Round 1

Reviewer 1 Report

In this paper, the authors present a comparison study for improving the battery autonomy in a Photovoltaic system between two different optimization algorithms, Particle swarm and Grey Wolf Optimization algorithms. The paper provided a relatively comprehensive investigation for the selection of best algorithms when a battery storage system is fed through a photovoltaic system. Though the study results are based on the simulation, it is interesting and can be considered for the publication in the Journal of Applied sciences-Basel.

But there are still several problems need to be addressed, and major revisions should be made before consideration in the journal.

(1) The authors should highlight that why the two algorithms are adopted for the study, not other algorithms, such as CS, GA etc.

(2) Many optimization algorithms have been used in different optimization problems, such as structural damage identification, if possible, the following papers can be cited in the paper:

1. GA: Vibration-based structural Damage Identification under Varying Temperature Effects[J]. Journal of Aerospace engineering, 2018, 31(3): 04018014.
2. Bare Bones Particle Swarm Optimization: Structural Damage Identification Based on l(1)Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy, MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 5954104
3. Moth-Flame optimization: Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization, structures, 2020, 28, 1119-1136
4. whale optimization algorithm: Structural damage identification based on substructure method and improved whale optimization algorithm, JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING, 2021, 11(2): 351-380

(4) Some benchmark functions should be used for the validation of the performance of the different algorithms before they are applied for the simulation of a PV system.(6) Please improve the text and pattern of the paper.

(5) In Figure 7, pleased differentiate the four lines by different line style, not a, b, c, and d.

(6) In Table 3, some parameters are specified for the two algorithms, the authors should explain the reasons why they are chosen.

Author Response

AUTHORS’ RESPONSE TO REVIEWERS’ COMMENTS ON THE PAPER ‘A comparison between Particle swarm and Grey Wolf Optimization algorithms for improving the battery autonomy in a Photovoltaic system’

 

 

The authors would like to thank reviewers for their constructive comments and valuable suggestions which helped us to improve the manuscript. We really appreciate the reviewers’ observations and we hope to have fully addressed all their remarks.

 

Note: Reviewers’ comments are presented in bold.

 

REVIEWER 1

 

·       REVIEWER COMMENT R.1.1

 

The authors should highlight that why the two algorithms are adopted for the study, not other algorithms, such as CS, GA etc.

 

RESPONSE R.1.1

We agree that this explanation was missing and we have added it in the new version of the paper. Specifically, we have added:

“There is a variety of swarm algorithms, which have been applied in multiple systems such as in [28–31]. Among them, Particle Swarm Optimization (PSO) [25] and Grey Wolf Optimization (GWO) [32] have shown their reliability to  solve real optimization problems where the objective function is not linear. In particular, the works in [33], [17] only considered these two algorithms to configure a DC/DC power converter.  The review presented in [34] show that PSO algorithms are still investigated to tune the power converters of microgrids.  Moreover, the study elaborated by Mirjalili in [32] presents a comparison between multiple swarm algorithms. As a conclusion, they state that the better results were found for the PSO and the GWO algorithms. Indeed, the two algorithms are inspired by natural competence to get high speed and precision. Based on these previous works, in this paper we evaluate their relative feasibility and performance of employing the swarm algorithms to configure the power converter of the PV panels in order to cope with different shading conditions.”

·       REVIEWER COMMENT R.1.2

Many optimization algorithms have been used in different optimization problems, such as structural damage identification, if possible, the following papers can be cited in the paper:

 

GA: Vibration-based structural Damage Identification under Varying Temperature Effects[J]. Journal of Aerospace engineering, 2018, 31(3): 04018014.

Bare Bones Particle Swarm Optimization: Structural Damage Identification Based on l(1)Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy, MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 5954104

Moth-Flame optimization: Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization, structures, 2020, 28, 1119-1136

whale optimization algorithm: Structural damage identification based on substructure method and improved whale optimization algorithm, JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING, 2021, 11(2): 351-380

 

RESPONSE R.1.2

Thank you for your suggestions. Three of these references were inserted into the text in Section 1, when describing the optimization algorithms. Specifically, they can be found in references from 28 to 31.

·       REVIEWER COMMENT R.1.3

Some benchmark functions should be used for the validation of the performance of the different algorithms before they are applied for the simulation of a PV system.

 

RESPONSE R.1.3

 

We have verified the performance of the implementations by varying the configuration parameters. We have checked that increasing the number of maximum number of iterations and the number of particles/wolves leads to a reduction of the processing time while better performances are achieved. This is an expected behaviour, so we can conclude that the algorithms are correctly implemented.

 

·       REVIEWER COMMENT R.1.4

Please improve the text and pattern of the paper.

 

RESPONSE R.1.4

Thank you for your comments. The paper was carefully revised and many sections were rewritten.

·       REVIEWER COMMENT R.1.5

In Figure 7, pleased differentiate the four lines by different line style, not a, b, c, and d.

RESPONSE R.1.5

Thank you for your suggestion. Figure 7 has been modified accordingly.

·       REVIEWER COMMENT R.1.6

In Table 3, some parameters are specified for the two algorithms, the authors should explain the reasons why they are chosen.

 

RESPONSE R.1.6

We agree that this information should be included. The choice of the algorithms parameters was based on several online tests and evaluations. With this initial work, we found the best combination of parameters that make the algorithms faster and more precise. We have mentioned this into the text. Specifically, we have added:

" The choice of these parameters was fixed after several online simulation tests, where the goal is to find the best combination in terms of the algorithm running speed and the best performances. Specifically, we have varied the number of particles /wolves and the maximum number of iterations. The first test had the configuration of 150 iterations and 10 particles. The corresponding simulation time was evaluated to 30 min when using an I5 laptop with 8-GB as RAM memory. The resulting performances were found perfect.
The second configuration was fixed to 50 iteration and 8 particles, and then the corresponding simulation time was evaluated to 24min, but the resulting energetic performances were not so good. There were some problems with the stability of the output power.
Many other tests (more than 5 combinations) were also applied and the best combination was found as it is indicated in table (3). For the selected configuration, the simulation time was 26 min and we get a good performance in terms of extracted power and stability.“

 

Author Response File: Author Response.docx

Reviewer 2 Report

This is the review of the manuscript entitled „A comparison between Particle swarm and Grey Wolf Optimization algorithms for improving the battery autonomy in a Photovoltaic system”.

In my opinion, the article seems mathematically correct. The writing style is acceptable, and the organization of this manuscript is reasonable.

Paper is suitable for the journal, but some additional workload is needed to rise paper quality.

1. Introduction Section must be written in a more quality way, i.e. more up-to-date references addressed. The novelty of the work must be clearly addressed and discussed, compare your research with existing research findings and highlight novelty.
2. Discuss and critically analyze the data presented in the Results Section.
3. The practical applicability and usefulness of the study are not specified.

Author Response

AUTHORS’ RESPONSE TO REVIEWERS’ COMMENTS ON THE PAPER ‘A comparison between Particle swarm and Grey Wolf Optimization algorithms for improving the battery autonomy in a Photovoltaic system’

 

 

The authors would like to thank reviewers for their constructive comments and valuable suggestions which helped us to improve the manuscript. We really appreciate the reviewers’ observations and we hope to have fully addressed all their remarks.

 

Note: Reviewers’ comments are presented in bold.

 

 

REVIEWER 2

 

·       REVIEWER COMMENT R.2.1

Introduction Section must be written in a more quality way, i.e. more up-to-date references addressed. The novelty of the work must be clearly addressed and discussed, compare your research with existing research findings and highlight novelty.

 

RESPONSE R.2.1

Thank you for your comments. Section 1 has been rewritten and new references were mentioned. Some of the most relevant changes are:

• Section 1 has been rewritten by explaining new concepts, changing the order of the ideas and revising the text. Specifically, we now explain the non-linear P-V curve associated to shading areas, the existence of several local maxima points in this curve and that we try to configure the system to extract the maximum power.
• Figure 1 has been added in Section 1 to show the non-linear relationship of P-V curve in a partial shaded PV panel.
• We have extended the review of the related work. More than 10 new references have been included in Section 1.
• We have highlighted the contribution of this work with the following text in Section 1:

 

“These two algorithms have already been applied and evaluated separately in PV systems. Particle Swarm Optimization algorithm can help to calculate the duty cycle of the power converter in the PV connection dynamically. Several works tested this solution for this application as in [35] where the authors proved that this solution could be efficient if it is running offline. On the other side, the Grey Wolf Optimization (GWO) algorithm appeared as a useful solution for extracting energy from the PV system with maximum efficiency [36]. However, the two algorithms have several parameters and constants, which must be fixed initially to start the algorithms correctly.

 

The contribution of the paper is to evaluate these two optimization algorithms for a PV system considering realistic partial shading conditions. So we can compare both performances to study their suitability. The evaluation of each of these algorithms is based on the precision and the speed for tracking the global MPP with different partial shading conditions. Specifically, we have studied the two swarm-intelligence based algorithms for 4 shading conditions in a 4-module PV system.”

 

·       REVIEWER COMMENT R.2.2

 

Discuss and critically analyze the data presented in the Results Section.

 

AUTHORS’ RESPONSE R.2.2

 

We have proceeded accordingly and we have added the following text in Section 4:

 

“As can be observed, the simulation results from Figure 10 to Figure 13 shows the evolution of the power, current, voltage and duty cycle of the PV system for the four types of shading distribution we have tested on the panels. From these results, it can be observed that the two solutions ensure a good MPP tracking. The advantage of the PSO MPPT over the GWO MPPT is related to two issues: (i) the amplitude of the oscillations at the transient state and (ii) the accuracy to track the point of maximum power. A high oscillation exists for the case of GWO, which can be one the weaknesses of this algorithm. There is also a small oscillation when executing the PSO at the beginning, when the radiation form changes. However, this will not cause a problem as in the real situation, the modification of the radiation comes very slow. So, we a look a better performance in a real situation.”

 

 

·       REVIEWER COMMENT R.2.3

The practical applicability and usefulness of the study are not specified.

 

AUTHORS’ RESPONSE R.2.3

 

Thank you for your comment. We have proceeded accordingly and mentioned a discussion regarding this comment into the new version of our paper. In particular, this text has been added:

 

“The application of these algorithms in real time requires the use of the high-speed processor given the large number of operations to be carried out in one second (processing and control measurement). Therefore, the time needed to converge towards the best response depends on the speed of the algorithm used and the material available (essentially the speed of the processor). In addition, the presence of high amplitude oscillations during the transient phase is a harmful phenomenon for electrical systems and can cause a variety of problems. According to the simulation results and the criteria indicated above, the PSO MPPT algorithm shows itself well for the real-time application.”

 

 

 

Author Response File: Author Response.docx

Reviewer 3 Report

The paper applied two metaheuristic algorithms, Grey Wolf Optimization (GWO) and particle swarm optimization (PSO), to improve the battery autonomy in a Photovoltaic system. Finally, experimental results were presented.

Overall, I have to say that the contribution of the paper is not strong enough for publishing in a high ranked journal like "Applied Science", and there are several major concerns regarding this paper. First, the structure of the paper is not good, and some sections, particularly those that are related to PSO and GWO, are very bad. It seems authors rephrase some paragraphs without having enough knowledge about metaheuristics, or maybe the problem is that some keywords have not been appropriately selected. In addition, the structure of some sentences is bad, and it is very difficult for a reader to follow the content. For example, in the abstract "The mathematical simulation of the photovoltaic system, its function, and its features, taking into account the synthesis of the step-up converter and the maximum power point tracking analysis, are the key topics covered in this article. This research looks at two intelligent control strategies for getting the most power out." Or in line 20, "A comparison between the intelligent solution "particle swarm optimization method" and the "grey wolf optimization method", is made in this study, and with simulation tests, the best method is identified." Furthermore, many claims and sentences in the paper are unprofessional. For example, in line 60, "Climate change, which refers to changes in irradiance, is the most pressing concern with a solar-powered system." OR in line 389" The proposed GWO algorithm aims to compensate for the limitations of conventional maximization algorithms, which cannot find the overall maximum, especially during a partial shading phenomenon. "Or in line 66, authors mentioned some factors without appropriate references, "The efficiency of PV energy production depends mainly on the conditions to which the PV field is subjected, namely radiation, temperature, and the state of the PV field surface (dirt, deterioration)." Line 119 is another example that I really couldn't understand "Particle Swarm Optimization (PSO) algorithm was one selected algorithm that can help to calculate the duty? Cycle dynamically, according to the position of their particles." In addition, authors should check abbreviations, for example, in line 64, "The Maximum Power Point Tracking management tool is a required control loop that helps to get the most power out of a solar system." This list is endless…… 

Second, Appling new proposed optimization methods for a problem and comparing them cannot be considered as a STRONG contribution to the knowledge. Unfortunately, a bad trend has been started in the past years, and every year hundreds obscure metaheuristic methods are proposed, and many researchers in different field JUST applying them in their research without any significant contribution in the method. "Strange" metaheuristics are being severely criticized lately. The literature on optimization methods, be those discrete (combinatorial optimization) or continuous is extremely large. Therefore, proposing new paradigms or using recent paradigms is not a promising venue of research. As regards combinatorial optimization methods we have from the classical genetic algorithms or tabu search to basically anything ranging from intelligent water drops, musicians playing jazz, imperialist methods, leapfrogs, wolfs, all types of swarms and insects and even mine blast methods. New metaheuristics for optimization problems should only be published if they are comprehensively compared against state-of-the-art methods for many different optimization problems and for a wide range of benchmark instances in order to be convincing. Otherwise, there is little gain for the scientific community for yet another optimization method. Note that the metaheuristics community is already taking this stance seriously. Note for example the recent paper published in International Transactions of Operations Research by Kenneth Sorensen, titled "Metaheuristics—the metaphor exposed". For your reference, find below the --in my opinion eye opening abstract--

"In recent years, the field of combinatorial optimization has witnessed a true tsunami of "novel" metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together - it seems that no idea is too far-fetched to serve as inspiration to launch yet another metaheuristic. In this paper, we will argue that this line of research is threatening to lead the area of metaheuristics away from scientific rigor. We will examine the historical context that gave rise to the increasing use of metaphors as inspiration and justification for the development of new methods, discuss the reasons for the vulnerability of the metaheuristics field to this line of research, and point out its fallacies. At the same time, truly innovative research of high quality is being performed as well. We conclude the paper by discussing some of the properties of this research and by pointing out some of the most promising research avenues for the field of metaheuristics."

I do not see a real contribution, the paper as it is cannot be published. I suggest the paper to be rejected. Below I further substantiate this recommendation.

Author Response

AUTHORS’ RESPONSE TO REVIEWERS’ COMMENTS ON THE PAPER ‘A comparison between Particle swarm and Grey Wolf Optimization algorithms for improving the battery autonomy in a Photovoltaic system’

 

 

The authors would like to thank reviewers for their constructive comments and valuable suggestions which helped us to improve the manuscript. We really appreciate the reviewers’ observations and we hope to have fully addressed all their remarks.

 

Note: Reviewers’ comments are presented in bold.

 

REVIEWER 3

 

 

• REVIEWER COMMENT R.3.1

 

Overall, I have to say that the contribution of the paper is not strong enough for publishing in a high ranked journal like "Applied Science".

 

AUTHORS’ RESPONSE R.3.1

 

The main contribution of the paper is to study and compare two of the most used swarm-intelligence based algorithms for the same scenarios. They have already been used and tested separately for PV systems but in our paper we wanted to compare their performances for realistic partial-shading conditions. We have included some new references to prove that the performance of the PSO and GWO optimization are very close.

 

We have modified the text in the paper to highlight this contribution. Specifically, in Section 1, we have added this new paragraph:

 

“These two algorithms have already been applied and evaluated separately in PV systems. Particle Swarm Optimization algorithm can help to calculate the duty cycle of the power converter in the PV connection dynamically. Several works tested this solution for this application as in [35] where the authors proved that this solution could be efficient if it is running offline. On the other side, the Grey Wolf Optimization (GWO) algorithm appeared as a useful solution for extracting energy from the PV system with maximum efficiency [36]. However, the two algorithms have several parameters and constants, which must be fixed initially to start the algorithms correctly.

 

The contribution of the paper is to evaluate these two optimization algorithms for a PV system considering realistic partial shading conditions. So we can compare both performances to study their suitability. The evaluation of each of these algorithms is based on the precision and the speed for tracking the global MPP with different partial shading conditions. Specifically, we have studied the two swarm-intelligence based algorithms for 4 shading conditions in a 4-module PV system.”

 

We have also extended the review of the related work in Section 1. We have included more than 10 recent references to justify the comparison between these two algorithms. These references indicate that the two algorithms are suitable for optimization problems and even some of them are show that they are able to control power converters. From these works, we can justify the use of these algorithms in the optimization problem related to the configuration of the DC/DC converter of the PV system.

 

 

 

• REVIEWER COMMENT R.3.2

 

First, the structure of the paper is not good, and some sections, particularly those that are related to PSO and GWO, are very bad. It seems authors rephrase some paragraphs without having enough knowledge about metaheuristics, or maybe the problem is that some keywords have not been appropriately selected

 

 

AUTHORS’ RESPONSE R.3.2

 

Thank you for your indication. We have modified the two paragraphs that show the PSo and GWO algorithms.

 

• REVIEWER COMMENT R.3.3

 

 The structure of some sentences is bad, and it is very difficult for a reader to follow the content. 

 

AUTHORS’ RESPONSE R.3.3

 

The complete text has been carefully revised. We have modified some sentences and we have also changed the sequence of the ideas explained in the text.

 

 

• REVIEWER COMMENT R.3.4

 

Appling new proposed optimization methods for a problem and comparing them cannot be considered as a STRONG contribution to the knowledge. New metaheuristics for optimization problems should only be published if they are comprehensively compared against state-of-the-art methods for many different optimization problems and for a wide range of benchmark instances in order to be convincing. Otherwise, there is little gain for the scientific community for yet another optimization method. Note that the metaheuristics community is already taking this stance seriously. Note for example the recent paper published in International Transactions of Operations Research by Kenneth Sorensen, titled "Metaheuristics—the metaphor exposed". 

 

AUTHORS’ RESPONSE R.3.4

 

Thank you for your comment. Based on your remark, we have included new references to show that the application of PSO and GWO for the MPPT problem is of interest now for the configuration of power converters in Section 1. Our analysis concludes that these algorithms have been applied separately. Thus, we believe that a comparison between these two swarm-intelligence based algorithms could be a remarkable contribution. In order to highlight the contribution of the paper, we have included the text mentioned in R.3.1

 

The evaluation of the configuration of the algorithms has also been carried out considering realistic parameters related to their applicability (speed of processing, voltage peaks and stability) and for the specific case of PV systems.

 

 

Author Response File: Author Response.docx

Reviewer 4 Report

This paper is indirectly interesting because of its pro-ecological and financial nature. The efficiency of the use of renewable energy sources is very important in the context of their rational use and competitiveness with regard to conventional energy sources.

1. My question is how the optimization methods indicated in the work were chosen? What was the criterion for their selection? Why, for example, there is no the Firefly Algorithm?
2. In  my opinion the paper should already include an element of comparing the values ​​of individual algorithm parameters, but the authors decided to do it in the later stages of the work. It is acceptable, but supplementing the work with this element would increase its value.
3. Have any experimental studies confirming the simulations and results been carried out or planned?
4. The paper should include a diagram of the system of photovoltaic modules (at least simplified) for which the simulation was made.
5. The paper requires refinement also in terms of text editing, formatting and graphics.

Other comments:

• lines 139 and 142: red font,
• line 146: the chapter title should not be at the end of the page,
• line 156: figure resolution should be better,
• line 172: one colon unnecessary (?),
• line 181: why is there a comma at the end of the line, and sometimes it is not?
• line 204: why there are different fonts in the figure, the editing of this graphic should be improved,
• equations should be centered (general comment, not all equations are centered),
• line 261: figure caption should be centered,
• lines 274, 275, 293, 294: the small distance between the equations make the writing difficult to read,
• line 300: unnesesery dot at the end of the line,
• line 331: the resolution of the figure should be better, whether it is original drawing or prtscr, source should be provided,
• line 337: there is probably one equation,
• line 335: no numbering,
• once is useing "equation", and once "eq" in equations caption, it should be standardized,
• line 436: what is the difference between the figure on the right and the figure on the left, it should be understandable looking at the graph itself and its description, why in the description of the vertical axis it is sometimes PV, and sometimes Pv,
• line 441: table caption and table should not be on two different pages,
• lines 447, 450, 454, 458: times or time (?),
• the style of all tables should be standardized at work (general comment).

Author Response

AUTHORS’ RESPONSE TO REVIEWERS’ COMMENTS ON THE PAPER ‘A comparison between Particle swarm and Grey Wolf Optimization algorithms for improving the battery autonomy in a Photovoltaic system’

 

 

The authors would like to thank reviewers for their constructive comments and valuable suggestions which helped us to improve the manuscript. We really appreciate the reviewers’ observations and we hope to have fully addressed all their remarks.

 

Note: Reviewers’ comments are presented in bold.

 

 

REVIEWER 4

 

 

• REVIEWER’S COMMENT R.4.1

My question is how the optimization methods indicated in the work were chosen? What was the criterion for their selection? Why, for example, there is no the Firefly Algorithm?

 

 

AUTHORS’ RESPONSE R.4.1

 

This is indeed an important comment and we have explained it in the new version of the paper.  Based on many recent researches and publications about swarm-intelligence based  algorithms, the best score is for the Particle swarm and Grey wolf optimization algorithms. These two algorithms have already been applied to MPPT problem but the comparison between them is still missing. We have justified the selection of these two algorithms with the following text:

 

“These two algorithms have already been applied and evaluated separately in PV systems. Particle Swarm Optimization algorithm can help to calculate the duty cycle of the power converter in the PV connection dynamically. Several works tested this solution for this application as in [35] where the authors proved that this solution could be efficient if it is running offline. On the other side, the Grey Wolf Optimization (GWO) algorithm appeared as a useful solution for extracting energy from the PV system with maximum efficiency [36]. However, the two algorithms have several parameters and constants, which must be fixed initially to start the algorithms correctly.

The contribution of the paper is to evaluate these two optimization algorithms for a PV system considering realistic partial shading conditions. So we can compare both performances to study their suitability. The evaluation of each of these algorithms is based on the precision and the speed for tracking the global MPP with different partial shading conditions. Specifically, we have studied the two swarm-intelligence based algorithms for 4 shading conditions in a 4-module PV system.”

 

 

Firefly algorithm seems to be also important and valid for PV applications. We will add this algorithm in our future plans to test its efficiency in the PV field.

• REVIEWER’S COMMENT R.4.2

 

In  my opinion the paper should already include an element of comparing the values ​​of individual algorithm parameters, but the authors decided to do it in the later stages of the work. It is acceptable, but supplementing the work with this element would increase its value.

 

AUTHORS’ RESPONSE R.4.2

 

Thank you for your suggestion. We have added the following explanation:

 

“The choice of these parameters was fixed after several online simulation tests, where the goal is to find the best combination in terms of the algorithm running speed and the best performances. Specifically, we have varied the number of particles /wolves and the maximum number of iterations. .The first test had the configuration of 150 iterations and 10 particles. The corresponding simulation time was evaluated to 30 min when using an I5 laptop with 8-GB as RAM memory. The resulting performances were found perfect.
The second configuration was fixed to 50 iteration and 8 particles, and then the corresponding simulation time was evaluated to 24 minutes, but the resulting energetic performances were not so good. There were some problems with the stability of the output power.
Many other tests (more than 5 combinations) were also applied and the best combination was found as it is indicated in table (3). For the selected configuration, the simulation time was 26 min and we get a good performance in terms of extracted power and stability.“

 

 

• REVIEWER’S COMMENT R.4.3

 

Have any experimental studies confirming the simulations and results been carried out or planned?

 

AUTHORS’ RESPONSE R.4.3

 

Thank you for your comment. The experimental project has not yet executed as a high processor equipment is required and we do not have it in our lab. Anyway, we have mentioned a new paragraph regarding this comment. The new text is:

 

“The application of these algorithms in real time requires the use of the high-speed processor given the large number of operations to be carried out in one second (processing and control measurement). Therefore, the time needed to converge towards the best response depends on the speed of the algorithm used and the material available (essentially the speed of the processor). In addition, the presence of high amplitude oscillations during the transient phase is a harmful phenomenon for electrical systems and can cause a variety of problems. According to the simulation results and the criteria indicated above, the PSO MPPT algorithm shows itself well for the real-time application.”

 

• REVIEWER’S COMMENT R.4.4

 

The paper should include a diagram of the system of photovoltaic modules (at least simplified) for which the simulation was made.

 

AUTHORS’ RESPONSE R.4.4

 

Thank you for your indication. We have included this new Figure 8 in Section 5.

 

 

• REVIEWER’S COMMENT R.4.5

 

The paper requires refinement also in terms of text editing, formatting and graphics.

 

AUTHORS’ RESPONSE R.4.5

 

We have proceeded as suggested and we have edited the text of the new version of the paper. The new version of the text includes reordering the ideas to make the paper clear. We have also modified some Figures (such as Figure 1, 8 and 9).

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Most of the comments are replied positively, but the format of the authors's name of Ref. 28 is not correct and Ref. 29 is not correct.

Author Response

·       REVIEWER COMMENT R.1.1

Most of the comments are replied positively, but the format of the authors's name of Ref. 28 is not correct and Ref. 29 is not correct.

RESPONSE R.1.1

Thank you to the respected reviewer for your comments. The references were modified as the Journal format.

Author Response File: Author Response.docx

Reviewer 2 Report

The authors responded to my suggestions.

Author Response

·       REVIEWER COMMENT R.1.1

 

The authors responded to my suggestions.

RESPONSE R.2.1

Thank you to the respected reviewer for accepting our work.

Author Response File: Author Response.docx

Reviewer 3 Report

My main concerns are about the following comments. And, I strongly believe applying two new methods and comparing them can not have a strong contribution for a journal with a high impact factor.

Overall, I have to say that the contribution of the paper is not strong enough for publishing in a high ranked journal like "Applied Science".

Appling new proposed optimization methods for a problem and comparing them cannot be considered as a STRONG contribution to the knowledge. New metaheuristics for optimization problems should only be published if they are comprehensively compared against state-of-the-art methods for many different optimization problems and for a wide range of benchmark instances in order to be convincing. Otherwise, there is little gain for the scientific community for yet another optimization method. Note that the metaheuristics community is already taking this stance seriously. Note for example the recent paper published in International Transactions of Operations Research by Kenneth Sorensen, titled "Metaheuristics—the metaphor exposed". 

Author Response

• REVIEWER COMMENT R.3.1

 

My main concerns are about the following comments. And, I strongly believe applying two new methods and comparing them can not have a strong contribution for a journal with a high impact factor.

Overall, I have to say that the contribution of the paper is not strong enough for publishing in a high ranked journal like "Applied Science".

Appling new proposed optimization methods for a problem and comparing them cannot be considered as a STRONG contribution to the knowledge. New metaheuristics for optimization problems should only be published if they are comprehensively compared against state-of-the-art methods for many different optimization problems and for a wide range of benchmark instances in order to be convincing. Otherwise, there is little gain for the scientific community for yet another optimization method. Note that the metaheuristics community is already taking this stance seriously. Note for example the recent paper published in International Transactions of Operations Research by Kenneth Sorensen, titled "Metaheuristics—the metaphor exposed".

 

 

AUTHORS’ RESPONSE R.3.1

 

Thank you the respected reviewer for His comment. The main contribution of the paper is to study and compare two of the most used swarm-intelligence based algorithms for the same scenarios. They have already been used and tested separately for PV systems but in our paper we wanted to compare their performances for realistic partial-shading conditions. We have included some new references to prove that the performance of the PSO and GWO optimization are very close.

 

We have also extended the review of the related work in Section 1. We have included more than 10 recent references to justify the comparison between these two algorithms. These references indicate that the two algorithms are suitable for optimization problems and even some of them are show that they are able to control power converters. From these works, we can justify the use of these algorithms in the optimization problem related to the configuration of the DC/DC converter of the PV system.

Author Response File: Author Response.docx