Master–Slave Game Pricing Strategy of Time-of-Use Electricity Price of Electricity Retailers Considering Users’ Electricity Utility and Satisfaction

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The novelty if very poor. the paper like a poor report. There is no extensive model or results developed in this paper. 

Author Response

Point 1: What is the novelty of this paper? What is the difference from the existing studies?

Response 1:

This paper lists a large number of relevant literatures and analyzes their shortcomings one by one.In view of the above problems, this paper introduces electricity utility and elec-tricity satisfaction, constructs the comprehensive benefit function of electricity retail-ers and users, and uses the master-slave game to simulate the interaction between the two parties. Both parties aim at maximizing benefits. As the leader, the electricity re-tailer first publishes the price, and as the follower, the user responds by changing the electricity consumption. Finally, a time-of-use electricity price pricing strategy model based on the master-slave game is established, and the inverse induction method is used to solve the problem. The results of the example show that the model can effec-tively optimize the electricity selling price of the electricity selling company and the load curve of the user, and can achieve a win-win benefit for both parties. The main contributions of this paper are summarized as follows :

(1)    The master-slave game model is introduced into the time-of-use electricity price pricing strategy, and the user response ability is mobilized in the game process to achieve a win-win situation for both parties.

(2)    From the perspective of the user side, most of the existing articles only consider the user cost, including the electricity cost to the satisfaction cost. In fact, the user 's electricity utility can be considered as the user 's income when using electricity.

Therefore,the user benefit function is studied, and the user 's electricity utility is intro-duced as the user 's income, and the user 's electricity purchase cost and satis-faction cost are taken as the user 's expenditure.

(3)    When studying the time-of-use electricity price optimization mechanism based on the master-slave game model, the existing articles fail to fully consider that the user demand response capability is restricted by many factors.

Therefore,three different electricity price mechanisms, fixed electricity price, peak-valley time-of-use electricity price and 24-hour time-of-use electricity price, are set up to study the impact of different electricity price flexibility on the benefits of both parties under the game model.

(4)    Moreover,three different types of users, including residential users, industrial users and commercial users, are set up to study the impact of different user response capabilities on the benefits of both parties under the game model.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents a study on Master-slave game pricing strategy of time-of-use electricity price of electricity retailers considering users ' electricity utility and satisfaction, the paper fits the scope, the topic is good and interesting, here are some comments:

-add a highlight section where you mention your main findings and contribution

-the abstract is nicely written however add some numerical findings

-the intro is short, it's better to add a literature review section where you discuss recent studies and their approaches and identify research gaps and how are you gonna deal with them in this paper

-it's better at the end of your intro/literature review section, to add a paragraph illustrating the flow of rest of the paper so you guide readers of your paper well

-page 6 has some Chinese writing check that

-Table 2 first column isn't clear

-it's better to follow the norm in organizing your paper: intro, literature review, methodology, results and discussions, conclusion

-add a comparison between your method and the recent literature methods

-add limitations and future studies

-add DOI to all references for easy locating

-add specific numerical results to your conclusion

-check all writings 

 

Author Response

Point 1: add a highlight section where you mention your main findings and contribution

Response 1:

Regarding the findings and contributions of this article, we have added a highlighting section in the introduction and named it ' Contribution '.

this paper introduces electricity utility and electricity satisfaction, constructs the com-prehensive benefit function of electricity retailers and users, and uses the master-slave game to simulate the interaction between the two parties. Both parties aim at maxim-izing benefits. As the leader, the electricity retailer first publishes the price, and as the follower, the user responds by changing the electricity consumption. Finally, a time-of-use electricity price pricing strategy model based on the master-slave game is established, and the inverse induction method is used to solve the problem. The results of the example show that the model can effectively optimize the electricity selling price of the electricity selling company and the load curve of the user, and can achieve a win-win benefit for both parties. The main contributions of this paper are summa-rized as follows :

(1)    The master-slave game model is introduced into the time-of-use electricity price pricing strategy, and the user response ability is mobilized in the game process to achieve a win-win situation for both parties.

(2)    The user benefit function is studied, and the user 's electricity utility is intro-duced as the user 's income, and the user 's electricity purchase cost and satis-faction cost are taken as the user 's expenditure.

(3)    Three different electricity price mechanisms, fixed electricity price, peak-valley time-of-use electricity price and 24-hour time-of-use electricity price, are set up to study the impact of different electricity price flexibility on the benefits of both parties under the game model.

(4)    Three different types of users, including residential users, industrial users and commercial users, are set up to study the impact of different user response capabilities on the benefits of both parties under the game model.

Point 2: the abstract is nicely written however add some numerical findings

Response 2:

In this paper, by setting examples and analysis results, it is concluded that the time-of-use electricity price optimization strategy based on the master-slave game model can mobilize the potential of user demand response and achieve a win-win situation for e-commerce and users. In the abstract part, we add specific numbers to support the conclusion.

Point 3: add a literature review section where you discuss recent studies and their approaches and identify research gaps and how are you gonna deal with them in this paper

Response 3:

Added reference studies are as follows:

Based on the background of smart grid, the literature [6] developed a new energy management system based on time-of-use electricity price for household users, which effectively improved the energy utilization rate.

In Reference [7], the optimal operation model of microgrid alliance, shared energy storage and active distribution network is established. Through the cyclic iteration among the three participants, the final operation scheme and dynamic time-of-use price of the distribution network are formulated to effectively promote the consumption of renewable energy.

 In Reference [8], the time-of-use electricity price is combined with the optimal operation problem of the smart microgrid, and the time-of-use electricity price is implemented under the conditions of renewable energy and incentive-based demand response to reduce the operating cost of the smart microgrid.

Reference [9] describes the user 's demand response behavior through reinforcement learning and optimizes the user 's load curve under the time-of-use electricity price mechanism.

In Reference [10], a time-of-use pricing model considering the uncertainty of wind power is proposed. The optimal solution set is obtained by using NSGA-II multi-objective optimization algorithm with the target of peak-valley difference of equivalent net load and user dissatisfaction. At the same time, it ensures the user 's electricity comfort and the role of peak shaving and valley filling.

Reference [11] studied the dynamic period division of time-of-use electricity price mechanism and explored the potential of demand-side response.

In Reference [17], the game theory method is applied to the time-of-use pricing mechanism of energy, and the game pricing process for renewable energy is mainly studied.

Reference [18] established the Ai pricing model, described the pricing problem of generative AI as a game between two companies, and studied the benefit space between competitors under the AI pricing model.

Reference [23] constructed a seasonal time-of-use electricity price optimization model, which effectively reduced the peak-valley difference and mobilized the potential of user demand response.

In the introduction, we add ' Literature Review ' and analyze the shortcomings of each article. In addition, it also analyzes the overall shortcomings of the existing literature and puts forward the innovation points of this paper.At rhe same time,we add a comparison between our method and the recent literature methods.

At the end of the introduction, we add the ' Contribution ' section to illustrate the innovation and contribution of this paper, and introduce the structure of this article in detail.

Point 4: it's better at the end of your intro/literature review section, to add a paragraph illustrating the flow of rest of the paper so you guide readers of your paper well

Response 4:

In the last paragraph of the introduction, we introduce the structure and work of this paper in detail, as follows:

The rest of this paper is organized as follows : In the second section, the compre-hensive benefit function of both the electricity retailer and the user is analyzed and es-tablished. In the third section, the time-of-use electricity price pricing strategy based on the master-slave game model is constructed. The fourth section analyzes the solu-tion process of the game model. The fifth section verifies the effectiveness of the method through an example and analyzes that the benefits of both parties are restrict-ed by the flexibility of electricity price and the user 's response ability under the game model. Finally, the conclusion of this paper is put forward in the sixth section.

Point 5: Chinese writing check and table check

We checked and optimized the English expression of the full text, and checked all the variable names and chart information to confirm that it is correct.

Point 6: add DOI to all references for easy locating

We have added all the DOI information we can find. But there are one doctoral dissertation and conference papers,we can not find DOI.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

1. It is recommended to supplement the references of recent years to understand the latest research status at home and abroad.
2. It is suggested to check the grammar and sentences of the article, and uniformly standardize the letter forms of the same variable.
3. It is recommended to correct the "Error! Source not found" in the article. And uniformly modify the format of the references.
4. It is proposed that the model should consider the uncertainties such as the randomness of new energy power generation to make the model more in line with reality.

Comments on the Quality of English Language

It is suggested to check the grammar and sentences of the article, and uniformly standardize the letter forms of the same variable.

Author Response

Response 1:

Added reference studies are as follows:

Based on the background of smart grid, the literature [6] developed a new energy management system based on time-of-use electricity price for household users, which effectively improved the energy utilization rate.

In Reference [7], the optimal operation model of microgrid alliance, shared energy storage and active distribution network is established. Through the cyclic iteration among the three participants, the final operation scheme and dynamic time-of-use price of the distribution network are formulated to effectively promote the consumption of renewable energy.

 In Reference [8], the time-of-use electricity price is combined with the optimal operation problem of the smart microgrid, and the time-of-use electricity price is implemented under the conditions of renewable energy and incentive-based demand response to reduce the operating cost of the smart microgrid.

Reference [9] describes the user 's demand response behavior through reinforcement learning and optimizes the user 's load curve under the time-of-use electricity price mechanism.

In Reference [10], a time-of-use pricing model considering the uncertainty of wind power is proposed. The optimal solution set is obtained by using NSGA-II multi-objective optimization algorithm with the target of peak-valley difference of equivalent net load and user dissatisfaction. At the same time, it ensures the user 's electricity comfort and the role of peak shaving and valley filling.

Reference [11] studied the dynamic period division of time-of-use electricity price mechanism and explored the potential of demand-side response.

In Reference [17], the game theory method is applied to the time-of-use pricing mechanism of energy, and the game pricing process for renewable energy is mainly studied.

Reference [18] established the Ai pricing model, described the pricing problem of generative AI as a game between two companies, and studied the benefit space between competitors under the AI pricing model.

Reference [23] constructed a seasonal time-of-use electricity price optimization model, which effectively reduced the peak-valley difference and mobilized the potential of user demand response.

These articles involve smart grid operation optimization, new energy pricing and AI pricing. They are relatively new, progressing and innovative in the field of time-of-use electricity price.

 

Point 2: check the grammar and sentences of the article, and uniformly standardize the letter forms of the same variable.

Response 2:Agree

We checked and optimized the English expression of the full text, and checked all the variable names and chart information to confirm that it is correct.

Point 3: correct the "Error! Source not found" in the article. And uniformly modify the format of the references.

Response 3: Agree

All have been modified.

Point 4: consider the uncertainties such as the randomness of new energy power generation to make the model more in line with reality.

Response 4:

The research content of this paper is to focus on the interactive behavior of e-commerce and users in the electricity retail market, and use the master-slave game method to simulate the decision-making process of both parties, so as to mobilize the regulatory characteristics of users and maximize the profits of both parties. In this paper, two examples are set up to study the impact of electricity price flexibility and user elasticity on the benefits of both parties.

At the same time, we agree that the impact of energy uncertainty on demand response potential should also be taken into account. However, due to the limited supporting data and information obtained in this article, we put it in the last part of the article as an improvement of the article.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

1. The abstract is also unclear on assumptions within the modeling.

2. While time-of-use pricing is a concept with applicability, additional detail on methodology, testing, and real-world implementation are necessary to address possible gaps.

3. The introduction is based on out-of-date sources, which do not reflect the latest developments in electricity prices, demand response, and smart grid technology.

4. Nowadays, AI-based pricing, decentralized energy, and renewable integration are very important; these need to be covered in the paper.

5. There should be a new literature review to help the paper seize the latest changes in the domain.

Comments on the Quality of English Language

It can be revised by an English expert.

Author Response

Point 1: The abstract is also unclear on assumptions within the modeling.

Response 1:

This paper modifies the abstract, and explains the problems, methods and models proposed in this paper, and the conclusions drawn are clear. The revised summary is as follows :

With the establishment of a competitive electricity retail market, how to optimize the retail electricity price mechanism has become the core of all kinds of retail companies to explore. Aiming at the pricing problem of time-of-use electricity price, this paper proposes a pricing strategy based on master-slave game model. Firstly, considering the user 's electricity utility and satisfaction factors, the comprehensive benefit function of the electricity selling company with electricity price as the decision variable and the user 's comprehensive benefit function with electricity consumption as the decision variable are established respectively. Then, a master-slave game model is established with the electricity selling company as the leader and the user as the follower, and the reverse induction method is used to solve the model. Finally, considering the influencing factors of user response ability, different electricity price types and user types are set up for simulation. The results show that the revenue of electricity retailers can be increased by up to 170,000 yuan, and the average electricity price of users can be reduced by up to 8 yuan. It is verified that the model can effectively achieve a win-win situation for both sides and promote peak shaving and valley filling. At the same time, it is proved that the role of the model is posi-tively related to electricity price flexibility and user response.

Point 2: additional detail on methodology, testing, and real-world implementation are necessary to address possible gaps.

Response 2:Agree

In the introduction part, this paper lists a large number of latest research and progress in the field of time-of-use electricity price, and analyzes their shortcomings in detail, and puts forward the innovation points and structure of this paper.

Point 3: add literature review and reflect the latest developments in electricity prices, demand response, smart grid technology,AI-based pricing, decentralized energy, and renewable integration

Response 3: Agree

Added reference studies are as follows:

Based on the background of smart grid, the literature [6] developed a new energy management system based on time-of-use electricity price for household users, which effectively improved the energy utilization rate.

In Reference [7], the optimal operation model of microgrid alliance, shared energy storage and active distribution network is established. Through the cyclic iteration among the three participants, the final operation scheme and dynamic time-of-use price of the distribution network are formulated to effectively promote the consumption of renewable energy.

 In Reference [8], the time-of-use electricity price is combined with the optimal operation problem of the smart microgrid, and the time-of-use electricity price is implemented under the conditions of renewable energy and incentive-based demand response to reduce the operating cost of the smart microgrid.

Reference [9] describes the user 's demand response behavior through reinforcement learning and optimizes the user 's load curve under the time-of-use electricity price mechanism.

In Reference [10], a time-of-use pricing model considering the uncertainty of wind power is proposed. The optimal solution set is obtained by using NSGA-II multi-objective optimization algorithm with the target of peak-valley difference of equivalent net load and user dissatisfaction. At the same time, it ensures the user 's electricity comfort and the role of peak shaving and valley filling.

Reference [11] studied the dynamic period division of time-of-use electricity price mechanism and explored the potential of demand-side response.

In Reference [17], the game theory method is applied to the time-of-use pricing mechanism of energy, and the game pricing process for renewable energy is mainly studied.

Reference [18] established the AI pricing model, described the pricing problem of generative AI as a game between two companies, and studied the benefit space between competitors under the AI pricing model.

Reference [23] constructed a seasonal time-of-use electricity price optimization model, which effectively reduced the peak-valley difference and mobilized the potential of user demand response.

These articles involve smart grid operation optimization, new energy pricing and AI pricing. They are relatively new, progressing and innovative in the field of time-of-use electricity price.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

1. please use USD instead of Yuan in the whole paper.
2. Provide a more in-depth comparison of this strategy compared to other strategies considering nonparametric statistical analysis, both time and memory usage besides accuracy.

3. A detailed graphical abstract is required.

Author Response

Dear Reviewer,

Thank you for giving us the opportunity to review our paper " Master-slave game pricing strategy of time-of-use electricity price of electricity retailers considering users ' electricity utility and satisfaction " and your recognition of our work. We are very grateful to the reviewers for their valuable comments and suggestions on our paper, which are of great help to our paper improvement. We have modified our paper according to the reviewer's comments, and used word's revision mode in the revised manuscript to record our deletion. We have also carried out grammar checks and revisions to our papers to improve their readability and accuracy. We hope the revised paper would satisfy you. Below are our point-by-point responses to reviewer comments:

Point 1: please use USD instead of Yuan in the whole paper.

Response 1:

We appreciate the reviewer's suggestion to use USD as the currency unit. However, after careful consideration, we have retained the original unit yuan in this manuscript for the following reasons:

1. Localized Data Source: All case study data are derived from a specific province in China, and using yuan directly reflects the regional context of the analysis.
2. Consistency in Visualization: Converting the units to USD would obscure the comparative trends in Figures, as the original values are tightly clustered within the Chinese economic scale.
3. Journal Policy Compliance: We confirmed that Sustainability does not mandate the use of USD (as stated in its author guidelines), and multiple published papers in this journal retain local currency units for region-specific studies.

In addition, when we quote some data in the conclusion, we use million yuan as a unit to avoid excessive figures.

To enhance international readability, we have added USD equivalents in parentheses for key monetary values in the text.

Point 2: Provide a more in-depth comparison of this strategy compared to other strategies considering nonparametric statistical analysis, both time and memory usage besides accuracy.

Response 2: In the conclusion of this paper, we enumerate the influence of considering the type of electricity price and the type of user on the profit of the electricity supplier and the user under the model, which is not compared in the previous papers.

Point 3: A detailed graphical abstract is required.

Response 3: We added a detailed graphic summary and attached it at the end of the article.

With best regards,

Sincerely Yours,

Corresponding author:

Name: Xue Cui

E-mail: xue_cui_whu@163.com

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper has improved well. Thanks and good luck

Comments on the Quality of English Language

No issues detected 

Author Response

Response to Reviewer 2 Comments

Dear Reviewer,

Thank you for giving us the opportunity to review our paper " Master-slave game pricing strategy of time-of-use electricity price of electricity retailers considering users ' electricity utility and satisfaction " and your recognition of our work. We are very grateful to the reviewers for their valuable comments and suggestions on our paper, which are of great help to our paper improvement. We have modified our paper according to the reviewer's comments, and used word's revision mode in the revised manuscript to record our deletion. We have also carried out grammar checks and revisions to our papers to improve their readability and accuracy. We hope the revised paper would satisfy you. Below are our point-by-point responses to reviewer comments:

Thank you for your comments, this revision we made improvements to the paper in the following areas:

1.In the conclusion of this paper, we enumerate the influence of considering the type of electricity price and the type of user on the profit of the electricity supplier and the user under the model, which is not compared in the previous papers.

2.We added a detailed graphic summary and attached it at the end of the article.

With best regards,

Sincerely Yours,

Corresponding author:

Name: Xue Cui

E-mail: xue_cui_whu@163.com

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Accept

Comments on the Quality of English Language

It can be polished again.

Author Response

Response to Reviewer 4 Comments

Dear Reviewer,

Thank you for giving us the opportunity to review our paper " Master-slave game pricing strategy of time-of-use electricity price of electricity retailers considering users ' electricity utility and satisfaction " and your recognition of our work. We are very grateful to the reviewers for their valuable comments and suggestions on our paper, which are of great help to our paper improvement. We have modified our paper according to the reviewer's comments, and used word's revision mode in the revised manuscript to record our deletion. We have also carried out grammar checks and revisions to our papers to improve their readability and accuracy. We hope the revised paper would satisfy you. Below are our point-by-point responses to reviewer comments:

Thank you for your comments, this revision we made improvements to the paper in the following areas:

1.In the conclusion of this paper, we enumerate the influence of considering the type of electricity price and the type of user on the profit of the electricity supplier and the user under the model, which is not compared in the previous papers.

2.We added a detailed graphic summary and attached it at the end of the article.

With best regards,

Sincerely Yours,

Corresponding author:

Name: Xue Cui

E-mail: xue_cui_whu@163.com

Author Response File: Author Response.pdf