Introduction

The increasing recognition of global warming and its related greenhouse gas emissions has driven the engineering sector to explore renewable energy sources and develop sustainable energy systems. Solar energy, which can be transformed into electricity via photovoltaic (PV) systems in an exceptionally clean manner, stands out as one of the most accessible energy sources. Recent advancements in solar technology, coupled with a decrease in the costs of PV products, have created a significant opportunity to produce low-cost electricity with minimal environmental repercussions. Solar PV installations rank among the most economically viable renewable energy solutions, and the adoption of solar panels in buildings is on the rise due to enhanced efficiency and lower initial costs1,2. Distributed rooftop photovoltaics, predominantly located on the roofs of industrial and commercial structures, have witnessed significant expansion in China over the last ten years, as demonstrated in Fig. 1, increasing from 0.41 GW in 2011 to 15.52 GW in 2020. However, since 2018, China has progressively scaled back subsidies for solar photovoltaic power generation. The introduction of electricity price bidding in 2022 and the occurrence of negative electricity prices in 2024 have considerably diminished the advantages of new energy microgrids, resulting in an extended payback period for investments3,4.

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
Full size image

Development of distributed photovoltaic power stations in China in the past decade (scale: GW).

In the world, the microgrid market is experiencing rapid growth to mitigate the volatility and unpredictability associated with renewable energy sources, while also improving the economic viability and stability of energy systems. Research efforts are advancing in the areas of wind energy utilization and integrated urban energy systems. Comprehensive risk assessment and management strategies are employed to oversee the anticipated profit distribution of wind storage systems, utilizing a risk-averse stochastic decision-making framework alongside various risk metrics, such as conditional value at risk, value at risk, and the probability of shortages5. These strategies offer robust solutions tailored to different categories of risk-averse decision-makers. Furthermore, models have been developed for the planning of integrated energy systems in urban settings, as well as reward-penalty ladder carbon trading models aimed at addressing carbon emissions6. These models assess performance by aggregating costs related to equipment operation and maintenance, daily procurement, energy purchases, carbon trading, and comprehensive demand response. They also account for the stochastic nature of renewable energy, quantifying the associated uncertainty intervals and intermittent fluctuations. Nevertheless, despite these advancements, the practical implementation of Vehicle to Grid (V2G) technology has been sluggish, hindered by the complexities of technology and system support requirements, which adversely affect the grid7,8,9.

Previous studies on microgrids have predominantly concentrated on strategies for managing grid-connected electricity pricing10 and the configuration of energy storage systems11. In China, the three principal power grid companies—State Grid Corporation of China, China Southern Power Grid Corporation, and Mengxi Power Grid Corporation—exercise significant control over the power grid ecosystem12. Consequently, the majority of existing microgrid research is conducted by these power grid companies13, with limited investigations into new energy microgrids from the perspective of stakeholder equity. The value chain system, which encompasses a framework established through the coordinated scheduling of multiple stakeholders, resources, information flows, and value symbiosis, seeks to optimize value through vertical value creation activities involving upstream, midstream, and downstream stakeholders, as well as the horizontal competitive and cooperative dynamics among various links14. Nonetheless, the implementation of value chain systems within microgrids remains relatively rare15.

The swift advancement of distributed photovoltaic power stations in China, coupled with the economic challenges arising from policy adjustments, highlights critical issues related to high dependency on policies and limited market adaptability within the realm of new energy microgrids. Furthermore, technical obstacles and grid integration challenges indicate that, despite the potential of microgrid technology, its practical implementation and growth are still constrained by current technological capabilities and the level of modernization of grid infrastructure. Additionally, the inadequate utilization of value chain systems in microgrids implies that the opportunities for value co-creation among various stakeholders remain largely untapped. This scenario necessitates that researchers and practitioners not only prioritize technological advancements but also investigate strategies to foster collaboration and value sharing among diverse stakeholders through policy frameworks, market incentives, and technological innovations, thereby facilitating the sustainable development of new energy microgrids.

European research has investigated innovative strategies for microgrid development, particularly in the integration of renewable energy sources and the promotion of community involvement16. In contrast, studies in the United States have concentrated on advanced grid management technologies and market mechanisms aimed at creating sustainable energy systems17,18. By placing this study within a global context, it becomes clear that, despite significant international advancements, there is a notable deficiency in research that addresses the specific challenges posed by China’s policy-driven microgrid ecosystem. This study aims to fill this gap by analyzing China’s unique circumstances while also drawing insights from international best practices.

This paper examines the antecedent configuration of value co-creation among various stakeholders within a multi-agent microgrid system, employing a configuration perspective through Fuzzy-set Qualitative Comparative Analysis (fsQCA). The research identifies the antecedent conditions and their impacts that facilitate the realization of multi-stakeholder value co-creation in new energy microgrids, while also exploring the pathways to achieve this co-creation. This investigation aims to elucidate the process mechanisms underlying value co-creation in new energy microgrids and to foster their sustainable development. This innovative methodological framework offers advantages in both Chinese and international contexts. This comprehensive viewpoint not only strengthens the theoretical foundations of value co-creation as proposed by Prahalad and Ramaswamy19, but also highlights its capacity to yield insights that transcend China’s boundaries, thereby fostering the advancement of microgrid development globally.

The results reveal that the pathways to value co-creation in new energy microgrids predominantly manifest in three categories: third-party service support-dominated, distributed power generation-dominated, and all-factor agglomeration types. Furthermore, policy support, electricity subsidies, and financial assistance are recognized as critical enablers for value co-creation. Additionally, lowering initial capital expenditures can significantly enhance the operational sustainability of new energy microgrids.

The findings of this study can be applied in various regions by selecting one of three distinct pathways or a combination thereof, tailored to the unique renewable energy potential, economic conditions, consumer behaviors, and societal attitudes towards renewable energy in each area. By strengthening the role of third-party services, the expertise in energy management and personalized services can be leveraged to address technical challenges effectively. In regions with high energy demand, it is essential to continue expanding solar and other renewable energy sources. The full-factor aggregation approach underscores the significance of integrating diverse resources and stakeholders to enhance efficiency and foster value co-creation. On a global scale, these insights can also promote international collaboration in technology and infrastructure development, thereby advancing the establishment of a sustainable energy future worldwide.

Materials and methods

Defining the stakeholders

From the perspective of the service ecosystem, value co-creation is a dynamic process wherein participants within intricate network systems collaboratively generate value through the integration of resources and interactive cooperation20. Throughout the value creation process, participants assume dual roles as both resource providers and recipients. Each participant contributes value based on the unique heterogeneity of their resource endowments and achieves their own value acquisition through resource integration and collaborative interaction21. As the theory of value co-creation continues to evolve, some scholars argue that in complex network systems, value co-creation is collectively driven by multiple entities, highlighting that all participants serve as integrators of value co-creation resources.

Stakeholder of new energy microgrids

According to the theory of value co-creation, the new energy microgrid functions as a dynamically evolving complex network system that facilitates the process of value co-creation, encompassing value sharing (input) and value acquisition (output). Within this framework, various value creation agents—including energy suppliers, governmental bodies, investment institutions, and electricity consumers—utilize the new energy microgrid as a collaborative network. They engage in value sharing based on the principles of open cooperation and actively participate in energy exchanges through both internal and external network connections. This engagement enhances the efficient transmission and circulation of internal resources while ensuring the timely input and output of external resources, thereby enabling multi-agent value co-creation. Each agent continuously generates terminal value for the new energy microgrid, contributing to the maximization of value for innovation agents. Furthermore, each agent not only provides feedback to the new energy microgrid, fostering its high-quality development, but also encourages other agents to engage in value co-creation through value sharing. This interaction cultivates a virtuous cycle of continuous cooperation and resource integration.

Consequently, this paper identifies the input factors that facilitate value co-creation by utilizing the theoretical framework of “value sharing (value co-creation input) --- value acquisition (value co-creation output).” These factors are examined at three levels: the primary grid entity of the new energy microgrid, the distributed power generation entity, and the third-party service entity.

Current status of new energy microgrids

The existing power grid is composed of the ecological chain of power generation22,23, transmission, transformation, distribution and consumption. Intermittent power sources (photovoltaic, wind power, etc.), controllable power sources (thermal power, nuclear power, etc.), transmission and transformation networks, energy storage stations, and power users are constructed by various small power networks24,25, as shown in Fig. 2.

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
Full size image

Power grid system.

A microgrid is defined as a compact power generation and distribution system that includes distributed energy resources, energy storage systems, energy conversion technologies, loads, and monitoring and protection equipment, as illustrated in Fig. 326,27. The concept of microgrids is designed to facilitate the flexible and efficient utilization of distributed energy resources while addressing the challenges associated with the integration of diverse distributed power systems. The advancement and proliferation of microgrids significantly enhance the large-scale incorporation of distributed energy sources and renewable energy, ensuring a highly reliable supply of various energy forms28. This approach serves as an effective strategy for realizing active distribution networks, thereby transitioning traditional power grids into smart grids29.

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
Full size image

Microgrid system.

As illustrated in Fig. 3, the extensive power grid consists of N smaller power grids that operate on similar systems. The increasing adoption of residential photovoltaics and distributed factory rooftop photovoltaics has introduced uncontrollable intermittent power sources and fixed energy storage units into these small power grids, thereby naturally creating N microgrid systems30,31,32. The value chain of microgrids encompasses three key entities:

Main power grid

In the microgrid value chain, the main grid serves several essential roles that are vital for the stable operation, economic viability, and sustainable growth of microgrids. Acting as the primary connection between microgrids and the external power market, the main grid supplies necessary power, particularly during periods when microgrid generation is inadequate. Additionally, it assists microgrids in regulating power, balancing supply and demand, and enhancing system stability, thereby fulfilling both supply and regulatory functions. The main grid is also tasked with ensuring the safe and efficient interconnection of microgrids, facilitating their integration into the broader power system. The advancement of grid connection technologies and the establishment of relevant policies are critical for the commercial viability and power trading capabilities of microgrids. By developing technical standards and interface specifications, the compatibility and interconnectivity of microgrid systems are enhanced, leading to improved efficiency and reliability across the entire network33. Furthermore, the main grid provides microgrids with access to the power market, enabling participation in mechanisms such as electricity trading and demand response. Through the main grid, microgrids can engage in buying and selling electricity. The main grid also offers technical support to address challenges related to grid stability and power quality, along with planning, operational, and maintenance services to ensure the long-term stability of microgrids. To maximize the main grid’s role within the microgrid value chain, it is essential to establish an effective collaboration mechanism that aligns the interests and objectives of all stakeholders, thereby fostering the development of microgrids34.

Distributed power generation entities

Distributed generation entities are integral to the microgrid value chain. They not only engage in the direct production and supply of energy but also enhance the economic, sustainable, and social impacts of microgrid initiatives through technological innovation, cost management, policy adaptation, and collaborative efforts.

Regarding energy supply and diversification, distributed generation entities are tasked with supplying energy to microgrids, delivering clean and reliable electricity by deploying renewable energy technologies such as solar photovoltaics, wind turbines, and small hydropower systems. This decentralized energy provision reduces reliance on centralized power plants while enhancing energy diversity and flexibility.

In terms of cost-effectiveness and return on investment, these entities can lower initial capital and operational expenses, thereby improving the overall economic performance of the system. This is achieved through strategic investments in high-quality construction, including the selection of cost-efficient components and optimized designs. Furthermore, effective maintenance practices and investments in energy storage solutions can minimize downtime and maintenance costs, thereby enhancing return on investment.

With respect to balancing supply and demand and ensuring grid stability, distributed generation entities facilitate the equilibrium of supply and demand within microgrids by utilizing energy storage systems and intelligent scheduling technologies, particularly during fluctuations in renewable energy generation. This approach reduces reliance on costly peak-shaving power sources and bolsters grid stability and reliability.

In the realm of technological innovation and advancement, distributed generation entities often lead the charge in adopting and promoting new technologies and products, thereby driving the innovation and enhancement of microgrid technologies. Such technological advancements not only boost power generation efficiency but also foster broader industry development.

Concerning policy responsiveness and market adaptation, distributed generation entities must proactively engage with government policy support and market incentives, such as per-kilowatt-hour subsidies and financial assistance, to enhance the financial viability and market competitiveness of their projects. They must also remain adaptable to policy shifts and market dynamics to ensure the sustainable progression of their initiatives, particularly in light of diminishing government subsidies.

In terms of environmental and social responsibility, distributed generation entities contribute to the reduction of greenhouse gas emissions and the fight against climate change by supplying clean energy. Additionally, they can fulfill their social obligations and amplify the societal impact of microgrid projects by generating employment opportunities and fostering local economic growth.

Finally, regarding multi-party cooperation and coordination, distributed generation entities must collaborate with various stakeholders, including main grid operators, governmental bodies, and financial institutions, to achieve resource sharing, risk mitigation, and mutually beneficial outcomes through coordinated consultation and collaboration strategies35.

Third-Party service providers

Third-party service providers are essential in enhancing and sustaining the value chain of microgrids. They create the necessary external conditions and environment for the successful implementation of microgrid initiatives through various means, including policy development, financial assistance, risk management, and stakeholder coordination. Regarding policy development and market accessibility, the government facilitates microgrid projects by enacting and enforcing policies that foster microgrid growth, streamlining the approval process, and minimizing administrative expenses, thereby encouraging the initiation and advancement of these projects. In terms of regulatory frameworks and legal safeguards, the government is tasked with establishing and refining the laws and regulations governing new energy microgrids, offering legal protection for project development and operations, and ensuring that microgrid initiatives adhere to legal and regulatory standards. Concerning policy incentives and cost reduction, government subsidies can significantly lower the operational expenses of new energy microgrids, enhancing the economic viability of these projects and attracting greater investment and participation36. Furthermore, by providing low-cost financial support and investment incentives, the government and investors can draw more stakeholders into new energy microgrid projects, expand funding opportunities, and enhance the financial feasibility of these initiatives. Additionally, affordable financial support can assist project developers in diversifying investment risks and can be utilized for the expansion and upgrading of microgrids, thereby increasing system capacity and improving service quality. The government also assumes a supervisory and regulatory role in microgrid projects to ensure project quality and safety, thereby safeguarding consumer and public interests36,37.

Model construction

This study investigates the value co-creation process in new energy microgrids by developing models that analyze the roles and interactions of key stakeholders, including the main grid, distributed generation entities, and third-party service providers. The objective of these models is to identify the essential factors and mechanisms that enable the effective integration and internalization of resources, ultimately enhancing value for all participants involved. This theoretical framework aims to provide significant insights into the establishment of dynamic and collaborative ecosystems that support the sustainable development and economic viability of new energy microgrids, as illustrated in Fig. 4.

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
Full size image

New energy microgrid value co-creation theoretical model.

Study design

Data source

The selection of the 60 microgrid cases was conducted using a rigorous and transparent set of criteria to guarantee comparability and representativeness across the regions under study. Geographically, provinces such as Jiangsu, Shandong, Anhui, and Henan were selected due to their substantial investments in renewable energy infrastructure and their advanced microgrid development, making them exemplary representatives for analyzing value co-creation processes in technologically mature systems. The criterion of “similar annual sunshine hours” was operationally defined as a range of 1,200 to 1,500 h per year, ensuring that the chosen regions demonstrate comparable solar resource availability, which allows for consistent evaluation of distributed photovoltaic performance across the cases, as shown in Fig. 5. In terms of economic factors, “per capita GDP” was defined to encompass regions with values between $10,000 and $20,000 USD, reflecting a middle-income level that supports both public and private investments in microgrid technologies. This criterion guarantees that the selected cases are economically comparable while also capturing variations in local economic conditions that may influence stakeholder behavior. Additionally, policy-related considerations were integrated by selecting regions with similar regulatory frameworks and government incentives for renewable energy adoption, thereby controlling for policy-induced disparities among the cases. These clearly defined selection criteria ensure a balanced representation of geographic, economic, and policy-related factors, facilitating meaningful comparisons and enhancing the robustness of the study’s findings. 60 cases details as shown in Table 1.

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
Full size image

China’s annual sunshine hours (data from Solar GIS).

Table 1 60 cases detail.
Full size table

The primary metrics for the main grid, distributed energy generation, and third-party service support are obtained from the “Microgrid Operation Data 2021–2022” and the “State Grid Corporation of China Public Annual Report.” Considering that the different output indicators of value co-creation exhibit a specific cycle, the antecedent conditions are based on data from 2021, while the outcome variables utilize data from 2022. Detailed data sources are presented in Table 2.

Table 2 Selection of variables and data source.
Full size table

Research method

In 1987, Ragin developed Qualitative Comparative Analysis (QCA), a methodology that conceptualizes cases as holistic entities made up of various causal conditions, emphasizing the intricate causal relationships between configurations of conditions and their outcomes. QCA is categorized into three types based on the nature of the variables: crisp-set QCA (csQCA), multi-value QCA (mvQCA), and fuzzy-set QCA (fsQCA). While csQCA and mvQCA utilize crisp sets and truth tables, fsQCA significantly improves QCA’s capacity to analyze interval and ratio variables. Unlike its predecessors, fsQCA is capable of addressing both categorical and complex issues that involve varying degrees of membership. By converting fuzzy-set data into truth tables, fsQCA preserves the advantages of truth tables in managing qualitative data and streamlining configurations38. The distinctions among the three QCA types are summarized in Table 339.

Table 3 Three types of QCA.
Full size table

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is particularly effective for examining complex, small-sample, and multi-stakeholder systems, such as the microgrid cases explored in this research. Unlike conventional methods like Structural Equation Modeling (SEM) or regression analysis, which require large sample sizes and presume linear relationships among variables40,41,42, fsQCA is adept at uncovering intricate causal pathways and configurations in contexts with limited data points. Traditional methods often fail to account for the interrelatedness of various factors and the potential non-linear effects present in systems with diverse stakeholders, such as microgrids. In contrast, fsQCA enables researchers to capture the heterogeneity across cases while systematically investigating how combinations of conditions result in specific outcomes. Given the distinctive characteristics of China’s policy-driven microgrid ecosystems, where path dependency and institutional diversity complicate causal relationships, fsQCA serves as a robust framework for revealing the interactions among these factors in small, context-specific systems. This methodological approach not only overcomes the limitations of alternative techniques but also aligns with the study’s objective of understanding the dynamic interplay between policy, technology, and stakeholder behavior in complex energy systems.

The methodologies, mathematical frameworks, artifacts generated, and evaluation models utilized in this study are detailed in Table 435,43,44:

Table 4 Models and artifact of fsQCA.
Full size table

Variable measurement

Outcome variables

Value co-creation refers to a collaborative process where various stakeholders generate additional value through extensive communication and resource sharing. This process can yield both monetary and non-monetary benefits, including economic, social, and experiential value throughout the resource integration continuum. This study considers value co-creation as the dependent variable, utilizing provinces as the regional units of analysis. Economic value is represented by the profit generated from microgrid power generation per kWh, while social value is indicated by the microgrid abandonment rate. Drawing from existing literature, this research employs a more objective weighting approach—the coefficient of variation method—to assign weights to these two indicators, resulting in values of 42.51% for economic value and 57.49% for social value35. To address the issue of incommensurability amontg the evaluation indicators due to differing units and magnitudes, this study applies the extreme value processing method to normalize the indicators, ultimately deriving a comprehensive index for value co-creation.

Preconditions

  1. (1)

    Main Grid. As an essential component of value co-creation, the main grid supplies fundamental and critical input elements for this process. This study categorizes the main grid services into two types: grid access services and power consumption services offered by microgrids, based on theoretical analysis. The capacity of grid access services is quantified by the number of access points provided by the main grid, while the capacity of power consumption services is assessed by the ratio of distributed photovoltaic power consumption to the total cost of photovoltaic power consumption. Given that the input elements are evaluated using multiple indicators, the coefficient of variation method and extreme value processing technique are employed to ascertain the overall value of both services.

  2. (2)

    Distributed power generation entities serve as the final service providers within the power grid and can contribute various factors to enhance value co-creation. Based on prior research, the investment factors at the level of distributed power generation entities encompass investments in photovoltaic power station construction, maintenance, and energy storage. Specifically, construction investment is evaluated based on the unit price per kilowatt-hour for distributed photovoltaic installations; maintenance investment is represented by the maintenance cost per kilowatt-hour of distributed photovoltaic power generation, which is determined by the actual operation and maintenance unit price of the microgrid; and energy storage costs pertain to distributed photovoltaic power stations equipped with energy storage, measured by the unit price per kilowatt-hour of the microgrid for that particular year.

  3. (3)

    Third-party service entities. In addition to the essential components of the primary power grid and residential power generation, various third-party service entities, such as government agencies, venture capital firms, and industry investment funds, can also facilitate value co-creation within microgrids. This study focuses on government agencies and investment funds as the third-party service support entities. It identifies government policy subsidies (Policy and Subsidy) and financial support from investment funds (Financial Support) as key input factors for value co-creation. Specifically, policy subsidies from government agencies are assessed based on the active policies related to distributed power stations in each region as of 2022, along with the total amount of government financial subsidies received by these stations. The overall value is computed using the coefficient of variation method and extreme value processing. Conversely, financial support from investment funds is evaluated by the total amount of electricity generated by distributed power stations through various forms of investment and financing.

Results

Fuzzy set calibration

In fuzzy set qualitative comparative analysis (fsQCA), the term “fuzzy” pertains to the notion of fuzzy sets. Unlike conventional binary sets, fuzzy sets permit partial membership, allowing elements to belong to a set to varying degrees between 0 and 1. This characteristic offers a more adaptable approach to capturing the complexity and uncertainty inherent in real-world situations. The initial and critical step in applying fsQCA is Fuzzy Set Calibration, which converts raw data into fuzzy set membership scores that range from 0 to 1. This calibration process is foundational, setting the stage for subsequent truth table analysis and the identification of causal configurations. By means of calibration, researchers can transform intricate variables into a format amenable to fsQCA analysis, thereby uncovering the sufficient conditions that lead to specific outcomes.

In accordance with the operational standards of fsQCA, this study utilizes the most widely adopted calibration technique, namely the direct calibration method. The theoretical foundations were derived from Fiss’s research45, ensuring that the thresholds align with established frameworks and expectations for configurations associated with high performance. Empirical benchmarks, including quartiles and other statistical thresholds, were employed to operationalize these theoretical constructs based on the distribution of observed data. Variables such as “Differentiation” and “Low Cost” within the strategic dimension were calibrated using quartiles to reflect their relative standings within the dataset. In contrast, variables like “Formalization” and “Centralization” were guided by theoretical standards that define high and low levels of organizational structure. This comprehensive approach ensures that the calibration process is both theoretically informed and responsive to empirical data, enabling meaningful comparisons across cases while maintaining a robust analytical foundation. The quartile method is applied to calibrate both the antecedent conditions and outcome variables. The three calibration points—complete membership, crossover point, and complete non-membership—are designated as the upper quartile, median, and lower quartile of the case sample, respectively. The calibration specifics for the antecedent conditions and outcome variables are detailed in Table 5.

Table 5 Calibration of antecedent conditions and outcome variables.
Full size table

Univariate necessity analysis

Before conducting configuration analysis, this study used fsQCA3.0 MAC version to test whether a single antecedent condition constitutes a necessary condition for value co-creation. The operating system version is macOS15.1.1.

The results are shown in Table 6. It can be seen that the consistency of all antecedent conditions is less than 0.9, which does not constitute a necessary condition and needs to be combined with each other to affect the value co-creation of microgrid multi-agents.

Table 6 Necessity A analysis results of single antecedent condition.
Full size table

Configuration analysis

Test of sufficiency of conditional configuration

This study builds upon the work of Du Yunzhou et al. (2017)37, establishing an original consistency threshold of 0.8, a PR1 value consistency threshold of 0.7, and a case frequency of 1. Through the analysis of the truth table, we derive the simplified solution, intermediate solution, and complex solution. Additionally, we employ the nested relationship comparison method to identify the core conditions for each solution. Specifically, the conditions that are present in both the intermediate and simplified solutions are classified as core conditions, while those that appear solely in the intermediate solution are designated as marginal conditions.

Six pathways to achieve value co-creation are outlined as follows:

Configuration A: ~Hard service investment * ~Construction investment * ~Maintenance investment * Energy storage investment * Fund support;

Configuration B: Construction investment * maintenance investment * ~energy storage investment * ~government support * financial support;

Configuration C: construction investment * ~maintenance investment * ~energy storage investment * government support * financial support;

Configuration D: ~Construction investment * energy storage investment * government support * financial support;

Configuration E: hard service investment * construction investment * maintenance investment * energy storage investment * capital support;

Configuration F: hard service investment * construction investment * energy storage investment * government support * financial support.

Among the configurations analyzed, configurations B and E share core conditions, establishing them as a second-order equivalent configuration. The overall consistency of the six paths is 0.831, while the overall coverage rate is 0.723, both of which satisfy the criteria for Qualitative Comparative Analysis (QCA) research.

An examination of Table 7 yields the following conclusions: (1) Financial support from third-party service entities is more effective in facilitating value co-creation in new energy microgrids than hard service investments in the main power grid, regardless of the configuration. This finding corroborates the observation that capital investment in microgrids in China is expected to decrease in 2022, with rising capital costs primarily attributed to excessive returns in the real estate sector. (2) In most instances, hard service investments in the main power grid do not significantly influence the value co-creation of microgrids. This is largely due to the gradual reduction of government subsidies for new energy power generation—declining from 0.37 CNY/kWh in 2018 to 0.08 CNY/kWh in 2020, with subsidies eliminated entirely in 2021—and the decrease in on-grid electricity prices. Consequently, stakeholders are increasingly inclined to adopt self-consumption strategies, leading to a more rational approach to microgrid construction. (3) Investment during the construction phase impacts 66.67% of the configurations. Notably, photovoltaic modules represent nearly 50% of the initial construction investment. The disparity in construction costs is primarily attributed to the higher prices of modules with superior power generation efficiency, underscoring the critical importance of investing in photovoltaic modules and related infrastructure to enhance value co-creation.

Table 7 Configurations of multi-agent value co-creation in renewable energy microgrids.
Full size table

Confidence interval calculation and significance level test

Based on the Consistency and Raw Coverage data for each path presented in Table 7, the conventional fsQCA method is employed with the following parameters: Bootstrap resampling conducted 1,000 times, a significance level (α) set at 0.05, a consistency threshold of 0.8 (with core conditions requiring a minimum of 0.8), and a coverage threshold of 0.5 (indicating strong explanatory power). The 95% confidence intervals for the consistency and coverage of each path were determined through Bootstrap resampling, with the findings displayed in Table 8:

Table 8 Confidence interval of all configuration.
Full size table

The results of the calculations indicate that the lower limit of the consensus confidence interval for all configurations exceeds 0.8 (for instance, 0.895 and 0.958 for configuration A), suggesting that all paths satisfy the core condition requirements and exhibit high robustness. Configuration E demonstrates the highest consistency (0.973) along with the narrowest confidence interval (0.950 to 0.992), signifying its superior stability. Notably, only configuration E has a lower limit of the coverage confidence interval approaching 0.395, while all other configurations fall below 0.4, indicating an overall medium explanatory power; however, configuration E provides the most extensive coverage of the results.

In the significance level test (displacement test), 1000 sets of random data were generated using the Permutation Test, and the p-value for the consistency of each path was calculated, with the results presented in Table 9:

Table 9 Significance level test.
Full size table

As illustrated in Table 9, the p-values for all configurations are less than 0.05, suggesting that the consistency of each path is significantly greater than random probability, thereby rejecting the null hypothesis that posits “the combination of conditions is independent of the outcome.” Configuration E exhibited the smallest p-value (< 0.001), signifying the highest level of statistical significance.

In conclusion, the consensus confidence interval for all paths exceeds 0.8, fulfilling the essential condition requirements. Configurations E (consistency 0.973) and F (0.953) demonstrate the greatest robustness. All paths yielded significant p-values (P < 0.05), indicating that the combination of conditions has a statistically significant effect on the value co-creation of new energy microgrids. Configuration E, which includes hard service investment, construction investment, maintenance investment, energy storage investment, and financial support, represents the path with the strongest explanatory power (coverage 0.443). In contrast, Configuration A, which lacks hard service investment, construction investment, and maintenance investment but includes energy storage investment and financial support, has lower coverage (0.338) yet maintains high consistency, making it suitable for specific scenarios, particularly when policy support is inadequate.

Configuration E surpasses other configurations due to its well-rounded integration of essential components, including investments in energy storage, technological innovations, and alignment with policy frameworks. This configuration underscores the necessity of coordinated actions across various sectors, ensuring that energy storage investments are in harmony with overarching sustainability and efficiency objectives. For example, in China, the swift establishment of distributed photovoltaic power stations has underscored the vital role of energy storage systems in mitigating grid instability and enhancing resource optimization. By prioritizing these elements, Configuration E not only improves operational efficiency but also promotes long-term resilience and adaptability in microgrid systems.

Real-world instances further demonstrate the relevance of Configuration E. In areas such as Shandong, where the adoption of renewable energy is prevalent, the incorporation of advanced storage technologies has markedly enhanced grid stability and diminished dependence on fossil fuels. Additionally, policy frameworks that encourage collaborative initiatives among stakeholders have played a crucial role in facilitating successful microgrid deployments. These practical examples illustrate how Configuration E can act as a model for developing sustainable energy systems, effectively balancing technical, economic, and policy-related factors to optimize both efficiency and impact.

Configuration

Based on varying core conditions, the six configuration paths outlined in Table 6 reveal that configurations A, B, and E are primarily influenced by distributed generation entities, while configurations C and D are predominantly governed by third-party service entities. Configuration F, on the other hand, is characterized by the influence of all factors. This summary is visually represented in Fig. 6:

Fig. 6
Fig. 6The alternative text for this image may have been generated using AI.
Full size image

Microgrid multi-agent value co-creation model.

Based on the construction input (bi), maintenance input (si), and energy storage input (esi) factors associated with distributed power generation entities, along with the government support (gs) and financial support (fs) input factors from third-party service entities, various leading entities establish distinct pathways for value co-creation. In the distributed power generation-dominated model, construction, maintenance, and energy storage investments exhibit varying leading and auxiliary factors across different pathways. Conversely, in the third-party support service-dominated model, government support serves as the primary factor, while financial support acts as a supplementary factor. For the all-factor aggregation model, comprehensive coordination of all input factors, except for the maintenance input factor, is essential.

(1) High-quality construction types in which distributed power generation entities primarily invest during the initial phase include Configurations A, B, and E. The likelihood of path A is 0.928, accounting for 33.8% of the multi-agent value co-creation in new energy microgrids. Path B has a likelihood of 0.883, explaining 35.2% of the multi-agent value co-creation in this context. The likelihood of path E is 0.973, which elucidates 44.3% of the multi-agent value co-creation in new energy microgrids. These three paths highlight that the establishment and maintenance of distributed power generation entities, along with initial investments in energy storage, are essential conditions for facilitating value co-creation. Furthermore, they represent three critical financial and technical factors. This configuration indicates that new energy microgrids share similarities with traditional microgrids, and that high-quality infrastructure investment plays a crucial role in achieving cost-effective operations.

Strategic investment in construction can lower the overall system’s unit cost, as high-quality equipment typically offers a longer lifespan and enhanced power generation efficiency. By selecting cost-effective components and optimizing design, initial investment can be minimized, leading to reduced operational expenses. Implementing effective maintenance strategies can avert equipment failures and performance declines, thus decreasing downtime and repair costs. Emphasizing preventive maintenance and incorporating user-friendly designs can further lower long-term operating costs. Investment in energy storage is crucial for balancing supply and demand, diminishing reliance on costly peak-shaving power sources, and ultimately lowering electricity supply expenses. Efficient energy storage systems can enhance the operation of microgrids and boost the economic viability of energy, particularly for renewable photovoltaic power generation, which is characterized by variable power output46.

Distributed power generation entities, in conjunction with emerging technologies and products, have established an effective pathway for value co-creation. In the three identified configurations, third-party financial support plays a secondary role across all pathways, highlighting the excessive financing costs associated with China’s new energy microgrids.

(2) Policy and financial support primarily reliant on third-party services: Configurations C and D. The likelihood of Configuration C’s occurrence is 0.846, accounting for 36% of the multi-agent value co-creation within new energy microgrids. Meanwhile, Configuration D has a probability of 0.901, explaining 26.1% of the multi-agent value co-creation in this context. Within these two pathways, government policy support, kilowatt-hour electricity subsidies, and affordable financial assistance emerge as critical conditions for facilitating value co-creation, significantly influencing economic enhancement, sustainable development, and social impact.

Government policies facilitate the realization of value co-creation for new energy microgrids through three primary avenues: Market Access: The government can enhance market accessibility for new energy microgrids by implementing supportive policies, streamlining the approval process, and minimizing administrative expenses. Regulatory Framework: It is essential to establish and refine laws and regulations governing new energy microgrids to ensure legal protection for project development and operation. Standard Formulation: The development of technical standards and interface specifications is crucial to promote compatibility and interconnection among microgrid systems.

The kWh subsidy contributes to the value co-creation of new energy microgrids in three significant ways: Cost Reduction: The kWh subsidy directly lowers the operating costs associated with new energy microgrids, thereby enhancing the project’s economic viability. Investment Incentives: Subsidy policies can motivate a greater number of investors to engage in new energy microgrid projects, thereby expanding funding opportunities. Technology Development: Subsidies can facilitate the research, development, and application of innovative technologies, fostering the advancement and enhancement of new energy microgrid technology.

Affordable financial assistance facilitates the value co-creation of new energy microgrids through three key dimensions: Financing expenses: Affordable financial support can substantially lower the financing expenses associated with microgrid projects, enhancing their financial viability. Risk mitigation: Financial backing from governmental bodies or financial institutions aids project developers in diversifying their investment risks. Project enhancement: Cost-effective funding can be utilized to expand and upgrade microgrids, thereby increasing system capacity and enhancing service quality.

The dependence on financial support as a common element across various configurations emphasizes its vital role in promoting microgrid development, while also revealing significant limitations that require further investigation. Although financial assistance can initiate and facilitate projects, this reliance creates vulnerabilities linked to regional economic disparities and policy fluctuations. For example, regions with restricted access to subsidies or grants may find it challenging to secure the necessary investment for effective microgrid implementation, thereby perpetuating existing inequalities in energy infrastructure development. Furthermore, dependence on external funding poses risks related to policy changes, as alterations in government priorities or budget allocations can disrupt project timelines and sustainability.

Real-world instances highlight these challenges. In China, despite national policies driving considerable investments in renewable energy, regional disparities remain stark. Provinces with strong economic foundations and advantageous subsidy programs frequently advance more rapidly than their less affluent counterparts, widening gaps in energy access and infrastructure quality. These examples underscore the necessity for a more nuanced strategy that balances financial support with localized solutions tailored to specific contexts, thereby reducing reliance on external funding while promoting long-term resilience and equity.

(3) The all-factor aggregation configuration F, with a probability of 0.953, accounts for 26.4% of the value co-creation in new energy microgrid multi-agent systems. This configuration encompasses all factors except for the maintenance investments made by distributed power generation entities. Key components include the hard investments from the main grid entity, as well as the construction and energy storage investments from distributed power generation entities, alongside government and financial support from third-party service providers. The primary contributors are the construction and energy storage investments of distributed power generation entities, in addition to governmental policy support and per-kilowatt-hour electricity subsidies. Following this analysis of the input factors related to distributed power generation and third-party service entities, we will now examine the input factors pertinent to the main grid entity.

For the main grid entity, ensuring effective connectivity between the microgrid and the main grid is crucial for achieving energy complementarity and optimal configuration. Enhancing the stability and reliability of this grid connection can be accomplished through technical advancements and management strategies. Furthermore, optimizing the power absorption ratio of the microgrid is essential for balancing self-consumption and grid-connected power, thereby mitigating issues related to the curtailment of wind and solar energy. Additionally, reducing absorption costs and enhancing economic benefits can be achieved through the implementation of an intelligent dispatching system.

The key to the all-factor aggregation model lies in the collaborative coordination of multiple input factors. To facilitate value co-creation, six coordination strategies are implemented:

  1. 1.

    **Multi-party Consultation**: A consultation mechanism is established that includes the primary grid company, distributed power generation entities, government agencies, and financial institutions to ensure balanced interests among all stakeholders.

  2. 2.

    **Policy Utilization**: Conduct thorough research and analysis of various policies to develop microgrid strategies that align with policy directions.

  3. 3.

    **Technological Innovation**: Employ advanced power and information technologies to enhance the intelligence of microgrids and optimize resource allocation.

  4. 4.

    **Cost Control**: Implement meticulous management practices to regulate construction and operational costs, thereby improving the project’s economic efficiency.

  5. 5.

    **Risk Management**: Create a risk assessment and management framework to address potential risks, including policy changes and market fluctuations.

  6. 6.

    **Benefit Sharing**: Develop a fair benefit distribution mechanism to ensure that all participating entities receive equitable returns from microgrid initiatives.

Through a comparative analysis of the three distinct configurations of value co-creation, it becomes evident that each configuration in the development and operation of microgrids involves different stakeholders and focal points. The high-quality construction configuration emphasizes the initial investments made by distributed power generation entities, which encompass aspects such as construction, maintenance, and energy storage. In contrast, the policy and financial support configuration centers on the services provided by government entities and financial institutions. The all-factor aggregation configuration represents a holistic model that integrates all critical elements and promotes multi-party collaboration.

The methods for achieving value co-creation vary among these configurations. High-quality construction aims to lower costs by enhancing construction and operational efficiencies, while policy and financial support seeks to mitigate financing costs and risks through government initiatives, subsidies, and funding. The all-factor aggregation configuration fosters value co-creation through collaborative strategies and multi-party discussions.

Risk and cost management approaches also differ across the configurations. High-quality construction tends to prioritize risk prevention and cost control through the use of superior equipment and technology. Conversely, policy and financial support may focus more on managing risks associated with policy shifts and market volatility. The all-factor aggregation configuration necessitates a more thorough risk assessment and management framework.

In terms of benefit distribution, the all-factor aggregation configuration places significant emphasis on designing mechanisms for equitable benefit sharing, ensuring that all participating entities receive fair returns, a consideration that is less pronounced in the other two configurations.

Finally, the complexity of coordination strategies varies. The all-factor aggregation configuration demands intricate negotiation and coordination strategies among multiple parties to facilitate effective collaboration, whereas the other two configurations may concentrate more on the investment and support related to specific aspects or a limited number of areas.

Despite the differences among the three configurations, their objective of value co-creation remains consistent. Each configuration seeks to facilitate value co-creation within microgrids through various approaches, ultimately aiming to enhance the economic viability and sustainability of the overall system via collaboration and investment. All configurations underscore the critical role of policy and financial backing. Whether concerning initial construction investments or third-party services, such support is essential for the successful implementation of microgrid projects. Government policy initiatives and per-kilowatt-hour subsidies have been instrumental in lowering costs, encouraging investment, and fostering technological advancements across all configurations. Furthermore, the significance of technological innovation and optimized design is highlighted in each configuration. Technology is pivotal in enhancing the performance of microgrids, whether in the establishment of distributed power generation entities or in their integration with the main power grid. In practical applications, the value co-creation of new energy microgrids must integrate the characteristics of these three configurations, employing a comprehensive strategy tailored to achieve optimal economic benefits and sustainability, taking into account specific geographical, economic, and social contexts, as well as the unique requirements of each project.

The impact of these approaches on the actual implementation and stakeholders in the field of new energy microgrids is shown in Table 10:

Table 10 The impact matrix on practical implementation and stakeholder.
Full size table

In conclusion, the successful execution of these value co-creation pathways necessitates the collective efforts and collaboration of policymakers, project developers, technology providers, investors, and operators. By engaging in these initiatives, significant economic advantages and sustainable development can be achieved in the realm of new energy microgrids.

Conclusions and implications

This research employs the fsQCA methodology to examine 60 cases of photovoltaic microgrids in China, identifying three pathways for value co-creation in new energy microgrids: third-party service support-led, distributed generation-led, and comprehensive factor aggregation. The findings underscore the critical role of policy support, per-kilowatt-hour subsidies, and financial assistance, highlighting how the reduction of initial capital expenditures can enhance operational sustainability. The practical implications of this study are significant:

  1. 1)

    Multi-actor collaboration perspective: This approach transcends the constraints of single-actor analysis, offering a holistic analytical framework for value co-creation in the microgrid sector through the lens of multi-actor interaction and collaboration.

  2. 2)

    Integration of theory and practice: This study delivers both theoretical foundations and practical insights for the sustainable and high-quality advancement of microgrids, facilitating the effective implementation and enhancement of new energy microgrid systems.

  3. 3)

    Reference for policy development: The findings of this research can serve as a basis for decision-making among policymakers, particularly regarding policy support, subsidy frameworks, and funding distribution.

  4. 4)

    Optimization of operational costs: This section outlines targeted strategies aimed at minimizing initial capital expenditures to enhance operational sustainability, which holds significant implications for microgrid operators. It is advisable for publicly listed companies to consider issuing project bonds in the capital market to alleviate the burden of initial investment costs.

  5. 5)

    Tools for analyzing complex systems: The fsQCA method introduces a novel research instrument for comprehending and examining the configurational effects within complex network systems. This approach aids in elucidating the mechanisms through which various factor combinations influence value co-creation.

  6. 6)

    Cross-disciplinary applicability: The insights and methodologies derived from this research can be applied to various domains, including energy management, environmental policy, and sustainable development studies, thereby demonstrating substantial interdisciplinary relevance.

The findings of this study, although valuable within the context of China, are limited by regional constraints that must be acknowledged. The research is centered on a specific set of conditions and policy environments that are characteristic of China, which may not be directly applicable to other global settings. Therefore, the generalizability of the conclusions should be approached with caution. While the pathways and strategies identified may provide useful insights for regions with similar economic structures or policy frameworks, their relevance will depend on the distinct socio-economic, regulatory, and cultural factors in those areas. To improve the contextual applicability of these findings, future research should investigate how these strategies can be tailored to various contexts, considering differences in infrastructure development, market dynamics, and policy priorities.

Second, while the study emphasizes the pathways for implementing value co-creation, it does not thoroughly explore the dynamic changes and long-term effects associated with this process. Subsequent studies could investigate the long-term impacts and the mechanisms of dynamic evolution in value co-creation.

Future research in multi-agent value co-creation within new energy microgrids should focus on specific, actionable questions that address particular contexts and mechanisms. For example, scholars might investigate how regulatory frameworks in liberalized energy markets, such as those in Germany, affect the development and execution of value co-creation strategies. This research could yield valuable insights into how policy environments influence stakeholder collaboration and innovation in microgrid initiatives. Additionally, a vital area for exploration is the impact of digital technologies, such as blockchain, in fostering trust and coordination among various stakeholders. By tackling these targeted inquiries, future studies can provide more precise recommendations for policymakers, practitioners, and researchers seeking to enhance value co-creation in evolving energy systems.