📈 economics

Research on Influencing Factors and Action Paths of Virtual Power Plant Pricing Mechanisms

This study empirically identifies consumer behavior, energy costs, market supply and demand, and environmental policies as the core factors influencing virtual power plant pricing mechanisms, demonstrating their significant positive impact on distributed energy promotion and efficiency while highlighting the regulatory roles of policy support and technology compatibility.

Original authors: Xiaojie Li

Published 2026-10-22

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Xiaojie Li

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The modern power grid is a delicate balance sheet, constantly matching the electricity people need with the electricity the sun and wind can provide. As societies shift toward renewable energy, a new challenge has emerged: these clean sources are often scattered across thousands of rooftops and fields, making them difficult to manage as a single, reliable force. To solve this, engineers have developed the concept of a virtual power plant. Imagine a digital manager that does not generate power itself but instead connects thousands of small, scattered energy sources—like home solar panels, battery storage units, and even flexible appliances—into one coordinated team. This virtual team can act like a traditional power plant, responding to the grid's needs in real time. However, for this system to work, the people and companies involved must agree on a price. If the rules for paying and charging are unclear or unfair, the system stalls. The central question becomes: what actually drives the price in this virtual marketplace, and how do those prices influence the success of the green energy transition?

A recent study by researcher Xiaojie Li tackles these questions by looking directly at the human and economic factors behind virtual power plant pricing. Instead of relying solely on computer simulations or theoretical models, the researcher gathered real-world insights from 285 people, including energy industry professionals, researchers, and ordinary residents. After screening for validity, 278 responses were used for the final analysis. They asked these participants to rate their views on various factors, such as how much energy costs, how people use electricity, and how government rules affect the market. By analyzing these responses with statistical tools, the researcher mapped out the invisible pathways that connect these different elements. They wanted to see which factors truly move the needle on pricing and whether those prices successfully encourage more people to join the green energy movement.

The investigation revealed that four main forces shape the price of electricity in a virtual power plant. The most powerful driver is the cost of energy itself. This includes the price of buying electricity, the cost of storing it in batteries, and any government subsidies involved. When these costs rise or fall, the price charged by the virtual power plant adjusts accordingly. The second most significant factor is environmental policy. Government rules and support for renewable energy act as a strong guide, pushing prices in a direction that favors green solutions. The third factor is how people behave. When consumers have specific habits or high demand during peak times, it creates pressure that influences the price. Finally, the balance of supply and demand in the broader market plays a role, though the study found it to be slightly less influential than costs or policies. The researcher found that these factors do not work in isolation; they interact to create a pricing structure that must be both fair to the operators and attractive to the users.

Crucially, the study confirmed that getting the pricing right has a direct and measurable impact on the future of energy. When the pricing mechanism is well-designed, it significantly boosts the willingness of people to connect their distributed energy sources, like rooftop solar, to the virtual grid. It also leads to better overall energy efficiency, meaning less waste and smarter use of resources. The data showed that for every improvement in how the pricing system works, the adoption of distributed energy and the efficiency of the system both rise noticeably. This suggests that the financial rules are not just administrative details but are the engine that drives the entire transition toward cleaner energy.

The research also uncovered how different conditions can strengthen or weaken these relationships. For instance, when government support is strong and clear, the impact of energy costs on pricing becomes even more pronounced, helping to stabilize the market. Conversely, when the technology connecting these systems is highly compatible and seamless, the wild swings in consumer behavior have less of a disruptive effect on the price. This means that investing in better technology and clearer policies can smooth out the bumps in the road, making the virtual power plant more reliable. The study also noted that different groups of people react differently to these factors. Energy industry professionals are most sensitive to cost changes, while researchers focus heavily on environmental policies, and ordinary residential users tend to be the most cautious about the pricing mechanisms themselves.

In light of these findings, the paper offers a clear path forward for improving how virtual power plants operate. It suggests that pricing should be built on a solid foundation of actual costs, with a flexible component that adjusts based on the type of energy and market conditions. To make this work, the study recommends specific policy actions, such as providing substantial subsidies for the cost of energy storage and offering small price reductions for users who connect their distributed energy systems. Furthermore, it emphasizes the need to improve the technology that links these systems together, ensuring that the connection between the virtual plant and the user's equipment is smooth and reliable. By aligning the financial incentives with the technical capabilities and policy goals, the virtual power plant can move from a promising concept to a practical, efficient reality that helps solve the energy challenges of the future.

Technical Summary: Research on Influencing Factors and Action Paths of Virtual Power Plant Pricing Mechanisms

Problem Statement
As the global energy sector advances toward "dual carbon" goals, Virtual Power Plants (VPPs) have emerged as a critical mechanism for integrating distributed energy resources (DERs) such as solar and wind. However, the current pricing mechanisms for VPPs in China remain underdeveloped. Existing literature predominantly focuses on theoretical model construction (e.g., game theory or cost-sharing models) and lacks empirical validation based on micro-level survey data. Furthermore, current research often fails to account for the synergistic effects of multiple dimensions—specifically the interplay between policy, technology, market dynamics, and consumer behavior—or to analyze how these factors vary across different business models. This gap hinders the optimization of VPP pricing strategies, affecting investment enthusiasm, grid stability, and the efficiency of energy resource allocation.

Methodology
To address these gaps, this study employs a quantitative approach combining questionnaire surveys with advanced statistical modeling:

Data Collection: A structured questionnaire was distributed via online (WeChat, industry communities) and offline channels to energy practitioners, researchers, and ordinary users. From 300 distributed questionnaires, 285 were recovered, yielding an effective recovery rate of 95% as reported in the abstract. After further validity screening (excluding samples with filling time < 60 seconds and homogeneous answers), 278 valid questionnaires were retained for the final analysis.
Instrument Design: The survey utilized a 5-point Likert scale to measure core latent variables: Consumer Behavior, Energy Costs, Market Supply and Demand, Environmental Policies, Pricing Mechanism, Promotion of Distributed Energy, and Improvement of Energy Efficiency. Regulatory variables included Policy Support Intensity, Information Transparency, and Technology Compatibility.
Statistical Analysis:
- SPSS: Used for reliability testing (Cronbach's α), validity testing (KMO and Bartlett's spherical test), descriptive statistics, and Pearson correlation analysis.
- AMOS (Structural Equation Modeling - SEM): Constructed a theoretical model linking influencing factors to pricing mechanisms and subsequent outcomes. The model underwent modification based on the Modification Index (MI) to improve fit indices (e.g., adding error term correlations).
- Heterogeneity Analysis: One-way ANOVA was conducted to examine differences in perceptions across occupational groups.

Key Contributions
This paper contributes to the VPP literature by shifting from purely theoretical modeling to empirical validation within the context of China's power market.

Empirical Validation: It provides systematic testing of pricing influencing factors using micro-survey data, addressing the scarcity of empirical research in this domain.
Path Analysis: It utilizes SEM to reveal the internal structural relationships and action paths between pricing factors and energy transition goals, overcoming the limitations of traditional regression analysis in handling latent variables and indirect effects.
Regulatory Mechanisms: The study identifies and quantifies the regulatory roles of policy support intensity and technology compatibility, offering a nuanced view of how external variables moderate core pricing paths.

Results
The structural equation model confirmed all research hypotheses with a good model fit ( $\chi^2/df = 2.18$ , RMSEA = 0.065). Key findings include:

Core Influencing Factors: Four factors significantly impact the VPP pricing mechanism, ranked by path coefficient:
1. Energy Costs ( $\beta = 0.41$ ): The most significant driver, encompassing power prices, subsidies, and storage costs.
2. Environmental Policies ( $\beta = 0.35$ ): Government support and regulations significantly guide pricing strategies.
3. Consumer Behavior ( $\beta = 0.32$ ): Demand volume and consumption habits directly affect dispatching costs.
4. Market Supply and Demand ( $\beta = 0.28$ ): Imbalances in the market drive price fluctuations.
Impact on Outcomes: A scientific pricing mechanism has a significant positive driving effect on:
- The promotion of distributed energy ( $\beta = 0.48$ ).
- The improvement of energy efficiency ( $\beta = 0.43$ ).
Regulatory Effects:
- Policy Support Intensity positively regulates the relationship between energy costs and pricing ( $\beta = 0.18$ ), amplifying the cost impact.
- Technology Compatibility negatively regulates the relationship between consumer behavior and pricing ( $\beta = -0.15$ ), effectively dampening the volatility caused by user behavior.
Heterogeneity: Significant differences exist among occupational groups. Energy industry practitioners are most sensitive to energy costs, while researchers prioritize environmental policies. Ordinary residential users show the lowest acceptance of current pricing mechanisms.

Significance and Claims
The paper claims that its findings provide empirical support for refining VPP pricing policies to better align with China's energy transition goals. By identifying "Energy Costs" and "Environmental Policies" as the primary levers, the study suggests that pricing mechanisms must prioritize cost recovery while simultaneously responding to policy orientations. The research concludes that improving the scientificity of pricing mechanisms is a key starting point for promoting distributed energy and enhancing overall energy efficiency. Consequently, the study offers a new analytical perspective for policymakers and industry stakeholders to design differentiated pricing strategies that balance the interests of operators, grid companies, and users, thereby facilitating the coordinated development of virtual power plants and the power market.

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