Review Reports
by
- Cristian Valeriu Stanciu * and
- Narcis Eduard Mitu
Reviewer 1: Junjie Zhong Reviewer 2: Anonymous Reviewer 3: Anonymous
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe manuscript titled " Price Behavior and Market Integration in European Union Electricity Markets: A VECM Analysis" examines EU electricity markets using a Vector Error Correction Model (VECM) to explore regional clustering, price behavior, and market integration. The study is relevant and offers meaningful insights into electricity market dynamics. However, the following issues should be addressed:
1) While the background and motivation are clear, the specific research gaps remain vague. Clearly stating how this study builds on and extends prior work on electricity market integration would enhance its significance.
2) The use of Dynamic Time Warping (DTW) is a strong point, but the rationale behind merging small clusters and excluding countries like Luxembourg needs more justification. A discussion on how these decisions affect the robustness of results is recommended.
3) The analysis is comprehensive, but explanations of cluster-specific dynamics (e.g., why Block 1 adjusts rapidly while Block 4 lags) require more depth. Linking these trends to infrastructure or market policies would improve clarity.
4) The manuscript cites relevant literature but misses some recent studies, particularly from the past two years. Adding references to recent works will strengthen the positioning of this study: https://ieeexplore.ieee.org/abstract/document/10061341
5) The study lacks an evaluation of the model's robustness under different scenarios, such as varying energy prices or renewable energy levels. Including sensitivity tests would strengthen the conclusions.
6) Some sentences are overly complex. For example, "The integration of EU electricity markets is a complex and multidimensional process shaped by various factors..." could be simplified for better readability.
7) Formulas and figures should be more standardized to conform to the journal's formatting
8) Please talk more future chances and challenges.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study provides a systematic analysis of the integration and price dynamics within European Union electricity markets, employing a Vector Error Correction Model (VECM). The research topic is both compelling and relevant, addressing a significant area of interest in the field. However, the manuscript, in its current form, may face challenges in gaining acceptance. Below are some constructive suggestions for improvement:
1. The methodology used to determine the cluster composition of European electricity markets requires further clarification. What specific criteria or processes were employed in defining these clusters? Providing a detailed explanation will strengthen the study’s analytical rigor.
2.The rationale for initially determining the optimal cluster count as nine and subsequently merging it into seven warrants additional explanation. Is there a theoretical or practical basis for this approach? Furthermore, would directly clustering into seven have yielded comparable results? Addressing these questions could enhance the theoretical foundation of the study.
3.Some sections of the manuscript could benefit from more concise writing. For instance, Table 3 appears to combine only one additional cluster compared to Table 2, while Table 4 presents largely redundant information except for the varying number of countries. Revising these tables to reduce redundancy and improve clarity is recommended.
4. The evaluation of the relationship between day-ahead electricity prices and wholesale markets overlooks the influence of key factors, such as energy prices and strategic interactions between power plants and users. For further insights, the authors could reference studies such as:“Bridging electricity market and carbon emission market through electric vehicles:Optimal bidding strategy for distribution system operators to explore economic feasibility…”“Optimal bidding and coordinating strategy for maximal marginal revenue due to V2G operation: Distribution system operator as a key player in China's uncertain electricity markets.”
5.While the evaluation of regional power interdependence is comprehensive, the study should elaborate on its broader implications. Given the natural monopoly of electricity and its dependence on physical infrastructure, consumers are typically constrained to local electricity markets. How does this research contribute to advancing understanding or addressing challenges within this context? Providing a clearer statement of the study’s significance would enhance its impact.
By addressing these suggestions, the authors can strengthen the manuscript’s quality and ensure it aligns with the expectations of the target audience.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThis manuscript examines the integration and price behavior of European Union electricity markets using a Vector Error Correction Model (VECM). The research identifies seven regional clusters, revealing strong long-term equilibrium relationships and interconnected short-term adjustments, with Germany and France playing pivotal roles in regional stability. The findings highlight persistent regional disparities, emphasizing the need for targeted cross-border trading policies and infrastructure investments to enhance market efficiency and resilience. The topic is interesting and timely, with rich content. My comments are as follows:
1. In the literature review, the authors have reviewed a substantial number of regression models used to analyze the dynamic relationships between electricity prices and other external variables, including ARIMA, VAR, and VECM. However, in recent years, many studies have employed machine learning, reinforcement learning, time series generative adversarial networks, and even the latest large language models to analyze spot electricity prices. It is recommended that the authors incorporate these advancements to enhance and update the literature review.
2. Therefore, the authors should also address in the literature review why traditional statistical methods were chosen over machine learning approaches.
3. In line 238, "Due to data unavailability and particular price formation, 3 countries were excluded," the term "particular price formation" requires further explanation from the authors to clarify what specific aspects of these countries' price formation mechanisms led to their exclusion.
4. In line 262-264, The merging of clusters in this case may appear overly subjective. Simply stating that clusters consisting of only one country (clusters 4, 6, and 9) were merged to enhance the robustness of the analysis does not seem like a strong enough reason. It might be more appropriate to justify the merger based on factors such as geographical proximity or a high share of renewable energy generation. The authors should carefully evaluate whether this merging is reasonable.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsAccept
Author Response
Comment: Accept.
Response: Thank you for your insightful review and for accepting our paper. We are grateful for your support and for recognizing the contributions of our study to the understanding of electricity market integration in the European Union.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper can be accepted now.
Author Response
Comments: The paper can be accepted now.
Response: Thank you for your insightful review and for accepting our paper. We are grateful for your support and for recognizing the contributions of our study to the understanding of electricity market integration in the European Union.
Reviewer 3 Report
Comments and Suggestions for AuthorsThank you for the author's revisions. The current version effectively addresses my previous concerns. However, I have found an incorrect citation in the references. In line 131:"Similarly, LLMs like GPT-4 have shown promise in integrating unstructured data, such as policy reports and news, into predictive models [23,34]."However, the citation [23] does not actually involve the use of LLMs. I suggest citing the recent paper "Large Language Model-based Bidding Behavior Agent and Market Sentiment Agent-Assisted Electricity Price Prediction", published in IEEE Transactions on Energy Markets, Policy, and Regulation. This paper discusses the application of LLMs in electricity price forecasting and would be a more relevant reference.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf