Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper shows a novel control strategy for synergizing solar and wind power. 

The abstract should be shortened to be more concise, and more concrete from the used topic.

The introduction of the paper should be more concrete; the first paragraph states exciting information about the motivation of the research; however, this should be a deeper analysis, which shows the state-of-the-art research papers from the area. The authors selected to use the PSO algorithm to enhance the control strategy, but what other methodologies exist in the literature? There are other evolutionary algorithms, like nsga2 have these codes been used in other papers? This should be shown in the introduction. These methods can be compared, because the selection of the optimal methodology depends on the problem, due to the no-free lunch theorem of the optimization (https://doi.org/10.3390/app10196653).

- The quality of the images, like figure 5, 6, 7should be improved.

- it is very hard to read the added information from figure 22, 23 , 28,29 please consider to change it.

In the conclusion, it would be important to highlight that how is the proposed methodology differs from the current applications and some more concrate and concise things should be used for a takeway from the project.

Author Response

Response to Reviewer 1 Comments

The paper shows a novel control strategy for synergizing solar and wind power. 

Thank you very much for the opportunity to review this paper. The authors would like to thank the reviewers for their suggestions and constructive comments. A review has been undertaken to respond to the reviewers' comments as well as to improve the quality of the paper. The significant changes have been highlighted in the revised paper.

1. The abstract should be shortened to be more concise, and more concrete from the used topic.
   - This study introduces an advanced hybrid energy system combining photovoltaic (PV) and wind power, focusing on sustainable and robust energy solutions. Using dynamic modeling and Lyapunov's theorem, the research emphasizes control system stability. Incorporating a PV array with a Doubly Fed Induction Generator and electronic converters, the design is evaluated through MATLAB simulations for environmental adaptability. The integration of Model Predictive Control with Particle Swarm Optimization enhances control efficiency and Maximum Power Point Tracking, confirming the system's stable power generation and practicality in renewable energy integration.
2. The introduction of the paper should be more concrete; the first paragraph states exciting information about the motivation of the research; however, this should be a deeper analysis, which shows the state-of-the-art research papers from the area. The authors selected to use the PSO algorithm to enhance the control strategy, but what other methodologies exist in the literature? There are other evolutionary algorithms, like nsga2 have these codes been used in other papers? This should be shown in the introduction. These methods can be compared, because the selection of the optimal methodology depends on the problem, due to the no-free lunch theorem of the optimization (https://doi.org/10.3390/app10196653).

-Thank you for your feedback.

“As the world's population soars and industrial activities expand, a corresponding surge in energy consumption has become increasingly evident, highlighting the indispensable role of energy in contemporary society [1-3]. This escalating demand, juxtaposed with the finite nature of fossil fuel resources and growing environmental impact, necessitates a swift pivot towards more sustainable and eco-friendly energy alternatives like solar, wind, and hydro power [4]. In the vanguard of renewable options, solar and wind energy have emerged as frontrunners due to their significant potential and rapid adoption, evidenced by the global expansion of photovoltaic (PV) systems and wind farms [5-6]. Advancements in PV technology have revolutionized the conversion of solar energy into electrical power, with notable progress and cost efficiencies achieved in solar module development [5], [8, 9]. Concurrently, wind energy, propelled by advancements in technologies such as Doubly Fed Induction Generators (DFIGs), is becoming increasingly prominent, offering a blend of efficiency, flexibility, and economic viability [6, 7]. Yet, the variable nature of both solar and wind resources introduces substantial challenges to maintaining system stability and achieving peak efficiency. This paper addresses these complexities by introducing an innovative hybrid system that integrates solar and wind power. At the core of our solution is a sophisticated control mechanism: a Model Predictive Control (MPC) framework bolstered by Particle Swarm Optimization (PSO). This combination enables precise management of the intricate dynamics characteristic of hybrid systems. The predictive prowess of MPC and the optimization capabilities of PSO are harnessed to fine-tune the control actions, ensuring not only improved energy output from both sources but also a robust response to their inherent variability [17-21]. In the sphere of control systems, the assurance of stability is essential for the reliable functioning of energy systems. We employ Lyapunov's theorem to provide a mathematical proof of stability for our controllers. By constructing a Lyapunov function that adheres to the necessary conditions of being continuously differentiable, positive definite, and decreasing along system trajectories, we establish that our system is bound for a stable equilibrium, irrespective of the fluctuating nature of the energy sources. The paper is structured to detail the hybrid energy system's components and operations, with an emphasis on the PSO-enhanced MPC strategy delineated in Section 2. Successive sections unfold as follows: Section 3 offers a critique of control strategies vis-à-vis a grid-connected wind system, evaluating their performance amidst varying wind conditions. Section 4 provides a thorough account of the DFIG wind system's elements, enhanced by the PSO control technique. Section 5 presents our simulation methodology and findings, evidencing the system's adaptability to changing solar irradiance and wind velocities. The final section, Section 6, synthesizes the study's pivotal discoveries and proposes directions for forthcoming research within this dynamic field.”

3. The quality of the images, like figure 5, 6, 7should be improved.

-Thank you very much again for your valuable comment, we modified figures 5, 6 and 7.

4. It is very hard to read the added information from figure 22, 23, 28,29 please consider to change it.

-We greatly appreciate your insightful feedback. Based on your suggestions, we have revised Figures 22, 23, 28, and 29.

5. In conclusion, it would be important to highlight that how is the proposed methodology differs from the current applications and some more concrate and concise things should be used for a takeway from the project.

-Thank you for all your advice and comments. We have reformulated and corrected the conclusion according to your advice.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper explores hybrid solar and wind systems, but its contribution remains unclear, as both configurations and control approaches for solar PV systems and wind generations are well-documented in the literature review. Furthermore, the paper suffers from poor writing and lacks scientific rigor. Specific issues include:

 

1.      Abstract: Rewrite the abstract to concisely summarize the research motivation, identify the research gap, outline the methodology, and highlight key findings.

2.      Introduction: The introduction is poorly written, and numerous statements lack references (e.g., see lines 71 to 107). Ensure that all statements are properly supported with references.

3.      Introduction (Continued): Include a section towards the end of the introduction that outlines the key contributions of this paper.

4.      Equations and Figures: Ensure that equations are properly referenced, and improve the quality of figures. Screenshots with poor resolution are not acceptable.

5.      Simulation Parameters: Provide missing key control and circuit parameters for the simulated system to enhance the clarity and completeness of the study.

6.      References: Update the references list with more relevant and up-to-date journal references from the last three years to strengthen the scholarly foundation of the paper.

Comments on the Quality of English Language

N/A

Author Response

Response to Reviewer 2 Comments

This paper explores hybrid solar and wind systems, but its contribution remains unclear, as both configurations and control approaches for solar PV systems and wind generations are well-documented in the literature review. Furthermore, the paper suffers from poor writing and lacks scientific rigor. Specific issues include:

Thank you very much for the opportunity to review this paper. The authors would like to thank the reviewers for their suggestions and constructive comments. A review has been undertaken to respond to the reviewers' comments as well as to improve the quality of the paper. The significant changes have been highlighted in the revised paper.

1. Abstract: Rewrite the abstract to concisely summarize the research motivation, identify the research gap, outline the methodology, and highlight key findings.
   - This study introduces an advanced hybrid energy system combining photovoltaic (PV) and wind power, focusing on sustainable and robust energy solutions. Using dynamic modeling and Lyapunov's theorem, the research emphasizes control system stability. Incorporating a PV array with a Doubly Fed Induction Generator and electronic converters, the design is evaluated through MATLAB simulations for environmental adaptability. The integration of Model Predictive Control with Particle Swarm Optimization enhances control efficiency and Maximum Power Point Tracking, confirming the system's stable power generation and practicality in renewable energy integration.
2. Introduction: The introduction is poorly written, and numerous statements lack references (e.g., see lines 71 to 107). Ensure that all statements are properly supported with references.

-Thank you for your feedback.

3. Introduction (Continued): Include a section towards the end of the introduction that outlines the key contributions of this paper.

“As the world's population soars and industrial activities expand, a corresponding surge in energy consumption has become increasingly evident, highlighting the indispensable role of energy in contemporary society [1-3]. This escalating demand, juxtaposed with the finite nature of fossil fuel resources and growing environmental impact, necessitates a swift pivot towards more sustainable and eco-friendly energy alternatives like solar, wind, and hydro power [4]. In the vanguard of renewable options, solar and wind energy have emerged as frontrunners due to their significant potential and rapid adoption, evidenced by the global expansion of photovoltaic (PV) systems and wind farms [5-6]. Advancements in PV technology have revolutionized the conversion of solar energy into electrical power, with notable progress and cost efficiencies achieved in solar module development [5], [8, 9]. Concurrently, wind energy, propelled by advancements in technologies such as Doubly Fed Induction Generators (DFIGs), is becoming increasingly prominent, offering a blend of efficiency, flexibility, and economic viability [6, 7]. Yet, the variable nature of both solar and wind resources introduces substantial challenges to maintaining system stability and achieving peak efficiency. This paper addresses these complexities by introducing an innovative hybrid system that integrates solar and wind power. At the core of our solution is a sophisticated control mechanism: a Model Predictive Control (MPC) framework bolstered by Particle Swarm Optimization (PSO). This combination enables precise management of the intricate dynamics characteristic of hybrid systems. The predictive prowess of MPC and the optimization capabilities of PSO are harnessed to fine-tune the control actions, ensuring not only improved energy output from both sources but also a robust response to their inherent variability [17-21]. In the sphere of control systems, the assurance of stability is essential for the reliable functioning of energy systems. We employ Lyapunov's theorem to provide a mathematical proof of stability for our controllers. By constructing a Lyapunov function that adheres to the necessary conditions of being continuously differentiable, positive definite, and decreasing along system trajectories, we establish that our system is bound for a stable equilibrium, irrespective of the fluctuating nature of the energy sources. The paper is structured to detail the hybrid energy system's components and operations, with an emphasis on the PSO-enhanced MPC strategy delineated in Section 2. Successive sections unfold as follows: Section 3 offers a critique of control strategies vis-à-vis a grid-connected wind system, evaluating their performance amidst varying wind conditions. Section 4 provides a thorough account of the DFIG wind system's elements, enhanced by the PSO control technique. Section 5 presents our simulation methodology and findings, evidencing the system's adaptability to changing solar irradiance and wind velocities. The final section, Section 6, synthesizes the study's pivotal discoveries and proposes directions for forthcoming research within this dynamic field.”

4. Equations and Figures: Ensure that equations are properly referenced, and improve the quality of figures. Screenshots with poor resolution are not acceptable.

-Thank you for your feedback. We have corrected equations and figures.

5. Simulation Parameters: Provide missing key control and circuit parameters for the simulated system to enhance the clarity and completeness of the study.

-Thank you very much again for your valuable comment, we modified it.

6. References: Update the references list with more relevant and up-to-date journal references from the last three years to strengthen the scholarly foundation of the paper.
   -Thank you for your feedback.

References:

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32. Mohamed, S.A.; El Sattar, M.A. A comparative study of P and O and INC maximum power point tracking techniques for grid-connected PV systems. SN Appl. Sci. 2019, 1, 174.
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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper presents an innovative approach to enhance grid stability using a hybrid system that integrates photovoltaic (PV) and wind energy sources. The system employs a Doubly Fed Induction Generator (DFIG) for wind power conversion and a high-gain DC-DC converter for PV power optimization. The control strategy is based on Model Predictive Control (MPC) enhanced by Particle Swarm Optimization (PSO) to achieve efficient and adaptive management of the power converters and the DFIG. The stability of the control system is validated by applying Lyapunov's theorem, which demonstrates the resilience and equilibrium of the system under varying environmental conditions. The simulation results confirm the system's capacity for stable power generation and grid integration, highlighting the benefits of combining PV and wind power. This paper contributes to the field of renewable energy by proposing a sophisticated control system that leverages the synergies of solar and wind power for a sustainable and clean-energy future.

I have some suggestions for the authors to improve this paper even further:

1. The introduction of your paper is too broad and does not provide a clear motivation or context for your work. The authors should focus on the specific problem that they are addressing, the existing literature and gaps, and the main objectives and contributions of the work presented. The authors should also outline the paper structure at the end of the introduction.

2. The configuration of the hybrid system in Section 2 is not well explained. The authors should provide more details on how the PV and wind components are connected and integrated, as well as the advantages and challenges of this configuration.

3. Current equation provided in (1) is incorrect. Current across a diode is equal to I = Irs (exp (Voltage across diode/AVt) – 1 ). So the "-1" in exponential should be outside the exponential. Also, the Figure 3 had Ipv as current generated by the PV panel. Whereas, the equation (1) and parameter description use I as the current generated by PV. Please fix it.

4. The description of the wind system in Section 4 is incomplete and unclear. The authors should provide more information on the DFIG, the RSC, and the GSC, and how they are controlled and optimized by the MPC and PSO algorithms. The authors should also explain how the wind system interacts with the PV system and the grid and the challenges and solutions for this interaction.

5. I will request the authors provide a better image for Figure 6.

6. The simulation results in Section 5 are not well presented and analyzed. The authors should provide more graphs and tables to show the hybrid system's performance under different scenarios and conditions. The authors should also compare their results with other existing methods and discuss the advantages and disadvantages of your approach.

7. The results section should present and discuss the simulation results, as well as compare them with the existing literature or benchmarks. Some figures are not well labeled or captioned, making them hard to interpret. For example, Figure 15 does not have a clear title or legend, and Figure 27 does not have a clear y-axis label or unit. Some figures are too small or blurry, such as Figure 11 and Figure 28. Some tables are not well formatted or aligned, such as Table 2. Some results are not well explained or analyzed, such as the results in section 5.3.1 and section 5.3.2.

 

8. The conclusion in Section 6 is too brief and does not summarize this work's main findings and implications. The authors should restate their work's main problem, approach, results, and contributions, and highlight its novelty and significance. The authors should also discuss the limitations and future directions of your research.

Comments on the Quality of English Language

The English quality of the paper is generally good.

Author Response

Response to Reviewer 3 Comments

Thank you very much for the opportunity to review this paper. The authors would like to thank the reviewers for their suggestions and constructive comments. A review has been undertaken to respond to the reviewers' comments as well as to improve the quality of the paper. The significant changes have been highlighted in the revised paper.

This paper presents an innovative approach to enhance grid stability using a hybrid system that integrates photovoltaic (PV) and wind energy sources. The system employs a Doubly Fed Induction Generator (DFIG) for wind power conversion and a high-gain DC-DC converter for PV power optimization. The control strategy is based on Model Predictive Control (MPC) enhanced by Particle Swarm Optimization (PSO) to achieve efficient and adaptive management of the power converters and the DFIG. The stability of the control system is validated by applying Lyapunov's theorem, which demonstrates the resilience and equilibrium of the system under varying environmental conditions. The simulation results confirm the system's capacity for stable power generation and grid integration, highlighting the benefits of combining PV and wind power. This paper contributes to the field of renewable energy by proposing a sophisticated control system that leverages the synergies of solar and wind power for a sustainable and clean-energy future.

I have some suggestions for the authors to improve this paper even further:

1. The introduction of your paper is too broad and does not provide a clear motivation or context for your work. The authors should focus on the specific problem that they are addressing, the existing literature and gaps, and the main objectives and contributions of the work presented. The authors should also outline the paper structure at the end of the introduction.

-Thank you for your feedback.

“As the world's population soars and industrial activities expand, a corresponding surge in energy consumption has become increasingly evident, highlighting the indispensable role of energy in contemporary society [1-3]. This escalating demand, juxtaposed with the finite nature of fossil fuel resources and growing environmental impact, necessitates a swift pivot towards more sustainable and eco-friendly energy alternatives like solar, wind, and hydro power [4]. In the vanguard of renewable options, solar and wind energy have emerged as frontrunners due to their significant potential and rapid adoption, evidenced by the global expansion of photovoltaic (PV) systems and wind farms [5-6]. Advancements in PV technology have revolutionized the conversion of solar energy into electrical power, with notable progress and cost efficiencies achieved in solar module development [5], [8, 9]. Concurrently, wind energy, propelled by advancements in technologies such as Doubly Fed Induction Generators (DFIGs), is becoming increasingly prominent, offering a blend of efficiency, flexibility, and economic viability [6, 7]. Yet, the variable nature of both solar and wind resources introduces substantial challenges to maintaining system stability and achieving peak efficiency. This paper addresses these complexities by introducing an innovative hybrid system that integrates solar and wind power. At the core of our solution is a sophisticated control mechanism: a Model Predictive Control (MPC) framework bolstered by Particle Swarm Optimization (PSO). This combination enables precise management of the intricate dynamics characteristic of hybrid systems. The predictive prowess of MPC and the optimization capabilities of PSO are harnessed to fine-tune the control actions, ensuring not only improved energy output from both sources but also a robust response to their inherent variability [17-21]. In the sphere of control systems, the assurance of stability is essential for the reliable functioning of energy systems. We employ Lyapunov's theorem to provide a mathematical proof of stability for our controllers. By constructing a Lyapunov function that adheres to the necessary conditions of being continuously differentiable, positive definite, and decreasing along system trajectories, we establish that our system is bound for a stable equilibrium, irrespective of the fluctuating nature of the energy sources. The paper is structured to detail the hybrid energy system's components and operations, with an emphasis on the PSO-enhanced MPC strategy delineated in Section 2. Successive sections unfold as follows: Section 3 offers a critique of control strategies vis-à-vis a grid-connected wind system, evaluating their performance amidst varying wind conditions. Section 4 provides a thorough account of the DFIG wind system's elements, enhanced by the PSO control technique. Section 5 presents our simulation methodology and findings, evidencing the system's adaptability to changing solar irradiance and wind velocities. The final section, Section 6, synthesizes the study's pivotal discoveries and proposes directions for forthcoming research within this dynamic field.”

2. The configuration of the hybrid system in Section 2 is not well explained. The authors should provide more details on how the PV and wind components are connected and integrated, as well as the advantages and challenges of this configuration.

-Thank you, we've done it.

“This study unveils a Hybrid Solar PV/Wind System, an elegantly integrated framework that marries the advantages of solar and wind energy to facilitate consistent and efficient power production. The solar facet is composed of photovoltaic panels that efficiently convert sunlight into electrical power. A boost converter then optimizes this power, enhancing the voltage from the solar array, as detailed in reference [22]. The subsequent DC output is converted to AC through a precise DC-AC inverter, maintaining a power factor of one. This AC is then conditioned through a transformer to meet grid voltage standards, as outlined in reference [23].

Parallel to this, the wind component is built around a Doubly Fed Induction Generator (DFIG), a system favored for its efficiency and versatility in harnessing wind energy, as cited in reference [24]. The DFIG integrates a pair of back-to-back converters—the Rotor Side Converter (RSC) and the Grid Side Converter (GSC)—which are pivotal in achieving optimal energy conversion and facilitating grid integration. The GSC is particularly crucial for maintaining a stable DC-link voltage and for its ability to supply reactive power to the grid, thereby improving the system's overall power factor.

As depicted in Figure 1, each element of the system plays an integral role: the solar array employs MPPT technology to maximize power output under variable solar conditions, while the DFIG-based wind subsystem is adept at adapting to changing wind speeds. The RSC is tasked with maximizing energy extraction, and the GSC is responsible for ensuring smooth grid integration and voltage stability. This cohesive strategy not only bolsters efficiency but also secures a reliable and uniform power supply, making it suitable for diverse applications.

The subsequent sections will delve into the modeling, operational performance, and control strategies of this hybrid system. The intricate cooperation between the PV modules, wind generator, power converters, and their respective control methodologies underpins this pioneering energy solution, marking an important step toward sustainable and reliable power generation.”

3. Current equation provided in (1) is incorrect. Current across a diode is equal to I = Irs (exp (Voltage across diode/AVt) – 1 ). So the "-1" in exponential should be outside the exponential. Also, the Figure 3 had Ipv as current generated by the PV panel. Whereas, the equation (1) and parameter description use I as the current generated by PV. Please fix it.
   -Thank you for your feedback. We have corrected the equation and the figure.
4. The description of the wind system in Section 4 is incomplete and unclear. The authors should provide more information on the DFIG, the RSC, and the GSC, and how they are controlled and optimized by the MPC and PSO algorithms. The authors should also explain how the wind system interacts with the PV system and the grid and the challenges and solutions for this interaction.

-Thank you for your comment.

“The Doubly Fed Induction Generator (DFIG) system anchors the architecture of modern wind turbines and is heralded for its advanced design that effectively captures wind energy for electrical conversion. Its market prominence is attributed to the system's ability to allow variable speed operation and its efficient energy modulation, facilitated by a dual-winding rotor design within the induction generator. One winding connects directly to the grid, promoting an uninterrupted flow of electricity, while the other interfaces through a sophisticated set of power electronics converters, namely the Rotor Side Converter (RSC) and the Grid Side Converter (GSC), as elaborated in reference [24].

The GSC is particularly critical in the DFIG configuration. It meticulously manages the Direct Current (DC) voltage across the DC link, a vital aspect that ensures the RSC is supplied with a stable voltage. This regulation is essential for harmonizing the power exported to the grid by the generator. Additionally, the GSC's control mechanism is adept at enhancing grid stability by providing necessary reactive power compensation, thereby enriching the power quality and improving the system's power factor.

The DFIG's innovative capacity lies in its variable speed operation enabled by the RSC, which adeptly modulates the rotor's extracted power. This modulation is responsive to the fluctuating nature of wind speeds, optimizing the generator's output for a range of wind conditions.

A transformer is judiciously incorporated within the system to align the generated power's voltage with that of the Point of Common Coupling (PCC), thus ensuring a seamless integration of wind-generated electricity into the grid's existing infrastructure.

The integration of this wind system with a Photovoltaic (PV) setup and the grid poses a sophisticated challenge due to the intermittent nature of both wind and solar resources. The study, shown in figure 11, presents a system robust and adaptable enough to accommodate the variability of wind speeds while ensuring a smooth grid integration.

Advanced control strategies like the Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) algorithms are employed to finely tune the performance of both the DFIG and the PV systems. MPC utilizes a predictive model of the system's behavior to optimize the control inputs proactively, while PSO optimizes the system's operational parameters by simulating the social dynamics observed in natural swarms.

The interaction between the wind and PV systems, particularly when coupled with the grid, necessitates a delicate balance. The integrated system must address the variability of the energy sources and the demand-response of the grid. Solutions include deploying energy storage to mitigate intermittence and employing smart grid technologies to ensure that the power output is consistent, reliable, and meets consumption demands.

Thus, the study portrays a wind power system that is not only robust and capable of adjusting to wind variability but also harmoniously integrated with the grid and complementary PV systems. The DFIG, with its advanced converters and control strategies epitomized by the MPC and PSO algorithms, stands as a testament to efficiency in wind energy generation, pointing to a sustainable trajectory for renewable power technologies.”

5. I will request the authors provide a better image for Figure 6.

-Thank you very much again for your valuable comment, we modified figure 6.

6. The simulation results in Section 5 are not well presented and analyzed. The authors should provide more graphs and tables to show the hybrid system's performance under different scenarios and conditions. The authors should also compare their results with other existing methods and discuss the advantages and disadvantages of your approach.

-We are grateful for your feedback regarding the presentation and analysis of the simulation results in Section 5. Your constructive comments highlight valuable areas for improvement, and we acknowledge the necessity for a more comprehensive display of the hybrid system's performance through additional graphs and tables. We agree that such enhancements would indeed elucidate the system's behavior under various scenarios and conditions more effectively.

In response to your suggestion, we are currently in the process of developing a new article entitled "Comparison between MPC-PSO Controller for Hybrid Systems and Other Algorithms." This forthcoming publication will specifically address the comparative analysis you have recommended. It will include an extensive review of the hybrid system's performance against other established methods, providing a clear perspective on the advantages and disadvantages of our proposed approach.

We believe that this dedicated article will offer a thorough examination and will serve as a robust platform for discussing the relative merits and potential limitations of the MPC-PSO controller within hybrid systems. We look forward to sharing our findings and contributing further to the discourse in this field.

Thank you once again for your insightful critique, which has prompted us to delve deeper into the subject and strive for clarity and precision in our research dissemination.

7. The results section should present and discuss the simulation results, as well as compare them with the existing literature or benchmarks. Some figures are not well labeled or captioned, making them hard to interpret. For example, Figure 15 does not have a clear title or legend, and Figure 27 does not have a clear y-axis label or unit. Some figures are too small or blurry, such as Figure 11 and Figure 28. Some tables are not well formatted or aligned, such as Table 2. Some results are not well explained or analyzed, such as the results in section 5.3.1 and section 5.3.2.

 -Thank you for your feedback. We have corrected it.

8. The conclusion in Section 6 is too brief and does not summarize this work's main findings and implications. The authors should restate their work's main problem, approach, results, and contributions, and highlight its novelty and significance. The authors should also discuss the limitations and future directions of your research.

-Thank you for all your advice and comments. We have reformulated and corrected the conclusion according to your advice.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

 You worked hard to revise this manuscript and consider the requests. However, due to the high number of changes in the text it's very hard to read this manuscript.

 

The introduction of the paper is shortened significantly, the text is easy to read and follow, however, the description of the novelty is still missing. The authors described very well the used methods, however, the similar solutions and the new manner of the paper are not seen from this description. To highlight these things, it is necessary to describe in details some differences, advantages compared to similar solutions, techniques, or to justify, why they selected the PSO algorithm or the other methodologies. It is not clear, why are novel to use these methodologies in this combination.

Author Response

Response to Reviewer 1 Comments

Dear Authors,

 You worked hard to revise this manuscript and consider the requests. However, due to the high number of changes in the text it's very hard to read this manuscript.

 

The introduction of the paper is shortened significantly, the text is easy to read and follow, however, the description of the novelty is still missing. The authors described very well the used methods, however, the similar solutions and the new manner of the paper are not seen from this description. To highlight these things, it is necessary to describe in details some differences, advantages compared to similar solutions, techniques, or to justify, why they selected the PSO algorithm or the other methodologies. It is not clear, why are novel to use these methodologies in this combination.

Introduction:
The relentless growth of the global population and continuous industrial advancements have led to a steep increase in energy demand, emphasizing the essential role of energy in our modern lives [1-3]. This burgeoning demand, paired with diminishing fossil fuel reserves and escalating environmental concerns, has hastened the transition towards cleaner and more sustainable energy sources such as solar, hydro, and wind energy [4]. Among these, solar and wind energies stand out in the renewable energy sector, with photovoltaic (PV) systems and wind power systems, particularly wind farms, experiencing significant global growth [5-6]. PV systems have revolutionized the conversion of solar energy into electricity, with substantial advancements and cost reductions in solar modules [5], [8, 9]. Similarly, wind energy, particularly through innovations in wind farms using Doubly Fed Induction Generators (DFIGs), is shaping the future of energy due to its efficiency, adaptability, and cost-effectiveness [6, 7]. However, the inherent variability and unpredictability of both solar and wind energy sources pose significant challenges to system stability and efficiency. To optimize these renewable energy sources' efficiency, accurate modeling and effective control mechanisms are indispensable. In PV systems, Maximum Power Point Tracking (MPPT) is crucial for maximizing electricity extraction under various environmental conditions [10-13]. In wind power systems, effectively managing power on both the generator and grid sides is critical, with power converters enabling DFIGs to operate at variable speeds [14-16]. Addressing these challenges, our study introduces a novel hybrid system that synergistically integrates photovoltaic and wind energy systems. Our approach leverages Model Predictive Control (MPC) enhanced by Particle Swarm Optimization (PSO) to efficiently manage the complex dynamics of this integrated system. Unlike traditional methods, MPC's ability to predict system behavior and adapt control actions is coupled with PSO for optimizing the cost function of the controller, ensuring enhanced performance and stability [17-21]. This hybrid system promises improved energy harvesting from both solar and wind sources while addressing their variability challenges. The paper is structured to detail the hybrid energy system's components and operations, with Section 2 introducing the PSO-enhanced MPC strategy. Subsequent sections include a comprehensive overview of the PV system and control methodology (Section 2), evaluation of a grid-tied wind system under varying conditions (Section 3), an in-depth look at the DFIG wind system and PSO control technique (Section 4), and simulation methodology and results (Section 5). The final section (Section 6) synthesizes our key findings and discusses potential future research directions.
Key Contributions:

1. Development of an innovative hybrid solar and wind energy system, distinct in its use of MPC combined with PSO. This approach is novel in its ability to address the unpredictable nature of renewable energy sources, a gap in existing methodologies.
2. Application of Lyapunov's theorem for rigorous stability analysis, providing a mathematical validation of our system's stability, a feature often overlooked in similar hybrid systems.
3. Comprehensive MATLAB simulations demonstrating the system's resilience and adaptability to changing environmental conditions, confirming its practicality and efficiency in renewable energy integration.

Dear Reviewer,
We are writing to express our sincere appreciation for your valuable insights and constructive critique regarding our manuscript. Your detailed review and thoughtful suggestions have played a crucial role in enhancing the overall quality and coherence of our work. We are particularly grateful for the time and effort you invested in examining our paper. Your expertise in the field is evident in the depth and relevance of your comments, which have helped us address key areas that needed improvement. Your suggestions have not only enriched our research but also guided us in presenting our results and conclusions with greater clarity and precision. Your feedback has been a source of inspiration and learning for us. It has encouraged us to delve deeper into our topic and refine our approach, ensuring that our work contributes meaningfully to our field of study. We would like to thank you once again for your thorough and insightful review. Your contribution as a reviewer has significantly improved our manuscript and has been instrumental in our journey towards academic excellence.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This is a resubmitted paper. In comparison to the previous version, the authors have made some improvements. However, some of the comments remain unaddressed. Please refer to my comments below:

1)      Abstract: Rewrite the abstract to concisely summarize the research motivation, identify the research gap, outline the methodology, and highlight key findings.

2)      Introduction: Include a section towards the end of the introduction that outlines the key contributions of this paper.

3)      Include a table that comprehensively lists the simulation parameters (current tables cover only the PV module and some parameters for the DFIG). What about the remaining parameters (e.g., all control parameters of DFIG and others)?

4)      Most of the figures are blurred and have low-resolution quality. Please improve.

Comments on the Quality of English Language

N/A.

Author Response

Response to Reviewer 2 Comments

 

This is a resubmitted paper. In comparison to the previous version, the authors have made some improvements. However, some of the comments remain unaddressed. Please refer to my comments below:

• Abstract: Rewrite the abstract to concisely summarize the research motivation, identify the research gap, outline the methodology, and highlight key findings.

Abstract:
In response to the escalating global energy crisis, this research is motivated by the need for sustainable and efficient energy solutions. Recognizing the gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, this study introduces a novel hybrid system combining photovoltaic (PV) and wind energy. The innovation lies in our methodological approach, which integrates dynamic modeling with a sophisticated control mechanism. This mechanism is a blend of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO), specifically designed to address the fluctuations inherent in PV and wind power sources. Our methodology involves a detailed stability analysis using Lyapunov's theorem, a critical step that differentiates our system from conventional renewable energy solutions. The integration of MPC and PSO is pivotal in enhancing the system's adaptability and optimizing the Maximum Power Point Tracking (MPPT) process, thereby improving control efficiency across key components like the Doubly Fed Induction Generator (DFIG), Rectifier-Sourced Converter (RSC), and Grid-Side Converter (GSC). Through rigorous MATLAB simulations, we demonstrate the system's robust response to changing solar irradiance and wind velocities. The key findings confirm the system's ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. This study not only fills a crucial gap in renewable energy control systems but also sets a precedent for future research in sustainable energy technologies.

• Introduction: Include a section towards the end of the introduction that outlines the key contributions of this paper.

Introduction:
The relentless growth of the global population and continuous industrial advancements have led to a steep increase in energy demand, emphasizing the essential role of energy in our modern lives [1-3]. This burgeoning demand, paired with diminishing fossil fuel reserves and escalating environmental concerns, has hastened the transition towards cleaner and more sustainable energy sources such as solar, hydro, and wind energy [4]. Among these, solar and wind energies stand out in the renewable energy sector, with photovoltaic (PV) systems and wind power systems, particularly wind farms, experiencing significant global growth [5-6]. PV systems have revolutionized the conversion of solar energy into electricity, with substantial advancements and cost reductions in solar modules [5], [8, 9]. Similarly, wind energy, particularly through innovations in wind farms using Doubly Fed Induction Generators (DFIGs), is shaping the future of energy due to its efficiency, adaptability, and cost-effectiveness [6, 7]. However, the inherent variability and unpredictability of both solar and wind energy sources pose significant challenges to system stability and efficiency. To optimize these renewable energy sources' efficiency, accurate modeling and effective control mechanisms are indispensable. In PV systems, Maximum Power Point Tracking (MPPT) is crucial for maximizing electricity extraction under various environmental conditions [10-13]. In wind power systems, effectively managing power on both the generator and grid sides is critical, with power converters enabling DFIGs to operate at variable speeds [14-16]. Addressing these challenges, our study introduces a novel hybrid system that synergistically integrates photovoltaic and wind energy systems. Our approach leverages Model Predictive Control (MPC) enhanced by Particle Swarm Optimization (PSO) to efficiently manage the complex dynamics of this integrated system. Unlike traditional methods, MPC's ability to predict system behavior and adapt control actions is coupled with PSO for optimizing the cost function of the controller, ensuring enhanced performance and stability [17-21]. This hybrid system promises improved energy harvesting from both solar and wind sources while addressing their variability challenges. The paper is structured to detail the hybrid energy system's components and operations, with Section 2 introducing the PSO-enhanced MPC strategy. Subsequent sections include a comprehensive overview of the PV system and control methodology (Section 2), evaluation of a grid-tied wind system under varying conditions (Section 3), an in-depth look at the DFIG wind system and PSO control technique (Section 4), and simulation methodology and results (Section 5). The final section (Section 6) synthesizes our key findings and discusses potential future research directions.
Key Contributions:

1. Development of an innovative hybrid solar and wind energy system, distinct in its use of MPC combined with PSO. This approach is novel in its ability to address the unpredictable nature of renewable energy sources, a gap in existing methodologies.
2. Application of Lyapunov's theorem for rigorous stability analysis, providing a mathematical validation of our system's stability, a feature often overlooked in similar hybrid systems.
3. Comprehensive MATLAB simulations demonstrating the system's resilience and adaptability to changing environmental conditions, confirming its practicality and efficiency in renewable energy integration.

• Include a table that comprehensively lists the simulation parameters (current tables cover only the PV module and some parameters for the DFIG). What about the remaining parameters (e.g., all control parameters of DFIG and others)?

 

-Again, thank you so much for your insightful feedback; it has been changed.

 

• Most of the figures are blurred and have low-resolution quality. Please improve.
  
  -Thank you very much again for your valuable comment, we modified it.

 

 

 

Dear Reviewer,
We are writing to express our sincere appreciation for your valuable insights and constructive critique regarding our manuscript. Your detailed review and thoughtful suggestions have played a crucial role in enhancing the overall quality and coherence of our work. We are particularly grateful for the time and effort you invested in examining our paper. Your expertise in the field is evident in the depth and relevance of your comments, which have helped us address key areas that needed improvement. Your suggestions have not only enriched our research but also guided us in presenting our results and conclusions with greater clarity and precision. Your feedback has been a source of inspiration and learning for us. It has encouraged us to delve deeper into our topic and refine our approach, ensuring that our work contributes meaningfully to our field of study. We would like to thank you once again for your thorough and insightful review. Your contribution as a reviewer has significantly improved our manuscript and has been instrumental in our journey towards academic excellence.

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

I would like to express my complete satisfaction with the revisions and modifications implemented by the authors. After a careful review of the paper, I believe it is now in a highly suitable and polished state for acceptance. Therefore, I kindly request that the paper be accepted in its present form without further alterations.

Comments on the Quality of English Language

I am pleased with the comprehensive revisions made by the authors in response to my earlier comments. At this point, I believe no further adjustments are necessary. The journal's publishing team can effectively address any remaining concerns or issues during the final proofreading stage.

Author Response

Response to Reviewer 3 Comments

I would like to express my complete satisfaction with the revisions and modifications implemented by the authors. After a careful review of the paper, I believe it is now in a highly suitable and polished state for acceptance. Therefore, I kindly request that the paper be accepted in its present form without further alterations.

Dear Reviewer,

We are writing to express our profound gratitude for your insightful and constructive feedback on our manuscript. Your thorough review and encouraging comments have been instrumental in enhancing the quality and clarity of our work. We deeply appreciate the time and effort you devoted to reviewing our paper. Your expertise and keen attention to detail shone through in your comments, guiding us to not only refine our research but also to present our findings in a more effective manner. Your recent communication, acknowledging your complete satisfaction with our revisions and recommending the paper's acceptance, is incredibly gratifying. It is immensely rewarding to know that our efforts to address your concerns have culminated in a manuscript that meets your esteemed standards. We are particularly grateful for your recommendation to accept the paper in its present form. This endorsement is not only a significant achievement in our research journey but also a strong motivator for us to continue striving for academic excellence. Please accept our sincerest thanks for your invaluable role in strengthening our work. Your contribution as a reviewer has been vital in making our research more robust and impactful. Thank you once again for your invaluable support and guidance.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

 Thank you for your answers, I can accept them, but please improve the qualiy of the following images like figure 10, figure 16, figure 17, figure 18.

Author Response

Response to Reviewer 1 Comments

Dear Authors,

 Thank you for your answers, I can accept them, but please improve the qualiy of the following images like figure 10, figure 16, figure 17, figure 18.

Dear Reviewer,
We would like to express our sincere gratitude for the time and effort you have dedicated to reviewing our manuscript. Your insights and suggestions have been invaluable in guiding us to enhance the quality of our work.

In particular, we appreciate your constructive comments regarding the quality of certain figures in our manuscript, specifically figures 10, 16, 17, and 18. We have taken your feedback seriously and are pleased to inform you that we have thoroughly revised these images. Our revisions focused on improving clarity, resolution, and overall presentation to ensure that these figures effectively complement and elucidate the textual content of our paper.

We believe that these modifications have significantly improved the visual appeal and informational value of the figures, thereby enriching the reader's understanding of our research findings. Our aim was to address your concerns while maintaining the integrity and accuracy of the data represented.

The revised manuscript, including the updated figures, has been resubmitted for your review. We hope that these changes meet your expectations and further the manuscript's suitability for publication.

Thank you once again for your invaluable contribution to our work. Your meticulous review and insightful suggestions have greatly assisted us in refining our manuscript to meet the high standards of the journal.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript is a revised submission. The authors have implemented certain enhancements compared to the previous version. Nevertheless, there are still some outstanding comments/suggestions as below:

 

1)      "Include a table that comprehensively lists the simulation parameters (current tables cover only the PV module and some parameters for the DFIG). What about the remaining parameters (e.g., all control parameters of DFIG and others)?". Table 3 only shows some parameters; what about others (e.g., 1) Kp & Ki for voltage, power, and current controllers, 2) switching frequency, 3) sampling frequency, …)?

2)      Improve the writing of the first three sentences in the abstract.

3)      Strictly use the passive voice in the entire paper, especially in the abstract.

4)      Avoid long paragraphs (e.g., see the introduction).

5)      Towards the end of the “1. Introduction” section, add a paragraph outlining how the rest of the paper is structured.

Comments on the Quality of English Language

N/A.

Author Response

Response to Reviewer 2 Comments

 

This manuscript is a revised submission. The authors have implemented certain enhancements compared to the previous version. Nevertheless, there are still some outstanding comments/suggestions as below:

 

• "Include a table that comprehensively lists the simulation parameters (current tables cover only the PV module and some parameters for the DFIG). What about the remaining parameters (e.g., all control parameters of DFIG and others)?". Table 3 only shows some parameters; what about others (e.g., 1) Kp & Ki for voltage, power, and current controllers, 2) switching frequency, 3) sampling frequency, …)?

1) Thanks for your comment, we have added the necessary parameters.

2)      Improve the writing of the first three sentences in the abstract.

3)      Strictly use the passive voice in the entire paper, especially in the abstract.

2&3) Thank you, we modified it.

Abstract: In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov's theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system's adaptability and optimizing the Maximum Power Point Tracking (MPPT) process, improves control efficiency across key components like the Doubly Fed Induction Generator (DFIG), Rectifier-Sourced Converter (RSC), and Grid-Side Converter (GSC). Through rigorous MATLAB simulations, the system's robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system's ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has a crucial gap in renewable energy control systems been filled by this study, but a precedent for future research in sustainable energy technologies has also been set.

4)      Avoid long paragraphs (e.g., see the introduction).

5)      Towards the end of the “1. Introduction” section, add a paragraph outlining how the rest of the paper is structured.

4&5) Thank you, we modified it.

Introduction:
The relentless growth of the global population, alongside continuous industrial advancements, has precipitated a steep increase in energy demand. This surge highlights the pivotal role of energy in our modern lives [1-3]. Concurrently, diminishing fossil fuel reserves and escalating environmental concerns are hastening the transition towards cleaner, more sustainable energy sources like solar, hydro, and wind energy [4].

Solar and wind energies, particularly photovoltaic (PV) systems and wind farms, are experiencing significant growth globally within the renewable energy sector [5-6]. PV systems have transformed the conversion of solar energy into electricity, marked by substantial advancements and cost reductions in solar modules [5, 8, 9]. Similarly, wind energy is shaping the future of sustainable energy through innovations in wind farms using Doubly Fed Induction Generators (DFIGs), known for their efficiency and cost-effectiveness [6, 7].

However, the inherent variability and unpredictability of solar and wind energy sources present significant challenges to system stability and efficiency. To enhance the efficiency of these renewable sources, accurate modeling and effective control mechanisms are crucial. For PV systems, Maximum Power Point Tracking (MPPT) is key to maximizing electricity extraction under various conditions [10-13]. In wind power systems, managing power effectively on both the generator and grid sides is essential. Power converters enable DFIGs to operate at variable speeds, addressing these challenges [14-16].

Our study introduces a novel hybrid system that integrates photovoltaic and wind energy systems. Utilizing Model Predictive Control (MPC) enhanced by Particle Swarm Optimization (PSO), this approach efficiently manages the dynamics of the integrated system. The unique combination of MPC's predictive capabilities with PSO's optimization of the control function promises improved performance and stability [17-21]. This hybrid system aims to enhance energy harvesting from both solar and wind sources, addressing their variability.

The paper is structured as follows: Section 2 introduces the PSO-enhanced MPC strategy, elaborating on its application in the hybrid energy system. Section 3 provides a comprehensive overview of the PV system and its control methodology. Section 4 evaluates a grid-tied wind system under varying conditions, focusing on the DFIG wind system and the PSO control technique. Section 5 discusses the simulation methodology and presents the results. Finally, Section 6 synthesizes our key findings and explores potential future research directions.

Key Contributions:

1. Development of an innovative hybrid solar and wind energy system, utilizing MPC combined with PSO. This novel approach addresses the unpredictability of renewable energy sources, filling a gap in existing methodologies.
2. Application of Lyapunov's theorem for stability analysis, providing mathematical validation of the system's stability, a critical aspect often overlooked in similar hybrid systems.
3. Extensive MATLAB simulations demonstrating the system's resilience and adaptability to changing environmental conditions, confirming its practicality and efficiency in renewable energy integration.

Dear reviewer,
We are writing to extend our deepest gratitude for the time and effort you have invested in reviewing our manuscript. Your insightful remarks and constructive feedback are highly valued and have provided us with a clear direction for improving our work. Your detailed analysis and thoughtful suggestions have highlighted key areas where our manuscript can be enhanced. We are particularly appreciative of the specific points you raised, as they offer us the opportunity to refine our research and present it in a more robust and compelling manner. In light of your valuable feedback, we would like to inform you that we are currently in the process of revising our article. We are committed to addressing each of the concerns and suggestions you have mentioned. Our goal is to ensure that the revised manuscript aligns closely with the high standards of the journal and contributes meaningfully to our field of study. We recognize the importance of your remarks in elevating the quality of our research, and we are dedicated to implementing these changes diligently. We believe that this revision process will not only improve our current manuscript but also guide us in our future research endeavors. Once again, thank you for your invaluable contribution to our work. Your expertise and guidance are greatly appreciated.

Author Response File: Author Response.docx