}}_i,{\mathcal{P}}_i,t\right)$$

(1)

Taking stakeholder $A_1$ in the scenario $s_1$ as an example, its economic benefit includes both the arbitrage value from purchasing electricity from the TDSOs ($r_{A_1,C_1}$) and the government subsidy received ($r_{A_1,D_2}$). This is formalized in Equation (2):

$${\alpha }_{A_1}=r_{A_1,C_1}+r_{A_1,D_2}$$

(2)

It is important to emphasize that the function ${\alpha }_i$ can also represent a more generalized form of utility. For example, the government (stakeholder $D_2$) may seek not only direct economic returns, but also additional value dimensions such as environmental value (denoted as $r_{D_2,D_3}$), grid stability ($r_{D_2,C_1}$), and V2G penetration ($r_{D_2,A_1}$).

In such cases, ${\alpha }_{D_2}$ represents a transformed or composite utility, incorporating multiple value components. This relationship is defined in Equation (3):

$${\alpha }_{D_2}={{\omega }_1·r}_{D_2,D_3}+{{\omega }_2·r}_{D_2,C_1}+{{\omega }_3·r}_{D_2,A_1}$$

(3)

where ${\omega }_1$, ${\omega }_2$ and ${\omega }_3$ denote the respective weighting coefficients assigned to each component, reflecting the government’s prioritization of environmental impact, grid stability, and technology diffusion in its value composition.

Different technological factors ($\mathcal{T}$) and policy factors ($\mathcal{P}$) will influence the structure of the function $r$, and this influence varies depending on the stakeholder $i$. For example, the presence of $TR2$ affects the discharge efficiency of user $A_1$. If a specific policy factor such as $PR1$ is not included in the scenario (i.e., $PR1 \notin s_1$), then the government subsidy received by $A_1$ becomes zero, $r_{A_1,D_2}=0$.

Next, we define the function ${\beta }_i$, as shown in Equation (4), to represent the cost incurred by stakeholder $i$ within the V2G system at a specific time $t$:

$${\beta }_i=g({{\sum }_j{C}_{i,j}\mathcal{T}}_i,{\mathcal{P}}_i,t)$$

(4)

Similarly, it is possible to define a more specific cost function for stakeholder $A_1$ in the scenario $s_1$, as expressed in Equation (5). In this case, the total cost consists of two components:

$c_{A_1,D_4}$ denotes the additional cost associated with battery degradation, which is determined by the presence of $TR1$;

$c_{A_1,C_2}$ represents the infrastructure investment cost, which is influenced by $TR2$.

The composite cost function is therefore given by:

$${\beta }_{A_1}=c_{A_1,D_4}+c_{A_1,C_2}$$

(5)

In most existing research, the focus has been on analyzing ${\rho }_i$ (as in Equation (6)), which represents the actual benefit of stakeholder $i$ at a particular moment $t$, or on establishing a general utility function (Equation (7)) that quantifies the aggregate benefit of multiple stakeholders within the system.

$${\rho }_i={\alpha }_i-{\beta }_i$$

(6)

$${\sum }_{i\in s}{\rho }_i$$

(7)

To enhance the economic evaluation of the V2G system, we propose a Value Realization Rate (VRR) indicator, which enables a more comprehensive assessment of both economic value and system feasibility, as defined in Equation (8).

$${VRR}_i={\displaystyle \frac{{\rho }_i}{{\rho }_i^{*}}}$$

(8)

In this formulation, ${\rho }_i^{*}$ denotes the expected economic value for stakeholder $i$, typically treated as a constant reference value. This value can differ depending on the stakeholder:

For $A_1$, ${\rho }_i^{*}$ may represent the expected electricity sales revenue per unit of time or the anticipated subsidy;

For $C_2$, it may reflect the expected return on infrastructure technology investment;

For $D_4$, it may correspond to the allocated budget or expected expenditure cap.

The VRR indicator thus serves as a benchmarked ratio between actual and expected value realizations, facilitating performance comparisons across scenarios.

Furthermore, we redefine ${\rho }_i$ in Equation (9) by incorporating ${\varphi }_i$ (Equation (10)), which accounts for the influence of stakeholder relationships on their realized returns.

$${\rho }_i={\varphi }_i({\alpha }_i-{\beta }_i)$$

(9)

$${\varphi }_i=h\left({\prod }_j{X}_{i,j},t\right)$$

(10)

Taking the Fusion Interactions described in Section 3.2.1 as an example, the economic value assessment for user $A_1$ may evolve through the integration process with Parking Lot Owners ($A_2$) and Regional Energy Suppliers ($A_3$), as shown in Equation (11).

$${\varphi }_{A1}=x_{A_1,A_2}·x_{A_1,A_3}$$

(11)

In this case, ${\varphi }_{A_1}$ represents the impact coefficient reflecting the post-integration adjustment in $A_1$’s value realization. This coefficient captures potential financial transfers to or from $A_2$ and $A_3$ based on predetermined sharing ratios or access to more favorable terms. As a result, integration may lead to enhanced net benefits for stakeholder $A_1$.

When applying either a continuous or discrete model for analysis, we obtain ${\mathrm{\Delta }VRR}_i$ (Equations (12)–(14)).

$$\mathrm{\Delta }{\rho }_i={(\rho }_{i,t}-{\rho }_{i,to})·\mathrm{\Delta }\mathrm{t}$$

(12)

$$\mathrm{\Delta }{\rho }_i={\int }_{to}^{t}d\dot{{\rho }_i}$$

(13)

$$\mathrm{\Delta }{VRR}_i={\displaystyle \frac{\mathrm{\Delta }{\rho }_i}{{\mathrm{\Delta }\rho }_i^{*}}}$$

(14)

At this stage, the ideal design objectives of the V2G system, as conceptualized under the BSTP framework, can be formalized as shown in Equation (15).

$${}_{s\in S}{}^{max}{\sum }_{i\in s}{\mathrm{\Delta }VRR}_i$$

(15)

which is subject to the constraint that ${\mathrm{\Delta }VRR}_i>0$ for all $i\in s$. This condition ensures that, for a given economic evaluation space $s$ within the V2G system, the total Value Realization Rate across all participating stakeholders during a period $\mathrm{\Delta }\mathrm{t}$ is maximized, while guaranteeing that every stakeholder remains in a profitable state (i.e., with positive $\mathrm{\Delta }VRR$).

The interpretation of ${\mathrm{\Delta }VRR}_i$ follows a structured approach:

• ${\mathrm{\Delta }VRR}_i\ge 1$ signifies that the acquired economic value or utility aligns with or exceeds the expected level.
• 0 <${\mathrm{\Delta }VRR}_i<1$ suggests that while the stakeholder gains economic value or utility, it does not meet expectations.
• ${\mathrm{\Delta }VRR}_i$≤ 0 indicates that the stakeholder operates at a loss within the V2G system.

Detailed definitions of all symbols used in the preceding equations are summarized in

Table 3. Notation and Definitions for VRR Analysis.

Symbol	Name	Description	Dimension
$S$	Set of domains	Represents the domain in which the BSTP framework operates under any spatial-temporal or contextual.	Domain Set
$\mathcal{I}$	Set of stakeholders	Represents the set of stakeholders involved in the system, $\mathcal{I}=\{A_1, A_2, B_1, ..., D_3, D_4\}$, refer to Table 1 for details.	Set
$\mathcal{T}$	Set of technical routes	Represents the set of technical routes factors influencing stakeholders, $\mathcal{T}=\left\{TR1, TR2, TR3, TR4\right\},$ refer to Table 2 for details.	Set
$\mathcal{P}$	Set of policies and regulations	Represents the set of policy and regulations factors influencing stakeholders, $\mathcal{P}\in \{PR1,PR2,PR3,PR4\}$, refer to Table 2 for details.	Set
$s$	Subspace in $S$	A subspace $s\subseteq S$, which consists of a selected subset of elements from S.	Set
$s_1$	Subspace for phase 1	The Phase 1 scenario described in Section 3.1.1, which is defined as: $s_1=\{A_1, A_2, C_1, C_2, D_2, TR1, TR2, PR1, PR4\}$.	Set
$\alpha$	Benefit accrued to stakeholder	Represents the cumulative benefits received by a stakeholder at a given moment.	Utility/Time
$\beta$	Cost incurred by stakeholders	Represents the total costs incurred by a stakeholder at a given moment.	Utility/Time
$\varphi$	Influence coefficient of stakeholder relationships	A cumulative factor representing changes in revenue resulting from complex relationships between stakeholders at a given moment.	Dimensionless
$\rho$	Net benefits	Represents the net benefits received by stakeholders, calculated as the difference between costs and revenues.	Utility/Time
$R$	Set of benefit functions	A set of functions used for modeling benefits in $\alpha .$	Function Set
$C$	Set of cost functions	A set of functions used for modeling costs in $\beta .$	Function Set
$X$	Set of influence coefficient functions	A set of functions used for modeling influence coefficient in $\varphi .$	Function Set
$r$	Benefit function	A function representing a specific benefit received by stakeholders over time.	Function
$c$	Cost function	A function representing a specific cost incurred by stakeholders over time.	Function
$x$	Influence coefficient function	A function representing a specific influence coefficient between stakeholders over time.	Function
$\omega$	Weight of utility	The weights of different value elements (e.g., environmental, economic).	Dimensionless
$t$	Time	Represents an abstract moment in a continuous function or a discrete point in time in a different function.	Time
$VRR$	Value Realization Rate	Represents the ratio of a user’s actual revenue to their expected revenue.	Dimensionless
$i$	A specific stakeholder	Represents a specific stakeholder, where $i\in \mathcal{I}$.	Index
$j$	A specific stakeholder interacts with $i$	Represents stakeholders that influence or engage in transactions with $i$, where $j\in \mathcal{I},j\ne i$.	Index

. It is worth emphasizing that the foregoing mathematics provides only a highly abstract, conceptual outline of how the BSTP framework can be modeled. In practice, the VRR-based evaluation can be integrated with a variety of analytical tools—such as multi-criteria decision analysis (MCDA), game-theoretic formulations, agent-based simulations, or other quantitative methods. Consequently, the mathematical representation presented here is not unique; it should be adapted and tailored to the specific research question and modeling approach adopted.

In the present study, we offer the concept and one concise illustrative case. Owing to space limitations, a full specification of the model and an exhaustive analytical demonstration cannot be provided here. A brief case study is given in Section 3.4.2, while a comprehensive treatment will be presented in our forthcoming work.

3.4.2. An Illustrative Example

This section analyzes the economic value and feasibility of a V2G system under the Phase 1—Initial Deployment scenario in Japan, using the BSTP framework and the VRR methodology. The analysis is conducted based on the following assumptions regarding the business model, stakeholders, and system operations:

Referring to Figure 4, we assume that five key stakeholders—A₁, A₂, C₁, C₂, and D₂ (see Table 1)—are involved in Phase 1. Among these, A₁ and A₂ are assumed to be highly integrated; that is, vehicles are charged exclusively at private home parking lots. Public parking and shared residential facilities (e.g., apartment complexes) are excluded from consideration.

The policy environment is assumed to permit reverse electricity flow and sales from individual users to the grid. A fixed-price buyback mechanism, similar to Japan’s Feed-in Tariff (FIT) system, is implemented. This mechanism compensates users at a predetermined rate for electricity supplied back to the grid, covering both the user subsidy and equipment depreciation costs.

From the user side, all EV owners are assumed to be homogeneous: they use the same type of vehicle, follow identical driving and charging patterns, and exhibit no variation in V2G acceptance or participation behavior. Each user is assumed to discharge 60% of their EV battery capacity daily back to the grid. Factors such as TR3 and TR4 are not considered at this stage.

For TDSOs, we ignore potential costs related to grid access, metering, and system stability that might be incurred by retrofitting infrastructure to accommodate V2G functionality.

For technology and infrastructure providers, it is assumed that all development and deployment costs are fully subsidized by the government, resulting in zero infrastructure costs for end-users.

Regarding the government, it is assumed that its V2G subsidy budget is financed through an increase in the national electricity tariff. This mechanism parallels the Renewable Energy Empowerment Credit (REEC) structure under the FIT system. The government’s objective is to maximize V2G penetration while minimizing its fiscal burden—in other words, achieving the highest adoption rate at the lowest possible increase in electricity bills.

Under these assumptions, the VRR-based model is constructed as shown in Equations (16)–(23).

$$s_1=\{A_1, C_1, C2, D2, TR1, TR2, PR1, PR4\}$$

(16)

$${}_{s_1}{}^{max}{\sum }_{i\in s_1}{\mathrm{\Delta }VRR}_i$$

(17)

$${\mathrm{\Delta }VRR}_i>0,i\in s_1$$

(18)

$${\mathrm{\Delta }VRR}_{A_1}={\displaystyle \frac{r_{A_1,D_2}-c_{A_1,D_4}}{{\rho }_{A_1}^{*}}}·\mathrm{\Delta }\mathrm{t}$$

(19)

$${\mathrm{\Delta }VRR}_{C_1}\equiv 1$$

(20)

$${\mathrm{\Delta }VRR}_{C2}\equiv 1$$

(21)

$${\mathrm{\Delta }VRR}_{D2}={\displaystyle \frac{{\rho }_{D_2}^{*}-c_{D_2,A_1}-c_{D_2,C_2}}{{\rho }_{D_2}^{*}}}·\mathrm{\Delta }\mathrm{t}$$

(22)

$$\pi \equiv 0.06$$

(23)

This is where $r_{A_1,D_2}$ represents the subsidy received by the user, $c_{A_1,D_4}$ denotes the battery loss incurred by the user when participating in the V2G process, ${\rho }_{A_1}^{*}$ indicates the profit expected by the user, and ${\rho }_{D_2}^{*}$ refers to the government’s budget allocated for the electricity tariff increase. $c_{D_2,A_1}$ denotes the cost of the user subsidy borne by the government, while $c_{D_2,C_2}$ represents the infrastructure investment cost also paid by the government. All values are defined on a per-unit-of-time and per-unit-of-electricity basis. The variable $\pi$ represents the penetration rate of V2G (0.06 is an assumption in Appendix A.2, serving as the threshold value reached in Phase 1).

The results illustrated in Figure 12 demonstrate that all three variables—${\mathrm{\Delta }VRR}_{total}$, ${\mathrm{\Delta }VRR}_{A_1}$ and ${\mathrm{\Delta }VRR}_{D2}$—exhibit approximately linear trends as the monthly user subsidy increases. Specifically, both ${\mathrm{\Delta }VRR}_{total}$ and ${\mathrm{\Delta }VRR}_{A_1}$ increase monotonically from negative values, reflecting a gradual improvement in stakeholder benefits. In contrast, ${\mathrm{\Delta }VRR}_{D2}$ shows a declining trend while remaining positive, indicating that government expenditures are steadily consumed but not yet exhausted.

Two critical thresholds are highlighted in the graph:

• Around 7000 JPY per month, both ${\mathrm{\Delta }VRR}_{total}$ and ${\mathrm{\Delta }VRR}_{A_1}$ become positive. This represents the point at which all stakeholders begin to receive net economic benefits, signaling a win-win equilibrium for the overall system.
• Around 12,000 JPY per month, ${\mathrm{\Delta }VRR}_{D2}$ approaches zero, suggesting that public expenditures or system-level burdens may exceed sustainable limits—i.e., beyond this point, subsidy costs may surpass the government’s budget capacity or acceptable cost-benefit thresholds.

This figure underscores the utility of the VRR method in evaluating economic outcomes across multiple stakeholders in the context of V2G systems. It offers quantitative insights to guide the design of subsidy policies, striking a balance between user incentives and systemic sustainability. These results highlight the potential of the VRR approach as a powerful analytical tool for policy formulation and system optimization.

4. Discussion

Now that we have outlined the BSTP framework and the VRR methodology for its application in economic value analysis, this section examines the distribution of BSTP elements across existing V2G economic literature, as illustrated in Figure 13. The goal is to identify prevailing research trends and highlight key gaps in the current body of work.

4.1. Big Models, No Trials

Based on our review of 132 articles related to the economic analysis of V2G (see Supplementary Data S1), most studies appear to be “ahead of their time.” As shown in Figure 13a, over 47% of the studies focus on the P3 and P4 of the business model, which involve large-scale V2G applications in electricity markets, carbon trading, or advanced decentralized energy systems. While these technologies hold significant promise, only 14% of the studies examine the P1 stage, where applications such as single-user arbitrage could be more easily validated.

This misalignment is further highlighted in Figure 13f, where only 12% of the studies incorporate real-world data—mainly from battery degradation experiments or individual surveys—while over 56% rely entirely on hypothetical scenarios. Additionally, Figure 13e shows that more than 63% of the studies use optimization-based simulations, often prioritizing technical complexity, model intricacies, and algorithmic details over clear, comprehensive conclusions about economic viability.

This tendency to focus on theoretical sophistication rather than practical validation creates a disconnect between research and real-world implementation. Policymakers and investors, who play a crucial role in driving V2G adoption, require tangible economic insights to inform decision-making. However, the prevailing “Big Models, No Trials” approach risks trapping V2G research in a cycle of academic discourse with limited real-world impact.

4.2. Filling the Missing Pieces

To address the “Big Models, No Trials” issue and facilitate the practical application of the $VRR$ method, we propose the following key research directions:

• Targeted Breakthrough Research

As shown in Figure 13g, over 51% of existing studies focus on localized systems (e.g., microgrids or distributed energy systems). However, research based on single-user experiments, surveys, and data integration remains crucial. These foundational studies can provide robust empirical support for the implementation of $VRR$ and similar approaches, strengthening the theoretical framework with real-world validation.

2.

Step-by-Step and Systematic Studies

A structured, incremental approach is essential for advancing V2G research. Our study emphasizes the importance of P1 and P2, where clearly defining cost-benefit flows and identifying stakeholder boundaries is critical. This structured perspective aligns with the insights of Huber et al. [67] and Kim et al. [72].

3.

Exploring Intra-Stakeholder Relationships

As shown in Figure 13b, over 61% of studies consider only a single stakeholder. However, V2G is inherently a multi-stakeholder system. Our review identifies several studies [29,43,66,80,109] that acknowledge the Value Chain Gaming among multiple stakeholders. Nearly all studies involving system scheduling focus on Transactional Dynamics between stakeholders, yet none have examined Fusion Interactions. While 38% of studies include multiple stakeholders, particularly in microgrid contexts, many rely on hypothetical scenarios rather than real-world implementations. Further research is needed to understand the complex benefit distribution mechanisms in actual V2G ecosystems.

4.

Addressing “Cold” Stakeholders

Figure 13b also shows that nearly 80% of studies primarily focus on users, with CVPs (Core V2G Participants) and VSPs (V2G Service Providers) receiving some attention. However, apart from a few studies on Energy Market Operators [134,136], there is a notable lack of research on Technology and Infrastructure Providers, the Automotive Industry, and the Financial Sector. While these entities are not directly involved in V2G transactions, their role in shaping business models is critical.

5.

Technological Integration of Charging, Discharging, and Communication Infrastructure

The economic evaluation of V2G is heavily influenced by infrastructure and communication technologies. Although some studies [94,95] explore these aspects, they are often overshadowed by research on batteries and energy planning. As shown in Figure 13c, over 50% of studies focus on the latter, whereas fewer than 10% analyze charging and discharging equipment or communication infrastructure. Additionally, research on regional differences, optimal selection of charging/discharging and communication equipment, and economic cost comparisons of different topologies remains largely unexplored.

6.

Investigating the Impact of Policy and Regulation Frameworks

As seen in Figure 13d, research on policy and regulation impacts accounts for less than 10% of V2G studies, with incentives policy addressed in only 9.8% of cases. This is insufficient, as incentive structures and mechanisms are fundamental drivers of P1 and P2 adoption. Furthermore, user behavior regulation, given the potential large-scale participation of EV users in V2G, requires systematic examination. While preliminary operational frameworks have been proposed by different entities, they remain incomplete. Legal frameworks, energy policies, and governance strategies should be further explored to create a stable and scalable V2G ecosystem.

5. Conclusions

To enhance the economic evaluation of V2G systems, this study adopts the Semi-Systematic Co-Design Approach to develop the BSTP framework, which integrates four key dimensions: Business Models, Stakeholders, Technological Routes, and Policies and Regulations. We construct a four-phase evolutionary process for V2G business models, identify 13 potential stakeholders within the V2G ecosystem, and conduct an in-depth analysis of three types of stakeholder-specific relationships. Additionally, we categorize the key influencing factors into two major groups—Technological Routes and Policies and Regulations—detailing their content and mechanisms within V2G systems.

Furthermore, we propose the Value Realization Rate (VRR) evaluation method to facilitate the application of BSTP in assessing the value and feasibility of V2G economic systems. This method is informed by our review of 132 core studies on V2G economic analysis, through which we identify six critical challenges that must be addressed in future research on V2G economic evaluation.

Unlike studies that focus primarily on technical aspects, our research provides a clear and direct reference for policymakers and investors, offering insights essential for shaping the development of the V2G industry. Ultimately, this study contributes to advancing V2G adoption and plays a pivotal role in the broader transition toward decentralized energy systems.

However, it is important to note that this study is conceptual in nature and intended as a review-based synthesis. The proposed BSTP framework and the VRR method are not empirically validated in this paper. Instead, we provide only a simplified illustrative example to demonstrate potential applications. Future studies should focus on collecting real-world data to validate and refine the proposed models and methods.

Moreover, as this research is primarily situated within the market and technological context of Japan, the generalizability of the framework may be limited. Differences in energy market structures, regulatory systems, and levels of technological maturity across countries necessitate further adaptation and localization of the BSTP framework. Future work should explore cross-national applications and conduct empirical assessments to enhance the global relevance and robustness of the proposed approach.