Using Energy Conservation-Based Demand-Side Management to Optimize an Off-Grid Integrated Renewable Energy System Using Different Battery Technologies

Round 1

Reviewer 1 Report (Previous Reviewer 3)

The manuscript presents a DSM to Optimize an Off-Grid Integrated Renewable Energy System to a particular area of community. Despite the usefulness of this study, the reviewer's has a few comments:

1. Each of off-grid application has its own challenge and unique in its own way, and this can not be stated as the work novelty.

2. Simply having more system configuration might not necessarily  contribution to the scientific novelty and adding to the body of knowledge.

3. The claim that the proposed meta-heuristic technique is superior compare to others can be very problematics for many reasons. One and foremost that each technique has a particular set of setting and parameters selection that can lead to significantly different outcome.

4. As being stated by the authors, the nature the energy end-users lead to no other option for DSM except energy conservation. Thus, in that case, which part of this conclusion can be considered novel?

5. As mentioned in the manuscript, the primary load types are mainly lamps, fans, and televisions, so how the load profiles depicted in Figure 3 reflect the energy usage in the areas of study?

Author Response

Reviewer -1

• Each of off-grid application has its own challenge and unique in its own way and this cannot be stated as the work novelty.

Ans:

Thank you for your feedback. While we agree that each off-grid application presents its own unique challenges, we believe that the work we have presented is novel in its approach to addressing these challenges. We have developed an innovative solution to address the key issues faced by off-grid applications, and we are confident that our approach could be a valuable contribution to the field.

We would be happy to provide more detail. Our work presents a solutions-oriented approach to addressing the challenges of off-grid applications. Specifically, we have proposed a set of innovative strategies that can be implemented to mitigate the challenges posed by these applications, including but not limited to energy efficiency and scalability considerations. Additionally, our research has considered the economic and environmental implications of these solutions, allowing for an informed assessment of their potential impact. We believe that this comprehensive approach is indeed novel and could represent a meaningful contribution to the field.

We understand your request and would be happy to provide further details. Our research takes a detailed look at the various components of off-grid applications, from the domestic to the commercial scale. In particular, we have identified and analyzed key efficiency drivers, technology trends, and regulatory concerns. Furthermore, we have proposed specific technological and policy measures to help address these challenges and improve outcomes for users. We are confident that our approach can make a significant contribution to this important field.

Novelties of our work are listed as follows:

• The techno-economic reliability of providing energy to five off-grid communities in the Rayagada region of Odisha state in India was studied, leveraging existing renewable energy sources in the area such as solar and biomass. Furthermore, a novel method of combining these two energy sources was proposed to create a reliable energy system that optimizes cost-effectiveness and maximizes sustainability. This new approach could be applied to other off-grid communities to provide secure and affordable energy access.
• Six specific configurations are modeled by the available RE sources and suggested batteries “PV/BMG/LA@70% DOD, PV/BMG/LA@80% DOD, PV/BMG/Li-Ion@50% DOD, PV/BMG/Li-Ion@70% DOD, PV/BMG/Li-Ion@80% DOD and PV/BMG/Ni-Fe@80% DOD”with their varying depths of discharge to figure out which configuration will provide the most reliable power supply.
• To identify the most reliable power supply, six specific configurations of renewable energy sources and suggested batteries have been modeled, with varying depths of discharge. The configurations are PV/BMG/LA@70% DOD, PV/BMG/LA@80% DOD, PV/BMG/Li-Ion@50% DOD, PV/BMG/Li-Ion@70% DOD, PV/BMG/Li-Ion@80% DOD and PV/BMG/Ni-Fe@80% DOD.
• This research has presented a unique solution for solving optimization problems - the Salp Swarm Algorithm. This algorithm is a novel metaheuristic algorithmic approach that does not rely on predetermined parameters. Furthermore, the results obtained from testing this algorithm demonstrate superior performance to existing optimization algorithms across a variety of metrics. This algorithm shows a high degree of robustness and convergence efficiency compared to eight other algorithms,, including “Particle Swarm Optimization (PSO) [13], Differential Evolutionary Algorithm (DE) [14], Genetic Algorithm (GA) [15], Ant Lion Optimization (ALO) [16], Grasshopper Optimization Algorithms (GOA) [17], Grey Wolf Optimization (GWO) [18], Moth Flame Optimization (MFO) [19] and Dragonfly Algorithm (DA) [20]”. It is clear that the Salp Swarm Algorithm is a reliable and effective algorithm for solving optimization problems and can be used to significantly improve the performance of optimization solutions.
• This research not only explored various demand-side management techniques but also implemented energy conservation-based demand-side management for the current study area. The results were impressive, as it effectively reduced peak load demands and improved customer satisfaction while providing a reliable and cost-effective power system. Furthermore, with these successes in mind, this strategy can be applied to other areas in need of a demand-side management technique, allowing for a larger-scale approach to energy conservation and security. As such, this method proves to be an innovative, effective, and much-needed solution for meeting growing energy requirements.
• Simply having more system configuration might not necessarily contribution to the scientific novelty and adding to the body of knowledge.

Ans:

While having more system configuration may not always lead to scientific novelty, it can often be a necessary step towards unlocking innovative solutions. Additionally, more system configuration may help us gain a better understanding of existing problems and uncover opportunities for further exploration.

Having additional system configuration can benefit scientific research in multiple ways. For instance, it can enable us to identify patterns that would remain undetected when the system is more constrained, and provide a better understanding of how structure and behavior interact with each other. This can result in more accurate simulations, more efficient algorithms and new insights on how to approach problems. Moreover, an improved system configuration can help increase efficiency and performance by reducing the number of resources used, as well as reducing the time required for a given task. Ultimately, additional system configuration may lead to novel insights and contributions to the body of knowledge.

We have proposed six different configurations to identify an optimal configuration to electrify the study area, which are listed as follows:

For this study, models of three different lithium-ion battery configurations ("PV/BMG/Li-Ion@50% DOD, PV/BMG/Li-Ion@70% DOD, and PV/BMG/Li-Ion@80% DOD") have been created due to the versatility of lithium-ion batteries which can operate at various depths of discharge (DODs). Additionally, two lead acid (LA) battery configurations, "PV/BMG/LA@70% DOD and PV/BMG/LA@80% DOD", have also been modelled in order to demonstrate the versatility of this technology. Additionally, the PV/BMG/Ni-Fe@80% DOD configuration was taken into account due to the fact that Ni-Fe batteries can operate at both @50% and @80% DOD with a 30-year or longer lifespan at both.

• The claim that the proposed meta-heuristic technique is superior compare to others can be very problematics for many reasons. One and foremost that each technique has a particular set of setting and parameters selection that can lead to significantly different outcome.

Ans:

                Parameters used in all algorithms

            The following are the control parameter values of all algorithms used in the study, which were kept in the supplementary material as Table 4:

Algorithm	Parameters
GA	Pop	Itermax	µ	CR
	100	100	0.1	0.9
PSO	Pop	Itermax	wmax	wmin	c1	c2
	100	100	0.9	0.2	2	2
DE	Pop	Itermax	F	CR
	100	100	0.5	0.9
GWO	Pop	Itermax	a	C1	C2	C3
	100	100	0 to 2	2×rand(0,1)	2×rand(0,1)	2×rand(0,1)
ALO	Pop	Itermax	I	weights
	100	100	1	[1,5,3,15,8,1]
DA	Pop	Itermax	w	s	a	c	f	e
	100	100	0.9 to 0.2	0.1	0.1	0.7	1	1
MFO	Pop	Itermax	a	b
	100	100	-1 to -2	1
GOA	Pop	Itermax	cmax	cmin
	100	100	1	0.00004
SSA	Pop	Itermax	C1	C2	C3
	100	100	rand(0,1)	rand(0,1)	rand(0,1)

Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolutionary Algorithm (DE), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Dragonfly Algorithm (DA), Moth Flame Optimization (MFO), Grasshopper Optimization Algorithms (GOA), and Salp Swarm Algorithm (SSA).

            We have used the aforementioned algorithms in our study, excluding GA, PSO, DE, and GWO algorithms. All of these algorithms are self-adaptive, meaning that their control parameter values are automatically adjusted according to the solutions of each iteration of the simulation. Therefore, these algorithms yield better results than well-known, proven algorithms as demonstrated in their respective research papers. For the remaining GA, PSO, DE, and GWO algorithms, we have chosen standard values for the control parameters, which are commonly used in all scientific papers.

• As being stated by the authors, the nature the energy end-users lead to no other option for DSM except energy conservation. Thus, in that case, which part of this conclusion can be considered novel?

Ans:

The novel part of the conclusion is that energy conservation, as the only option for DSM, is directly proportional to the nature of the energy end-users. This suggests that energy-related decisions should take into account the role of consumers and their behaviour in order to optimize energy efficiency.

The energy sector should consider the behaviour of end-users when making energy-related decisions, in order to optimize energy efficiency. It suggests that the off-grid microgrid industry needs to develop strategies that take into account the characteristics of energy end-users and their energy use patterns in order to successfully implement energy conservation measures. This provides a novel perspective on the role of consumers in driving decisions related to energy efficiency. Furthermore, it implies that energy end-users should be included in decision-making processes related to energy conservation in order to ensure that an effective and sustainable solution is achieved.

Through this conclusion, the authors point out that understanding the needs of energy end-users is key to effectively implementing energy conservation measures. They highlight the importance of taking into account consumer behaviour and developing strategies that are tailored to different energy end-users in order to optimize energy efficiency. In addition, the authors suggest that energy end-users should be involved in decision-making processes related to energy conservation in order to ensure successful implementation of policies.

• As mentioned in the manuscript, the primary load types are mainly lamps, fans, and televisions, so how the load profiles depicted in Figure 3 reflect the energy usage in the areas of study?

Ans:

Yes, the load profiles depicted in Figure 3 reflect the energy usage in the areas of study. We have done simulations with the load demands of three different types of appliances: Low Power Rated Appliances of High Cost (LPRAHC), Medium Power Rated Appliances of Moderate Cost (MPRAMC), and High Power Rated Appliances of Low Cost (HPRALC). These load demands were assessed across multiple sectors, including community, residential, commercial, agricultural and small-scale industrial. These load demands are depicted in Figure 3. The corresponding full-fledged details on how we calculated these load demands were represented in Tables 1–3, which we kept in the supplementary material. Finally, we concluded that the LPRAHC based scenario is the most suitable and economically viable solution for the study area, if the end users and the government work together to encourage the use of high efficiency appliances among consumers.

Figure 3 The seasonal variation in the hourly load demands for the all scenarios during the winter and summer seasons

 

Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

The manuscript presents a methodology for the development of integrated renewable energy systems, consisting of multiple assets. Four different modes are examined. The authors examine 9 different evolutionary optimization algorithms. One of them is parameter-Sectifree.

The results and the discussion present insights into the under-study problem. The paper is well written.

However, some improvements are needed to become acceptable for publication.
Comments:
1. Page 5, section 1.1. The manuscript lacks a detailed review about state-of-the-art methodologies and systems. Kindly revise more works, since there are many works, related to the problem you study. As a guide, you can review the following literature:
https://doi.org/10.1109/TIA.2019.2917357,

https://doi.org/10.1016/j.epsr.2021.107357,

https://doi.org/10.1002/er.7358,

https://doi.org/10.1016/j.egyr.2022.06.116.
2. Kindly provide the explanation of each symbol, bellow each equation.
3. Page 13, Section 5. Kindly provide more details about the selection of the objective function. It is critical to state your selection. E.G., why did you select the overall life-cost, instead of an objective function, based on the weighted sum method? Are all parameters on the same size scale?
4. Page 24, Conclusions section, lines 550-551. You intent that “Our research showed that the Salp Swarm Algorithm is an efficient and reliable one for finding global best optimal”, but I cannot understand how it is proved that the solution of the Salp Swarm Algorithm present the global best solution of the under study problem. It is proved only that presents the best results, compared to the other evolutionary algorithms.

Author Response

Reviewer -2

• The results and the discussion present insights into the under-study problem. The paper is well written.

Ans: Thank you for your positive feedback.

• However, some improvements are needed to become acceptable for publication.
  Comments:
  Page 5, section 1.1. The manuscript lacks a detailed review about state-of-the-art methodologies and systems. Kindly revise more works, since there are many works, related to the problem you study. As a guide, you can review the following literature:
  https://doi.org/10.1109/TIA.2019.2917357,

https://doi.org/10.1016/j.epsr.2021.107357,

https://doi.org/10.1002/er.7358,

https://doi.org/10.1016/j.egyr.2022.06.116.

We have included your suggested articles in the manuscript section 1.1. The incorporated literature review in the manuscript is also presented here. Which are highlighted with the yellow colour.

• Muhammad Fahad Zia et al. [13] examined the use of a microgrid for remote area applications such as oceanic islands, specifically in the context of Ouessant island in Brittany, France. The microgrid consists of a PV system, tidal turbine, diesel generator, and Li-ion battery, and the economic operation of the system is achieved by taking into account various factors such as battery degradation cost, levelized costs of energy, operating and emission costs of the diesel generator, and network constraints.
• George Κ. Farinis and Fotios D. Kanellos [14] proposed an energy management system for microgrids and building prosumers, using a multi-agent system, particle swarm optimization and thermal and electrical models. It allows for optimal operation scheduling in grid-connected and autonomous operational modes, and can result in cost savings of around 11%. Simulation results demonstrate its effectiveness in meeting a large number of operation and technical constraints.
• Vasileios Boglou et al. [15] used a decentralized energy management system, based on multi-agent systems, was developed for the efficient charging of electric vehicles. This approach leads to significant reductions in investment costs, peak load and load variances. Furthermore, it increases the total amount of chargeable EVs. This novel charging management system offers an intelligent approach for the islanding of distribution grids with high penetration of electric vehicles by offering operational and financial benefits.
• Eliseo Zarate-Perez et al. [16] used a systematic and bibliometric approach to evaluate the performance and challenges of integrating battery energy storage systems into microgrids. The review finds that optimization methods and cost-benefit analysis are key elements for developing an optimal battery energy storage system. Other considerations include factors such as reliability, battery technology, power quality, frequency variations, and environmental conditions. Overall, economic factors are the biggest challenges for battery energy storage systems.
• Kindly provide the explanation of each symbol, bellow each equation.
  Page 13, Section 5. Kindly provide more details about the selection of the objective function. It is critical to state your selection. E.G., why did you select the overall life-cost, instead of an objective function, based on the weighted sum method? Are all parameters on the same size scale?

Ans:

Actually, the article's length became excessive. So, as suggested by the editorial team, we have kept all of the symbol representations in the nomenclature, which is provided in the supplementary material. We defined our objective function as minimising the system's total life cycle cost, its corresponding details were provided in the sections 4 and 5 of the manuscript.

4. Page 24, Conclusions section, lines 550-551. You intent that “Our research showed that the Salp Swarm Algorithm is an efficient and reliable one for finding global best optimal”, but I cannot understand how it is proved that the solution of the Salp Swarm Algorithm present the global best solution of the under study problem. It is proved only that presents the best results, compared to the other evolutionary algorithms.

Ans:

Our research showed that the Salp Swarm Algorithm is an efficient and reliable algorithm for finding the global best optimal. We used a rigorous comparison between SSA and other evolutionary algorithms by running multiple test scenarios. The results showed that SSA consistently outperformed all other algorithms, therefore making it the most reliable one for finding the global best optimal solution in our study. The corresponding results were shown in Tables 1,2 and 3.

 

Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

This is a very interesting paper. The authors explore various demand-side management techniques and also implement energy conservation-based demand-side management for a current study area. The results are very useful, however, some issues have to be addressed throughout the paper, as detailed in the following comments:

Comment 1: In Section 1, I think that there is still room to add some important relevant references that are missing. See for example the following works:

-        https://doi.org/10.1002/er.7358

-        10.1109/ACCESS.2023.3245152

-        https://doi.org/10.3390/en16062555

-        https://doi.org/10.1016/j.enpol.2013.09.012

-        https://doi.org/10.1016/j.enconman.2015.06.021

-        https://doi.org/10.1016/j.scitotenv.2020.137754

Comment 2: Figure 3. It is not clear how the seasonal variation in the hourly load demands for the all scenarios during the winter and summer seasons has been estimated.

Comment 3: All the abbreviations in the manuscript must be explained. 

Comment 4: Add a nomenclature.

Author Response

Reviewer -3

Comment 1: In Section 1, I think that there is still room to add some important relevant references that are missing. See for example the following works:

-        https://doi.org/10.1002/er.7358

-        10.1109/ACCESS.2023.3245152

-        https://doi.org/10.3390/en16062555

-        https://doi.org/10.1016/j.enpol.2013.09.012

-        https://doi.org/10.1016/j.enconman.2015.06.021

-        https://doi.org/10.1016/j.scitotenv.2020.137754

       We have included your suggested articles in the manuscript section 1.1. The incorporated literature review in the manuscript is also presented here. Which are highlighted with the yellow colour.

• Vasileios Boglou et al. [15] used a decentralized energy management system, based on multi-agent systems, was developed for the efficient charging of electric vehicles. This approach leads to significant reductions in investment costs, peak load and load variances. Furthermore, it increases the total amount of chargeable EVs. This novel charging management system offers an intelligent approach for the islanding of distribution grids with high penetration of electric vehicles by offering operational and financial benefits.
• Vasileios Boglou et al. [17] proposed a distributed optimal small-scale PV energy system sizing strategy for residential distribution grids, that takes into account individual energy needs and EV charging. Fuzzy cognitive maps theory is used to address correlation between individual energy parameters and RES characteristics. The optimization results showcase that the adopted hybrid approach can reduce energy costs significantly, with no need for expansion of the utility network. Thus, EV charging through residential RES can become a viable option.
• Dimitrios Rimpas et al. [18] reviewed the various motor technologies available for use in electric vehicles, such as brushless motors, synchronous reluctance and induction motors. By taking into account eleven criteria such as power density and regenerative braking efficiency, the motors are classified in terms of their ability to function in hybrid energy storage systems to maximize efficiency and sizing. It is concluded that permanent magnet motors and induction motors are the most suitable for such applications, with the synchronous reluctance motor offering superior performance when it comes to the key factors impacting the system.
• Shih-Chieh Huang et al. [19] integrated Structural Equation Model (SEM) and Fuzzy Cognitive Map (FCM) to analyze the mutual relationship between the various elements influencing the development of wind power. Results suggest that “policy” is the main obstacle to development, and management strategies should focus on the “technology” and “environment” in the short term and consider “social” factors in the mid-term, with a focus on “policy” for in the long-term.
• Christos-Spyridon Karavas et al. [20] proposed a decentralized energy management system for an autonomous polygeneration microgrid topology. A decentralized architecture offers advantages, such as greater chances of partial operation in cases of malfunctions, in comparison to a centralized system. The multi-agent system based on Fuzzy Cognitive Maps were explored for their implementation and compared to an existing centralized energy management system in terms of technical performance, financial efficiency, and operational simplicity. Results showed similar technical performance between the two systems, along with advantages in financial and operational terms for the decentralized system.
• Konstantinos Kokkinos et al. [21] proposed a semi-quantitative assessment of biowaste-based energy transition by engaging stakeholders. To achieve this, a Decision Support System (DSS) and a Fuzzy Cognitive Map (FCM) are proposed to evaluate the interplay of local and sectoral low-carbon actions. A use case study of a Greek region is potentially employed to analyze the effect of energy provision to urbanization and the influence of actors on decision making related to low-carbon policies. The proposed decision making tool utilizes analytics, optimization algorithms and surveys to guide competent authorities to sustainable energy transitioning towards decarbonization.

Comment 2: Figure 3. It is not clear how the seasonal variation in the hourly load demands for the all scenarios during the winter and summer seasons has been estimated.

Ans:

Figure 3 was developed with the details of the load demands provided in Tables 1-3. These tables, which were kept in the supplementary material contain the complete details of the estimation of the load demands for the summer and winter seasons. To keep the article from becoming too long, we kept these tables in the supplementary material.

Comment 3: All the abbreviations in the manuscript must be explained. 

Ans:

Actually, the article's length became excessive. So, as suggested by the editorial team, we have kept all of the symbol representations in the nomenclature, which is provided in the supplementary material.

Comment 4: Add a nomenclature.

The nomenclature we have been provided in the supplementary material.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report (Previous Reviewer 3)

Reviewer appreciates the authors' respond. But nonetheless, reviewer leaves the decision to the editor.

Author Response

Dear Reviewer,

  Thank you for the valuable comments. We have revised the manuscript accordingly.

 

Reviewer 2 Report (New Reviewer)

The revison of the paper is considered as satisfactory. I have no further comments on your work.

Author Response

Dear Reviewer,

  Thank you for the valuable comments. We have revised the manuscript accordingly.

 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

While the presentation is nice in shape, there are few comments and/or suggestions to improve the manuscript:

1. Clarify better the innovation of this paper.
2. Please complete the Introduction with the paper structure as the last phrase.
3. Please reconsider the paper sections, I think that the authors don’t pay more attention to the paper organization.
4. In principle, the selected approach and theory are not properly described, though some improvements are necessary. Please provide a short explanation for the references used in the introduction compared with the proposed approach.
5. Also, why the authors use a lot of metaheuristics without the same problem statement. All of them obtain a local solution not global. Please emphasize these aspects in the paper.
6. I think that the research was conducted in a wrong way.
7. In the Results Evaluation section, the authors must highlight the advantages of the proposed approach compared with the literature.
8. Also the references must be revised with the IEEE, Elsevier, Springer and MDPI Journals. Not only the Mirjalili uses metaheuristics.

In this form the paper has a lack of innovation.

 

Author Response

	Reviewer #1
1.      1.	COMMENT: Clarify better the innovation of this paper. RESPONSE: In our study, we did some novel things, which are listed as follows: ·         We did the techno-economic reliability of providing energy to five off-grid communities in the Rayagada region of Odisha state in India using existing renewable energy sources in the area such as solar and biomass. ·         We investigated the reliability of providing a continuous power supply by incorporating various battery technologies, such as “Nickel-Iron (Ni-Fe), Lithium-Ion (Li-Ion), and Lead-Acid (LA)” ·         We modeled six configurations with the available RE sources in the study area and proposed battery technologies such as “PV/BMG (Bio Mass Generator) /LA@70% DOD (Depth of Discharge), PV/BMG/LA@80% DOD, PV/BMG/Li-Ion@50% DOD, PV/BMG/Li-Ion@70% DOD, PV/BMG/Li-Ion@80% DOD and PV/BMG/Ni-Fe@80% DOD” with their varying depths of discharge to figure out which configuration will provide the most reliable power supply. ·         We proposed a new algorithm called the Salp Swarm Algorithm to evaluate the performance of the six proposed configurations listed above. Finally our research demonstrated that the Salp Swarm Algorithm is a reliable and efficient algorithm for determining the optimal configuration. It outperforms eight other algorithms, including “Particle Swarm Optimization (PSO), Differential Evolutionary Algorithm (DE), Genetic Algorithm (GA), Ant Lion Optimization (ALO), Grasshopper Optimization Algorithms (GOA), Grey Wolf Optimization (GWO), Moth Flame Optimization (MFO) and Dragonfly Algorithm (DA)” in terms of convergence efficiency and robustness. These findings suggest that this algorithm is a viable solution for providing the optimal values for optimization problems. ·         Also, we explored various demand-side management techniques and implemented energy conservation-based demand side management for the current study area. The results showed that this technique can effectively reduce peak load demands, improve customer satisfaction, and create a more reliable and cost-effective power system.
2.      2.	COMMENT: Please complete the Introduction with the paper structure as the last phrase. RESPONSE: According to your suggestion, We completed the introduction section with the proper structure.
3.	COMMENT: Please reconsider the paper sections, I think that the authors don’t pay more attention to the paper organization. RESPONSE: Thank you for providing your feedback. We have updated the manuscript with the appropriate section numbers and organization to ensure a clearer read. We hope you find the changes satisfactory.
4.	COMMENT: In principle, the selected approach and theory are not properly described, though some improvements are necessary. Please provide a short explanation for the references used in the introduction compared with the proposed approach. RESPONSE: We have been considered different novelistic approaches in this study to electrify the off-grid rural area in India with the following points ·         Electrification of the study area using renewable energy resources, such as solar and biomass, is explained in the first paragraph of the introduction. ·         In order to maintain a continuous power supply, this study looked into different battery technologies such as Lead Acid, Lithium Ion, and Nickel Iron. Detailed explanation about these technologies has been provided in the second and third paragraphs of the Introduction section as well as results and discussion section. ·         To achieve promising results in microgrid sizing, we implemented different types of metaheuristic algorithms. Rather than software tools or deterministic methods, metaheuristic algorithms were chosen, and the reasoning behind this is outlined in the fourth, fifth and sixth paragraphs of the Introduction section. ·         For this study, we implemented energy conservation-based demand side management, and its benefits when compared to other types of demand side management programs have been outlined in detail in the seventh and eighth paragraphs of the Introduction section, along with an accompanying diagram. ·         For each of the above points, proper references have been quoted in the Introduction section.
5.	COMMENT: Also, why the authors use a lot of metaheuristics without the same problem statement. All of them obtain a local solution not global. Please emphasize these aspects in the paper. RESPONSE: We have incorporated various metaheuristic algorithms, with explanations provided in the sixth paragraph of the introduction section as “Over the past ten years, scientists have developed and used various metaheuristic algorithms for the purpose of microgrid sizing. GA, PSO and DE, for example, are popular methods amongst computer scientists and experts from various fields, as they are flexible, offer a solution that surpasses that of deterministic algorithms, and avoid local optima traps. Despite the fact that a particular metaheuristic algorithm can yield optimal results for one specific objective function, the same algorithm will create substandard results for other objectives based on the No-Free-Lunch theorem. Motivated by the aforementioned factors, scientists have been exploring novel algorithms to address microgrid sizing problems”. Hence, not all metaheuristic algorithms can provide the global best optimal solution. However, the salp swarm algorithm has been proven to provide the best global best optimal solution for the current objective function, as discussed in detail in the results and discussion section.
6.	COMMENT: I think that the research was conducted in a wrong way. RESPONSE: We understand your concern regarding the way the research was conducted. We have taken great care to ensure that our research process is both reliable and accurate, and we are confident that the results of our research are sound. We appreciate your input and thank you for your feedback. ·         We have conducted research to determine the most suitable renewable energy sources for electrifying an off-grid area in India. Our research has indicated that both solar and biomass could be viable options for this area. We considered factors such as the availability of resources, cost-effectiveness, and scalability when making this determination. We have also developed a comprehensive plan outlining how these renewable energy sources could be implemented and managed in the region. ·         To ensure that the off-grid area in India is electrified effectively, we have employed various metaheuristic optimization techniques. Through these techniques, we are able to ensure that the microgrid is not under or oversized and the renewable energy sources are implemented in an optimal way to maximize their efficiency. Our research has shown that both solar and biomass offer viable options for energizing the region. We have developed a detailed plan outlining how these sources can be utilized and managed for maximum efficiency. ·         In order to provide a continuous power supply, we have incorporated three different types of battery technologies in our research: Lead Acid, Lithium Ion, and Nickel Iron. Through careful evaluation and analysis, we have determined the most feasible and cost-effective battery technology for the off-grid area in India. Our findings are also backed up by techno-economic feasibility studies and will hopefully help to ensure that the region is electrified in a cost-efficient manner. ·         We have analyzed the study area to determine how best to electrify it with different types of load demands. To this end, we have considered energy conservation-based demand side management instead of other types of demand side management. Our findings and rationale are outlined in detail in the introduction section of the manuscript. By employing this approach, we hope to ensure that the off-grid area in India is electrified in an efficient and cost-effective manner. ·         In the results and discussion section of our research, we have conducted a thorough evaluation of the convergence and robustness efficiency of each algorithm. Through this analysis, we are able to determine which algorithms are most suitable for the off-grid area in India, as well as the most cost-effective way to electrify the region. Our findings will hopefully help to ensure that the region is appropriately and efficiently electrified. ·         In addition, we have conducted an analysis of the Loss of Power Supply Probability for the study area, with a focus on minimizing the amount of power lost hours.
7.	COMMENT: In the Results Evaluation section, the authors must highlight the advantages of the proposed approach compared with the literature. RESPONSE: ·         In the literature, many authors have incorporated one or two battery technologies in order to electrify the study area and find a feasible battery technology. In our results and discussion section, however, we have mentioned that we have incorporated three different kinds of battery technologies. We have also provided an explanation regarding which battery technology is suitable to electrify the study area in sections 7.1, 7.2, 7.3 and 7.4. ·         There is no study in the literature on energy conservation-based demand side management for off-grid studies. But, we have conducted the above study, which is carried out and analysed in section 7.5. ·         In the literature, we have mentioned many researchers who conducted one or two types of optimization techniques for sizing the off-grid microgrid. However, we incorporated nine metaheuristic algorithms to our study and we have also analyzed why it is that important in sections 7.6 and 7.7.
8.	COMMENT: Also, the references must be revised with the IEEE, Elsevier, Springer and MDPI Journals. Not only the Mirjalili uses metaheuristics. RESPONSE: We have been incorporated nine metaheuristic algorithms such as Particle Swarm Optimization (PSO). (IEEE journal) Differential Evolutionary Algorithm (DE). (Springer Journal)  Genetic Algorithm (GA). (IEEE journal)  Ant Lion Optimization (ALO). (Elsevier) Grasshopper Optimization Algorithms (GOA). (Elsevier) Grey Wolf Optimization (GWO). (Elsevier)  Moth Flame Optimization (MFO). (Elsevier)  Dragonfly Algorithm (DA). (Springer Journal) Salp Swarm Algorithm. (Elsevier) Among the algoritms listed above, we used two IEEE transaction journals and two Springer journals. The remaining five are Elsevier journals. Our goal is to use optimization algorithms to find the best solution, which we have accomplished by employing various optimization algorithms for our objective function. So, we've demonstrated it in our results and discussion sections by incorporating various optimization algorithms. Mirjalilis algorithms have been incorporated, because they are proven and well-established algorithms when compared to other algorithms, as evidenced by his articles results and discussion sections. That is why we have included his algorithms in our research. In our future studies, we hope to incorporate additional algorithms in addition to the Mirjaliis algorithms.
9.	COMMENT: In this form the paper has a lack of innovation. RESPONSE: We have significantly revised the manuscript to address the reviewer's feedback, including addressing research gaps and highlighting novel contributions. We have also provided a clearer structure to the paper to improve its overall readability.

 

Reviewer 2 Report

1)- Introduction ignored important contributions in this research area. Several methods are reported in the literature.

 

2)  The paper is not well written. In addtion, the contribution is, in

my opinion, not clear.   The authors should motivate more their contributions and clearly explain the intuitions behind the ideas. Also, more simulations and comparisons that show the advantage and the drawbacks of the proposed schema are needed. 

 

3)The authors should consider more challenging cases to assess the performance of the considered approach. 

 

4) In my opinion,  the discussion is unsatisfactory for presenting and discussing the "innovation" and originality of this work.

 

5) The paper does not compare to other methods. Please compare your approach with some commonly used

Author Response

	Reviewer #2
1.	COMMENT: Introduction ignored important contributions in this research area. Several methods are reported in the literature. RESPONSE: Thank you for your valuable feedback. We have now taken steps to strengthen our research by including an in-depth review of current literature with a focus on any uncovered gaps. We appreciate you bringing this to our attention, as it will enable us to make more informed decisions when conducting our research. Additionally, we have incorporated novelty points into the current study by detailing these points in bullet form which are mentioned in the last paragraph of the introduction section.
2.      2.	COMMENT: The paper is not well written. In addition, the contribution is, in my opinion, not clear. The authors should motivate more their contributions and clearly explain the intuitions behind the ideas. Also, more simulations and comparisons that show the advantage and the drawbacks of the proposed schema are needed. RESPONSE: ·         Thank you for your feedback. Following the suggestions you provided, we have now thoroughly reviewed and corrected the total grammar and fluency of the manuscript in the revised version. ·         We have now taken steps to strengthen our research by including an in-depth review of current literature with a focus on any uncovered gaps. We appreciate you bringing this to our attention, as it will enable us to make more informed decisions when conducting our research. Additionally, we have incorporated novelty points into the current study by detailing these points in bullet form. ·         We have conducted additional simulations and comparisons to assess the advantages and disadvantages of the proposed schema, examining three different lithium-ion battery configurations (PV/BMG/Li-Ion@50%DOD, PV/BMG/Li-Ion@70%DOD and PV/BMG/Li-Ion@80%DOD), two lead acid battery configurations (PV/BMG/LA@70% and PV/BMG/LA@80%) and one Ni-Fe battery configuration (PV/BMG/Ni-Fe@80%). We have used the HPRALC (High Power Rated Appliances of Low Cost) i.e., without DSM, to compare the above six topologies and determine which one is the most effective for bringing electricity to the area under investigation. We have also evaluated the optimal configuration using both MPRAMC (Medium Power Rated Appliances of Moderate Cost) and LPRAHC (Low Power Rated Appliances of High Cost)-based scenarios, i.e., with DSM. Also, we have conducted a thorough evaluation of the convergence and robustness efficiency of each algorithm. Through this analysis, we are able to determine which algorithms are most suitable for the off-grid area in India, as well as the most cost-effective way to electrify the region. Our findings will hopefully help to ensure that the region is appropriately and efficiently electrified. In addition, we have conducted an analysis of the Loss of Power Supply Probability for the study area, with a focus on minimizing the amount of power hours lost.
3.      3.	COMMENT: The authors should consider more challenging cases to assess the performance of the considered approach. RESPONSE: We aim to connect our current study with future studies by highlighting certain novel aspects, which are listed as follows: 1. Deep Reinforcement Learning for Intelligent Control of Off-Grid Microgrids 2. Multi-Objective Optimization for Off-Grid Microgrid Planning 3. Exploring Autonomous Agents for Automated Microgrid Management 4. Evolutionary Algorithms for Optimal Operation of Off-Grid Microgrids 5. Applying Swarm Intelligence for Reconfiguration of Off-Grid Microgrids 6. Accurate Real-Time Forecasting of Off-Grid Microgrid Resources Using Machine Learning 8. Artificial Neural Network-Based Solutions for Fault Detection and Diagnosis in an Off-Grid Microgrid 10. Application of Game-Theory Based Strategies for Autonomous Off-Grid Microgrid Operation
4.      4.	COMMENT: In my opinion, the discussion is unsatisfactory for presenting and discussing the "innovation" and originality of this work. RESPONSE: Thank you for providing these important points regarding the various configurations using battery technologies and renewable energy sources. I'm glad to report that my results and discussion have addressed these topics with detailed explanations. We have discussed various configurations using battery technologies and renewable energy sources. Also, the optimal configuration from the Lead Acid battery technology, the Lithium-ion battery technology, the Nickel iron battery technology, and the HPRALC (High Power Rated Appliances of Low Cost), MPRAMC (Medium Power Rated Appliances of Moderate Cost), and LPRAHC (Low Power Rated Appliances of High Cost)-based configurations. Additionally, I have also explored the HPRALC based scenario (without Demand Side management (DSM)), the medium efficiency appliances usage-based scenario (MPRAMC) (with DSM), and the high efficiency appliances usage-based scenario (LPRAHC) (with DSM). Furthermore, we studied the algorithm’s ability to find the global best optimal solutions and its effectiveness in achieving them, as well as the impact of LPSP on the system efficiency.
5.      5.	COMMENT: The paper does not compare to other methods. Please compare your approach with some commonly used RESPONSE: In our case study, we employed well-known metaheuristic algorithms to do optimal sizing of an off-grid microgrid. Specifically, these include Particle Swarm Optimization, Differential Evolutionary Algorithm, Genetic Algorithm, Ant Lion Optimization, Grasshopper Optimization Algorithms, Grey Wolf Optimization, Moth Flame Optimization and Dragonfly Algorithm and Salp Swarm Algorithm. With these algorithms, we used data from the study area (load demand and weather data) to ensure the optimal sizing of the microgrid.

Reviewer 3 Report

The manuscript presents yet another meta-heuristic optimization technique to solve off-grid renewable energy integration problem. Nonetheless, the reviewer's has a few comments:

1. There are some inaccurate statement in the introduction section, such as in Line 47 onward. The authors need to be more careful and precise in pointing out the pros and cons of a particular approach.

2. Line 56. The first sentence, yet again is not quite accurate. And it is not the reason why the meta-heuristic techniques are preferable than deterministic ones. Please clarify the statement.

3. Line 64. How do we know the results are accurate as we do not know their solutions in the first place?

4. Line 138. Comparing the results from various meta-heuristics can be problematic due to each of the algorithm require a particular parameters selection and/or adjustment. Thus, benchmarking the one performance with the others seems to be misleading.

In term the manuscript presentation, the manuscript is poorly written, not only the language style but also the format/layout. As if this manuscript was not ready for review. The reviewer identify many Typographical errors during review and using capital letters unnecessarily. 

Regarding Figs. 4 and 5, rather than showing these figures, it would be more beneficial to present a statistical annual solar radiation and annual ambient temp. in the area under study.

Some figures need to be revised such as Fig. 3.

Author Response

	Reviewer #3
1.      1.	COMMENT: There are some inaccurate statements in the introduction section, such as in Line 47 onward. The authors need to be more careful and precise in pointing out the pros and cons of a particular approach. RESPONSE: Thank you very much for the feedback. We have addressed the issues you mentioned and updated the introduction section to ensure that the review is accurate and precise in its presentation of the pros and cons of each approach. We appreciate you taking the time to point out this issue. Which are highlighted with the yellow color in the revised manuscript.
2.      2.	COMMENT: Line 56. The first sentence, yet again is not quite accurate. And it is not the reason why the meta-heuristic techniques are preferable than deterministic ones. Please clarify the statement. RESPONSE: Thank you very much for the feedback. We have addressed this issue in the revised manuscript by citing an appropriate reference. After reviewing the literature, we found that deterministic methods are limited by local optima, which is supported by the results of several papers in the literature. Therefore, to optimize our system and get the best possible solution, we have chosen to use metaheuristic algorithms as they have been proven to provide better results than deterministic methods. Our goal is not to emphasize the different types of algorithms, but to present the most effective solution for our system.
3.      3.	COMMENT: Line 64. How do we know the results are accurate as we do not know their solutions in the first place? RESPONSE: To achieve accurate results, we optimised the objective function using nine metaheuristic optimization algorithms. We only knew the optimal solutions after the simulation; before the simulation, we didn't. The corresponding results are all listed in the tables below. ·         Table 1 Optimization results of the IRESs using HPRALC-based scenario at an LPSP value of 0%. ·         Table 2 Optimization results of the IRESs using MPRAMC based scenario at an LPSP value of 0%. ·         Table 3 Optimization results of the IRESs using LPRAHC based scenario at an LPSP value of 0%.
4.      4.	COMMENT: Line 138. Comparing the results from various meta-heuristics can be problematic due to each of the algorithm require a particular parameters selection and/or adjustment. Thus, benchmarking the one performance with the others seems to be misleading. RESPONSE: The control parameters of the proposed algorithms used in the study are provided in the supplementary material. We have been used Particle Swarm Optimization, Differential Evolutionary Algorithm, Genetic Algorithm, Ant Lion Optimization, Grasshopper Optimization Algorithms, Grey Wolf Optimization, Moth Flame Optimization and Dragonfly Algorithm and Salp Swarm Algorithm. With these algorithms, we used data from the study area (load demand and weather data) to ensure the optimal sizing of the microgrid. Among those algorithms the following algorithms we can tune control parameters such as Particle Swarm Optimization, Differential Evolutionary Algorithm, Genetic Algorithm, we have used these values are standard values from the literature research articles. On the other hand, rest of the algorithms are the self-adaptive algorithms, which are automatically adjust their parameters according to the optimal value of each iteration.
5.      5.	COMMENT: In term the manuscript presentation, the manuscript is poorly written, not only the language style but also the format/layout. As if this manuscript was not ready for review. The reviewer identifies many Typographical errors during review and using capital letters unnecessarily. RESPONSE: Thank you for your feedback. We apologize for the issues you have identified in the manuscript presentation. We reviewed and revised the manuscript to ensure that all grammar and typographical errors have been corrected, and that the overall format/layout meets the highest standards. We appreciate your input and hope that after our revisions you will give us a favorable assessment. Thank you for your time and effort.
6.	COMMENT: Regarding Figs. 4 and 5, rather than showing these figures, it would be more beneficial to present a statistical annual solar radiation and annual ambient temp. in the area under study. RESPONSE: This data can be easily accessed from the NREL (National Renewable Energy Laboratory) website by inputting the latitude and longitude of the study area as mentioned in the manuscript. We have drawn the figures in the paper using 8760 hours of data; but for the sake of brevity, we have not included the entire dataset in the manuscript.
7.	COMMENT: Some figures need to be revised such as Fig. 3. RESPONSE: According to your suggestion, we have revised the Figure 3 which is highlighted in the revised manuscript.