As renewable energy continues to expand rapidly, China has implemented a series of policies aimed at facilitating the integration of renewable energy, promoting the reform of the new electricity market (Cheng et al., 2023; Zhao et al., 2023). These policies are designed to promote equitable market transactions, enhance demand-side responsiveness, and support the growth of electricity retailers. One such policy is the Double Listing and Double Delisting model (DLDD), introduced by the electricity trading center to foster a competitive, open, and orderly market structure. Under this model, electricity buyers (including electricity retailers and traditional grid companies) and sellers (power plants) participate simultaneously in the listing process, with both parties able to submit offers at any time based on market transaction volumes and price fluctuations, engaging in multiple rounds of listing and delisting until the process concludes. Compared to traditional bilateral and listing-based transactions, the DLDD model shifts the trading process from a one-time decision to multiple transactions. During the trading period, information such as electricity prices and quantities transitions from being relatively closed to being publicly disclosed in real time. This model minimizes in-person interactions between traders, thereby enhancing transparency, reducing information costs, and improving both fairness and efficiency in the trading process.

In 2015, the Chinese government launched a new round of electricity market reforms. This reform introduced the distribution and retail markets into the existing electricity framework, bringing significant changes to the sector (Zheng et al., 2021). As part of this reform, electricity retailers emerged as new market participants, distinct from traditional grid companies in several ways. First, electricity retailers offer greater flexibility as intermediaries in the energy market (Dimitriadis et al., 2021; Mathieu et al., 2019; Ramos, 2020). They purchase electricity from producers on the wholesale market and sell it to end users. With the rapid expansion of renewable energy, the issue of effectively incentivizing demand-side responsiveness has become critical (Cabot and Villavicencio, 2024; Gils, 2014; Siano, 2014). Demand-side incentives, which are essential policy tools, encourage consumers, including electricity retailers, to adjust their consumption patterns to align with supply conditions. Electricity retailers’ flexibility make them a crucial role in Demand-side responsiveness. Their role involves navigating the electricity market by participating in both medium- and long-term contracts and spot market transactions to secure competitive prices. At the same time, they implement demand-side incentives to modify consumer behavior, aligning consumption with fluctuating supply conditions and promoting more efficient electricity use through strategic pricing mechanisms (Ghazvini et al., 2015; Guo and Weeks, 2022).

Second, as a new market participant, compared to traditional grid companies, electricity retailers often have weaker bargaining power when negotiating with power plants. This disadvantage is due to factors such as the established market presence of power plants and the smaller operational scale of most electricity retailers. As a result, they may struggle to secure favorable prices in transactions. Balancing profitability with delivering value to consumers while operating in a market where they have the lower hand remains a fundamental challenge for electricity retailers (Correa-Giraldo et al., 2021; Defeuilley, 2009; Haar, 2021).

Given the flexibility of electricity retailers, which supports demand-side responsiveness, and their relative lack of bargaining power in the electricity market, studying how to enhance their bargaining position is crucial. Improving the equity bargaining power of electricity retailers not only facilitates their survival and development as equitable market participants but also significantly promotes demand-side responsiveness and the integration of renewable energy. This study focuses on the transaction prices of electricity retailers and explore how these retailers can strengthen their market position and address the disadvantages they face in an increasingly competitive environment. However, research in this area is hindered by a lack of comprehensive empirical data, as much of the existing literature is based on theoretical models with limited real-world validation (Cui et al., 2021; Nojavan and Zare, 2018).

This study examines the effects of Double Listing and Double Delisting (DLDD) policy and the resource aggregation capabilities of electricity retailers, specifically regarding their ability to aggregate user demand, on their bargaining power. The findings reveal that, contrary to traditional expectations, resource aggregation alone does not significantly improve the bargaining power of electricity retailers. Instead, the DLDD trading model plays a more pivotal role by introducing a structured and transparent trading process. This model mitigates the inherent disadvantages electricity retailers face when negotiating with power plants. These insights highlight the critical role of well-designed trading policies in fostering a more equitable and competitive electricity market.

This study makes contributions to the literature on electricity market by providing new insights into the effectiveness of trading policies. It challenges the conventional belief that resource aggregation is sufficient to improve the profitability (Gao et al., 2021). Instead, the study emphasizes the importance of a competitive trading environment in determining market success. The practical implications of this study are equally important for policymakers and industry stakeholders. The findings highlight the necessity of implementing trading policies, such as the DLDD model, to help new market participants such as electricity retailers, to overcome the disadvantages they face, thereby enabling them to secure more favorable transaction prices. It offers valuable guidance for policymakers on structuring demand-side incentives that balance environmental and economic goals.

Under the fixed-effects model in Table 2, column (1) shows that the variable Retailer has a significant positive effect on Price (Retailer, β ₁ = 22.709, p < 0.01), indicating that electricity retailers tend to face higher transaction prices. In column (4), the DLDD policy demonstrates a negative moderating effect on the relationship between Retailer and Price (Retailer*DLDD, β ₄ = -18.620, p < 0.01), suggesting that DLDD helps mitigate the pricing disadvantage experienced by electricity retailers. However, the interaction term between Volume and Retailer (Retailer* Volume, β ₅ = -0.083, p > 0.10) is not significant. The results across multiple models are consistent. Compared to traditional power grid companies, electricity retailers are at a disadvantage in the electricity trading market, struggling to negotiate lower prices with power plants. While the DLDD policy effectively reduces this disadvantage, resource aggregation does not provide a similar benefit in alleviating the pricing challenges faced by these companies.

Additionally, we controlled for the fixed effects of the Electricity Buyers as a robustness check. The results are shown in Table 3. The main effect of the Retailer is omitted by the model. The results confirm that the interaction term Retailer*DLDD remains significantly (Retailer*DLDD, β ₄ = -19.747, p < 0.01), while Retailer*Volume continues to be insignificant (Retailer* Volume, β ₅ = -0.018, p > 0.10), thereby supporting the consistency of our findings.

To further examine the robustness of our findings and rule out potential confounding factors, we conducted a falsification test. There are two potential mechanisms through which DLDD could lead to the significant negative interaction effect (Retailer*DLDD). The DLDD policy introduces a multi-round bidding mechanism for both buyers and sellers, which suggests that one possible explanation for the regression results is that the negative interaction between DLDD and Retailer may not be driven by the policy itself (Mechanism 1), but rather by the increasing experience of electricity retailers (Mechanism 2), which could gradually reduce their bidding disadvantage. To eliminate the potential confounding effect of Mechanism 2, we employed a falsification test and introduced an additional variable, After, into the regression model. This variable indicates whether the electricity buyer has experienced the DLDD policy, coded as one if the buyer participated in DLDD either in the current or previous transactions, and 0 otherwise. The interaction term After * Retailer thus captures transactions involving electricity retailers after they have experienced DLDD, including both DLDD and other transaction models. If the improvement in the retailer’s position is due to increased trading experience, the effect should remain consistent and progressively reduce their disadvantage. In this case, the interaction term After*Retailer should be significantly negative. This would imply that DLDD provided the retailer with an opportunity to gain trading experience. After participating in a DLDD transaction, the retailer would accumulate trading experience, allowing them to significantly lower prices in subsequent transactions, regardless of whether the transactions are under the DLDD model or other trading models.

The regression results are presented in columns (1), (2), and (3) of Table 4. Column (1) shows the main effect of After, while column (2) tests the interaction between After and Retailer (After*Retailer), and column (3) tests whether the effect of Retailer*DLDD, remains significant. The interaction term After*Retailer is not significant in either column (2) or (3), while the interaction term Retailer*DLDD remains significantly negative (−21.243, p < 0.01) in column (3). This suggests that electricity retailers did not gain a bidding advantage simply through increased experience with DLDD, meaning that Mechanism two does not hold. Instead, the DLDD bidding mechanism itself provided a more equitable environment, reducing the bargaining disadvantage and enabling electricity retailers to secure more favorable pricing. This supports our hypothesis, where Mechanism 1—the DLDD policy itself—is the primary factor driving these results.

The evolving landscape of electricity trading, particularly with the integration of renewable energy sources, has introduced new challenges and opportunities for electricity retailers. This study examines the impact of new electricity policies and resource aggregation on these companies within the context of medium- and long-term electricity trading. Our results show that in the Chinese electricity trading market, electricity retailers, as new entrants, are at a disadvantage when purchasing electricity from power plants compared to traditional power grid companies. This disadvantage cannot be mitigated through resource aggregation alone. However, the DLDD trading model has shown to assist these new market entrants in improving their bargaining power. Robustness checks consistently support these findings. These conclusions offer valuable insights for electricity market reform, particularly in promoting demand-side responsiveness. They also provide implications for designing policies that support new entrants in competitive markets, ensuring equality and inclusion.

First, it shifts the focus from the traditional supply-side perspective, which has dominated the literature (Mahmoudi-Kohan et al., 2010; Prado and Qiao, 2018; Yang et al., 2016), specifically electricity retailers in demand side. By analyzing their bargaining power and pricing outcomes, this study offers new insights into how these market participants, which are becoming increasingly important in modern electricity markets, can navigate evolving market structures. By strategically managing factors such as timing and transaction size (list and delist bids multiple times in DLDD), electricity retailers can improve their bargaining power and profitability. These findings offer a roadmap for new market entrants seeking to enhance their operations in an increasingly competitive market environment.

Second, the study critically evaluates the role of resource aggregation in enhancing the market position of electricity retailers. Contrary to conventional wisdom that resource aggregation inherently improves bargaining power (Gao et al., 2021), this study demonstrates that aggregation alone is insufficient. Instead, it highlights the pivotal role of policy mechanisms, particularly the Double Listing and Double Delisting (DLDD) trading model, in leveling the playing field for electricity retailers. The introduction of a more structured and transparent trading framework proves to be a crucial factor in improving transaction prices and reducing the dominance of traditional power plants.

Third, this study advances the discourse on demand-side incentives by highlighting how trading mechanisms shape competitiveness and transparency. While prior research has emphasized that aggregation enables economies of scale, our findings demonstrate that supportive market designs—particularly the DLDD model—can further strengthen demand-side responsiveness by reducing information acquisition costs for new entrants and improving the fairness of participation. Building on this insight, this study provides actionable implications for policymakers: (1) Extending the DLDD mechanism to a broader range of transaction types, including intra-provincial contracts and renewable energy trading, where enhanced price discovery would be especially beneficial. (2) Enhancing platform-level information disclosure, such as publishing historical aggregated bidding ranges, round-by-round delisting outcomes, and acceptance ratios, to further reduce information asymmetry and support more informed bidding strategies. (3) Developing targeted training and support programs for new and smaller retailers to help them better interpret multi-round bidding signals, improve their aggregation strategies, and strengthen their competitive position within the DLDD framework. (4) Improving digital infrastructure and data analytics capabilities on trading platforms. Investing in advanced analytics, real-time monitoring tools, and user-friendly interfaces could help retailers lower participation costs, reduce misinformed bidding decisions, and promote more equitable access to market opportunities.

In summary, this study contributes to the academic literature by providing a more nuanced understanding of the interplay between policy mechanisms, resource aggregation, and market outcomes for electricity retailers. It also offers practical implications for policymakers and industry stakeholders, emphasizing the need for well-designed trading policies that support both market efficiency and the integration of renewable energy.

While this study provides valuable insights, it is not without limitations. One limitation is the reliance on a specific dataset covering a single cross-provincial trading center from November 2021 to November 2023. Cross-provincial transactions, while highly market-oriented, do not fully capture intra-provincial trading or transactions involving diverse energy sources. Future research could expand this analysis to include a broader range of transactions, such as those involving different types of energy sources or other regional markets, to assess the generalizability of the findings. Although transaction volume serves as the most feasible proxy for resource aggregation given the structure of the cross-provincial trading platform, it may not reflect all underlying aspects of retailers’ aggregation strategies. Future research would benefit from incorporating more granular operational or contractual information to construct richer measures of aggregation capability. Additionally, while this study controls for various factors that could influence transaction prices, there may be other unobserved variables, such as the specific contractual terms or the financial health of the participating retailers, that might affect the results. Future research could incorporate these additional variables to develop a more comprehensive understanding of the factors influencing transaction outcomes. Finally, although we have attempted to explore the mechanism of DLDD’s impact, due to data and methodological limitations, we are unable to determine its long-term effects. During the period of our study, DLDD showed significant effects. Compared to the influence of trading experience, the impact of DLDD itself is likely to be more enduring.