Analysis of Optimal Operation of Multi-Energy Alliance Based on Multi-Scale Dynamic Cost Equilibrium Allocation

Round 1

Reviewer 1 Report

   1. Comprehensive proof read is essential throughout the manuscript as there are a lot of typo/grammatical errors.

  2.When the cost of wind power is the largest (57.44), then corresponding costs of photovoltaic, hydropower and thermal power are the minimum and output ratio of wind power is relatively large (0.6 and 0.5) -why?

    3. In text, it is mentioned that hydropower cost is the maximum (22.56), but in table pumped storage is mentioned. It is suggested to use the same name in text as well as table.

    4. Table 3 is smaller than the value of the corresponding column of the matrix (23) not clearly understandable.

    5.  In section 5.4, “Calculate its cost changes, as shown in Table 6. It can be seen from Table 6 that the cost ………..” but in paper Table 6 is not there.

    6.  In the conclusion section, it is better to include research contributions, research limitations, and future works.

Author Response

Dear Reviewers:

we are very grateful to you for allowing us to revise our manuscript. we appreciate you very much for your positive and constructive comments and suggestions on our manuscript entitled “Analysis of optimal operation of multi energy alliance based on multi-scale dynamic cost equilibrium allocation”

We have studied the reviewers’ comments carefully and tried our best to revise our manuscript according to the comments. The following are the responses and revisions I have made in response to the reviewers' questions and suggestions on an item-by-item basis. Thanks again for the hard work of the editor and reviewer!

 

1. Comprehensive proof read is essential throughout the manuscript as there are a lot of typo/grammatical errors

Response: Thanks for your suggestion. We have proofread the manuscript thoroughly. Corresponding modifications were made for word and grammar errors.

 

2.When the cost of wind power is the largest (57.44), then corresponding costs of photovoltaic, hydropower and thermal power are the minimum and output ratio of wind power is relatively large (0.6 and 0.5) -why?

Response: Thanks a lot to the reviewer for reminding. Three different combinations are described here (Please see Page 14, lines 454-458). It is not a description of a group of solutions. The first combination is the first column of Table 1, where the wind power cost is the largest and the corresponding output ratio is 0.3. The second combination is the sixth column of Table 1, with a minimum hydropower cost of 0.00 and a relatively large wind power output ratio of 0.6. The third combination is the last column of Table 1, the thermal power cost minimum is 0.01, and the corresponding wind power output ratio is relatively large to 0.5.

 

 

3. In text, it is mentioned that hydropower cost is the maximum (22.56), but in table pumped storage is mentioned. It is suggested to use the same name in text as well as table.

Response: Thank you for your advice. This paper studies four subjects including pumped storage, so all references to hydropower refer to pumped storage in this paper. For ease of reading, we will refer to pumped storage as hydropower for short.

 

4. Table 3 is smaller than the value of the corresponding column of the matrix (23) not clearly understandable.

Response: Thanks a lot to reviewer for reminding us. We have a statement error here, the matrix (23) should be changed to the matrix (27). The matrix (27) is the cost value of each entity when it operates independently. Table 3 is the cost extremum and a corresponding cost matrix of each entity under the operation of the multi-energy alliance. When comparing the matrix (27) with all the combinations in Table 3, it is found that the combinations in Table 3 are smaller than those in the matrix (27). That is, when the maximum cost of each subject is less than the cost value of each subject operating independently. It shows that the other combination schemes of joint operation are less than the cost of independent operation. Therefore, the mechanism of joint operation of multiple energy sources is superior to the independent operation of various energy sources.

 

5. In section 5.4, “Calculate its cost changes, as shown in Table 6. It can be seen from Table 6 that the cost ………..” but in paper Table 6 is not there.

Response: Thanks to the reviewer for reminding us. Here we have a misrepresentation. In the process of modification, we deleted the chart, resulting in a misalignment of serial numbers. Table 6 is not present in the paper, and should be replaced with Table 3.

 

6. In the conclusion section, it is better to include research contributions, research limitations, and future works.

Response: In the conclusion part, we add the limitations of this study and future work (Please see Page 21, lines 654-664). There are two main aspects: (1) Considering the overall power shortage mode of the system, the energy supply and demand are balanced. This method affects the accuracy of each subject cost calculation results to some extent. Considering the time-varying characteristics of the load and meeting the real-time power demand, the accurate calculation of the output allocation and dynamic cost allocation of the main bodies of the alliance is focused. (2) In this paper, the internal collaborative optimization operation mechanism of the alliance is summarized through the actual case data change rules. However, the differentiation and robustness of corresponding data changes under different load peak-valley difference operation modes need to be further studied. To further clarify the general applicability of this study program.

Meanwhile, the research contribution of this paper is in the introduction part of the article (Please see Page 4, lines 140-182 ). The main reason for not reflecting on the research contribution of this paper in the conclusion part is to compare the research contribution of this paper with the existing representative literature. Reflect on the differences between this article and the existing representative literature and the novelty of this article.

 

In addition to the above changes, we also made the following changes:

1. Summary section

In the summary section, we added the research methods used in this paper and the results of this study and related data.

 

2. Three articles were added and the way in which they are cited in this paper changed (reviewers 1, 3)

We have added three references in the References:

[14]Wang Yongli, Liu Zhen, et al. Research on the optimization method of integrated energy system operation with multi-subject game[J]. Energy,2022,245.

[15]Zhou Yifan, Hu Wei, et al. Compensation Pricing and Benefit Distribution Method of Cogeneration Participating in Peak Regulation of Power Grid [J]. Proceedings of the CSEE, 2019,39(18).

[16]Wei Chun,Shen Zhuzheng et al. An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining[J]. Applied Energy,2021,295.

Meanwhile, the main contents of the three kinds of literature are described in the introduction (Please see page 3, lines 102-11). The three works of literature introduce three different distribution methods: nucleolus method, Shapley and Nash equilibrium.

 

3. Add research deficiencies in existing literature and academic contributions to this paper in the introduction

In the problematic part of the existing research, the analysis of three allocation methods (Please see page 3, lines 117-127) was added to reflect the problems of different allocation methods. At the same time, academic contribution has increased. This paper proposes a solution to the shortcomings of existing allocation methods (Please see page 4, lines 140-147). That is, the discrete coefficient of cost-sharing value and its median index are introduced as the basis for judging the willingness of different subjects to participate and the stability of the alliance. The overall equilibrium effect of the coalition is studied to determine the power generation, power generation ratio and optimal power generation combination scheme of each agent under multi-spatio-temporal scales.

 

4.Added new model equations and corresponding simulation data

The new model equation is on page 9 in Section 4.3. To validate the novelty of this work, it is convenient to systematically evaluate the effect of different combinations of cost allocation equalization and optimal combination schemes. We choose to construct a cost equilibrium value discrete coefficient equation (see page 9, section 4.3: Multi-energy coalition Shapley cost allocation equilibrium effect and balanced combination test) to assess the overall stability of the coalition and the acceptance of the coalition 's cost equilibrium allocation effect by each agent. ​Based on the median equation, the median values of the discrete coefficients under various combinations are calculated. The discrete coefficient value centering scheme is used as a reference for the multi-scale optimal combination strategy for each agent in the alliance. Considering the influence of different combination cost-sharing balances, a high proportion of new energy power generation and the stability of the alliance, the optimal combination scheme of the alliance is determined. The corresponding analysis results and related data are on page 18, section 5.5, lines 548-584.

 

5. The Comprehensive discussion modifies some of the content

The (4) and (5) of the comprehensive discussion are modified as follows(Please see page 20, lines 614-630):

(4) There are differences between the research results based on hypotheses 1 through 4 and their actual application, primarily in the following areas: Firstly, the overall power shortage model based on the system directly affects the accuracy of the cost calculation results of each subject. That is, the output distribution and cost variation of each subject based on real-time power demand are different from this study. Secondly, the effect of the primary frequency regulation function of the photovoltaic or wind power generation system on the cost-sharing and the change in output ratio of each main body during actual operation has not been taken into account. Thirdly, the power generation cost and auxiliary service income of each main body have not been combined for the calculation to optimize the alliance's operation strategy.

        (5) The case data in this paper are 47 component cost data based on the load peak and valley periods in Assumption 3. It provides a research paradigm for the selection of a multi-energy alliance combination optimization mode. During the real-time electricity market transaction, the multi-energy alliance can be simulated to balance the 24 point dynamic load demand values, and the dynamic equilibrium cost sharing can be calculated to determine the generation power, generation ratio and optimal generation portfolio scheme under the multi-space-time scales of each main body.

Best regards.

Yong Cui.

Author Response File: Author Response.pdf

Reviewer 2 Report

The objective of the article seems to be good and essential. However, there are some concerns the authors may focus as follows:

1. Provide the numerical results from the research outcomes in the abstract.

2. Propose the methodology adapted for this study in the abstract.

3. Citations are not formatted correctly and not in order.

4. References may be increased to support the outcomes of the article.

5. It is strongly advised to compare the existing approaches with suggested one in this article to validate the novelty of the work.

Author Response

Dear Reviewers:

we are very grateful to you for allowing us to revise our manuscript. we appreciate you very much for your positive and constructive comments and suggestions on our manuscript entitled “Analysis of optimal operation of multi energy alliance based on multi-scale dynamic cost equilibrium allocation”

We have studied the reviewers’ comments carefully and tried our best to revise our manuscript according to the comments. The following are the responses and revisions I have made in response to the reviewers' questions and suggestions on an item-by-item basis. Thanks again for the hard work of the editor and reviewer!

 

1. Provide the numerical results from the research outcomes in the abstract.

Response: Thanks a lot for your suggestion, we add the research results and related data in the abstract ( Please see Page 1, lines 20-26 ).

 

2. Propose the methodology adapted for this study in the abstract.

Response: Thank you for your suggestions, we elaborated on the research methods used in this paper in more detail in the abstract part ( Please see Page 1, lines 12-20 ).

 

3. Citations are not formatted correctly and not in order.

Response: We have made a comprehensive review of the introduction and revised the citations ( Please see Page 1, lines 32-139 ).

 

4. References may be increased to support the outcomes of the article.

Response: We have added three references in the References:

[14]Wang Yongli, Liu Zhen, et al. Research on the optimization method of integrated energy system operation with multi-subject game[J]. Energy,2022,245.

[15]Zhou Yifan, Hu Wei, et al. Compensation Pricing and Benefit Distribution Method of Cogeneration Participating in Peak Regulation of Power Grid [J]. Proceedings of the CSEE, 2019,39(18).

[16]Wei Chun,Shen Zhuzheng et al. An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining[J]. Applied Energy,2021,295.

Meanwhile, the main contents of the three kinds of literature are described in the introduction (Please see Table 3, line 102-11). The three literatures introduce three different distribution methods: nucleolus method, Shapley and Nash equilibrium. This paper compares the advantages and disadvantages of the three methods, analyzes the shortcomings of the nucleolus method and the Nash equilibrium to reflect the superiority of Shapley in cost or profit distribution, and introduces the cost-sharing value dispersion coefficient and its median index as different subjects. The willingness to participate and the stability of the alliance are judged to support the results studied in this paper.

 

5. It is strongly advised to compare the existing approaches with suggested one in this article to validate the novelty of the work.

Response: Thanks a lot for your suggestions, it's interesting to compare the existing approach with the one used in this article. Firstly, we add three representative works of literature [14-16] which are highly relevant to the research method of this paper to the part of references. In addition, the three methods are compared in the problematic parts of existing literature research, and the shortcomings of existing methods are analyzed (Please see 4, lines 104-107).

​Finally, to validate the novelty of this work, it is convenient to systematically evaluate the effect of different combinations of cost allocation equalization and optimal combination schemes. We choose to construct a cost equilibrium value discrete coefficient equation (see page 9, section 4.3: Multi-energy coalition Shapley cost allocation equilibrium effect and balanced combination test) to assess the overall stability of the coalition and the acceptance of the coalition 's cost equilibrium allocation effect by each agent. ​Based on the median equation, the median values of the discrete coefficients under various combinations are calculated. The discrete coefficient value centering scheme is used as a reference for the multi-scale optimal combination strategy for each agent in the alliance. Considering the influence of different combination cost-sharing balances, a high proportion of new energy power generation and the stability of the alliance, the optimal combination scheme of the alliance is determined. The corresponding analysis results and related data are on page 18, section 5.5, lines 548-584.

 

In addition to the above changes, we also made the following changes:

1. Add research deficiencies in existing literature and academic contributions to this paper in the introduction

In the problematic part of the existing research, the analysis of three allocation methods (Please see page 3, lines 117-127) was added to reflect the problems of different allocation methods. At the same time, academic contribution has increased. This paper proposes a solution to the shortcomings of existing allocation methods (Please see page 4, lines 140-147). That is, the discrete coefficient of cost-sharing value and its median index are introduced as the basis for judging the willingness of different subjects to participate and the stability of the alliance. The overall equilibrium effect of the coalition is studied to determine the power generation, power generation ratio and optimal power generation combination scheme of each agent under multi-spatio-temporal scales.

 

2.Added new model equations and corresponding simulation data

The new model equation is on page 9 in Section 4.3. To validate the novelty of this work, it is convenient to systematically evaluate the effect of different combinations of cost allocation equalization and optimal combination schemes. We choose to construct a cost equilibrium value discrete coefficient equation (see page 9, section 4.3: Multi-energy coalition Shapley cost allocation equilibrium effect and balanced combination test) to assess the overall stability of the coalition and the acceptance of the coalition 's cost equilibrium allocation effect by each agent. ​Based on the median equation, the median values of the discrete coefficients under various combinations are calculated. The discrete coefficient value centering scheme is used as a reference for the multi-scale optimal combination strategy for each agent in the alliance. Considering the influence of different combination cost-sharing balances, a high proportion of new energy power generation and the stability of the alliance, the optimal combination scheme of the alliance is determined. The corresponding analysis results and related data are on page 18, section 5.5, lines 548-584.

 

3. The Comprehensive discussion modifies some of the content

The (4) and (5) of the comprehensive discussion are modified as follows(Please see page 20, lines 614-630):

(4) There are differences between the research results based on hypotheses 1 through 4 and their actual application, primarily in the following areas: Firstly, the overall power shortage model based on the system directly affects the accuracy of the cost calculation results of each subject. That is, the output distribution and cost variation of each subject based on real-time power demand are different from this study. Secondly, the effect of the primary frequency regulation function of the photovoltaic or wind power generation system on the cost-sharing and the change in output ratio of each main body during actual operation has not been taken into account. Thirdly, the power generation cost and auxiliary service income of each main body have not been combined for the calculation to optimize the alliance's operation strategy.

        (5) The case data in this paper are 47 component cost data based on the load peak and valley periods in Assumption 3. It provides a research paradigm for the selection of a multi-energy alliance combination optimization mode. During the real-time electricity market transaction, the multi-energy alliance can be simulated to balance the 24 point dynamic load demand values, and the dynamic equilibrium cost sharing can be calculated to determine the generation power, generation ratio and optimal generation portfolio scheme under the multi-space-time scales of each main body.

 

4. The conclusion part adds the limitations of this study and the main problems to be solved in the next step (Please see Page 21, lines 654-664).

There are two main aspects: (1) Considering the overall power shortage mode of the system, the energy supply and demand are balanced. This method affects the accuracy of each subject cost calculation results to some extent. Considering the time-varying characteristics of the load and meeting the real-time power demand, the accurate calculation of the output allocation and dynamic cost allocation of the main bodies of the alliance is focused. (2) In this paper, the internal collaborative optimization operation mechanism of the alliance is summarized through the actual case data change rules. However, the differentiation and robustness of corresponding data changes under different load peak-valley difference operation modes need to be further studied. To further clarify the general applicability of this study program.

Best regards.

Yong Cui

Author Response File: Author Response.pdf

Reviewer 3 Report

This is an interest paper presenting a methodology to assess the performance in regarding of optimal operation strategy of renewable energy and conventional energy systems. Please see my comments as follows.

1. Please condense the manuscript. It is too lengthy and there are too many sections for a journal paper. Please re-organize the content.

2. Please revise some of the expressions. For instance, what does "double carbon" mean?

3. Table 1 could be using narratives to compare the current study and the literature. The table is hard to read.

4. How is the model being validated?

Thanks.

Author Response

Dear Reviewers:

we are very grateful to you for allowing us to revise our manuscript. we appreciate you very much for your positive and constructive comments and suggestions on our manuscript entitled “Analysis of optimal operation of multi energy alliance based on multi-scale dynamic cost equilibrium allocation”

We have studied the reviewers’ comments carefully and tried our best to revise our manuscript according to the comments. The following are the responses and revisions I have made in response to the reviewers' questions and suggestions on an item-by-item basis. Thanks again for the hard work of the editor and reviewer!

 

1. Please condense the manuscript. It is too lengthy and there are too many sections for a journal paper. Please re-organize the content.

Response: Thank you for your comments. First of all, in the text part, we remove Table 1, which accounts for a large proportion of the space, and the space of the article is compressed. Secondly, we sort out the full text and delete some repetitive content.

 

2. Please revise some of the expressions. For instance, what does "double carbon" mean?

Response: Thank you for your advice. 'Double carbon ' means carbon peak and carbon neutralization, which is modified as Carbon Peak and Carbon Neutralization.

 

3. Table 1 could be using narratives to compare the current study and the literature. The table is hard to read.

Response: We remove Table 1 from the text and place it in the attached table. To improve the readability of the article, the contents of Table 1 are narratively presented in the introduction part (Please see 1, lines 32-139). Some contents of table 1 are deleted, and only the contents highly related to this paper are retained.

 

4. How is the model being validated?

Response: To validate the novelty of this work, it is convenient to systematically evaluate the effect of different combinations of cost allocation equalization and optimal combination schemes. We choose to construct a cost equilibrium value discrete coefficient equation (see page 9, section 4.3: Multi-energy coalition Shapley cost allocation equilibrium effect and balanced combination test) to assess the overall stability of the coalition and the acceptance of the coalition 's cost equilibrium allocation effect by each agent. ​Based on the median equation, the median values of the discrete coefficients under various combinations are calculated. The discrete coefficient value centering scheme is used as a reference for the multi-scale optimal combination strategy for each agent in the alliance. Considering the influence of different combination cost-sharing balances, a high proportion of new energy power generation and the stability of the alliance, the optimal combination scheme of the alliance is determined. The corresponding analysis results and related data are on page 18, section 5.5, lines 548-584.

 

In addition to the above changes, we also made the following changes:

1. Summary section

In the summary section, we added the research methods used in this paper and the results of this study and related data.

 

2. Three articles were added and the way in which they are cited in this paper changed

We have added three references in the References:

[14]Wang Yongli, Liu Zhen, et al. Research on the optimization method of integrated energy system operation with multi-subject game[J]. Energy,2022,245.

[15]Zhou Yifan, Hu Wei, et al. Compensation Pricing and Benefit Distribution Method of Cogeneration Participating in Peak Regulation of Power Grid [J]. Proceedings of the CSEE, 2019,39(18).

[16]Wei Chun,Shen Zhuzheng et al. An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining[J]. Applied Energy,2021,295.

Meanwhile, the main contents of the three kinds of literature are described in the introduction (Please see page 3, lines 102-11). The three works of literature introduce three different distribution methods: nucleolus method, Shapley and Nash equilibrium.

 

3. Add research deficiencies in existing literature and academic contributions to this paper in the introduction

In the problematic part of the existing research, the analysis of three allocation methods (Please see page 3, lines 117-127) was added to reflect the problems of different allocation methods. At the same time, academic contribution has increased. This paper proposes a solution to the shortcomings of existing allocation methods (Please see page 4, lines 140-147). That is, the discrete coefficient of cost-sharing value and its median index are introduced as the basis for judging the willingness of different subjects to participate and the stability of the alliance. The overall equilibrium effect of the coalition is studied to determine the power generation, power generation ratio and optimal power generation combination scheme of each agent under multi-spatio-temporal scales.

 

4. The Comprehensive discussion modifies some of the content

The (4) and (5) of the comprehensive discussion are modified as follows(Please see page 20, lines 614-630):

(4) There are differences between the research results based on hypotheses 1 through 4 and their actual application, primarily in the following areas: Firstly, the overall power shortage model based on the system directly affects the accuracy of the cost calculation results of each subject. That is, the output distribution and cost variation of each subject based on real-time power demand are different from this study. Secondly, the effect of the primary frequency regulation function of the photovoltaic or wind power generation system on the cost-sharing and the change in output ratio of each main body during actual operation has not been taken into account. Thirdly, the power generation cost and auxiliary service income of each main body have not been combined for the calculation to optimize the alliance's operation strategy.

        (5) The case data in this paper are 47 component cost data based on the load peak and valley periods in Assumption 3. It provides a research paradigm for the selection of a multi-energy alliance combination optimization mode. During the real-time electricity market transaction, the multi-energy alliance can be simulated to balance the 24 point dynamic load demand values, and the dynamic equilibrium cost sharing can be calculated to determine the generation power, generation ratio and optimal generation portfolio scheme under the multi-space-time scales of each main body.

 

5. The conclusion part adds the limitations of this study and the main problems to be solved in the next step (Please see Page 21, lines 654-664).

There are two main aspects: (1) Considering the overall power shortage mode of the system, the energy supply and demand are balanced. This method affects the accuracy of each subject cost calculation results to some extent. Considering the time-varying characteristics of the load and meeting the real-time power demand, the accurate calculation of the output allocation and dynamic cost allocation of the main bodies of the alliance is focused. (2) In this paper, the internal collaborative optimization operation mechanism of the alliance is summarized through the actual case data change rules. However, the differentiation and robustness of corresponding data changes under different load peak-valley difference operation modes need to be further studied. To further clarify the general applicability of this study program.

Best regards.

Yong Cui

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Thanks for addressing my comments.