Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Round 1

Reviewer 1 Report

This paper solves the high uncertainty of PHEV and RER in MG using MCS algorithm, and also addresses the hydrogen storage strategy problem in PEMFC-CHP using MINLP algorithm. The maximization of market profits is achieved by introducing an improved MOA algorithm. Through comparative analysis of cases, it can be concluded that the algorithm proposed in this paper can reduce operating costs while significantly shortening calculation time.

However, please note the following issues in the paper:

1. In the summary section, it is recommended to first indicate the overall goal and algorithm core, and finally explain the optimization points for each part of MG, so as to make the logic clearer and more rigorous.

2. More work related to hydrogen storage or energy storage can be discussed, e.g. An integrated model with stable numerical methods for fractured underground gas storage. Journal of Cleaner Production, 2023, 393, 136268.  A covering liquid method to intensify self-preservation effect for safety of methane hydrate storage and transportation. Pet. Sci. 19, Pages 1411-1419 (2022).

3. Lines 233-248, for the meerkat optimization algorithm, the author introduced the meerkat as an animal. Animal encyclopedia is very interesting, but it appears somewhat redundant in scientific papers. Can you remove unnecessary introductions to make this section more concise？

4. The innovation summary of this article is too long, and many concepts are repeatedly mentioned. In addition, from the overall perspective of the entire text, a lot of space is devoted to introducing and improving the MOA algorithm, while there is little mention of the MCS algorithm used to solve the uncertainty of PHEV and RER, as well as the MINLP method used to solve the hydrogen storage strategy in the PEMFC-CHP device, which makes it difficult to pay attention to these methods in the text. Please revise the paper or explain the reasons for this phenomenon.

Author Response

Dear Reviewer

We would like to thank you for your accurate attention and valuable recommendation. We are glad to hear the positive feedback and We have addressed your comments point by point below, and in the attachment.

Reviewer 1:

This paper solves the high uncertainty of PHEV and RER in MG using MCS algorithm, and also addresses the hydrogen storage strategy problem in PEMFC-CHP using MINLP algorithm. The maximization of market profits is achieved by introducing an improved MOA algorithm. Through comparative analysis of cases, it can be concluded that the algorithm proposed in this paper can reduce operating costs while significantly shortening calculation time.

However, please note the following issues in the paper:

Authors’ Response: Firstly, we would like to thank you for your accurate attention and valuable recommendation. We are glad to hear the positive feedback and We have addressed your comments point by point below.

Comment # 1: In the summary section, it is recommended to first indicate the overall goal and algorithm core, and finally explain the optimization points for each part of MG, so as to make the logic clearer and more rigorous.

Authors’ Response: Thank you very much for your good comment. The summary section was modified according to the valuable advice of the respected reviewer as below. Please see the changes highlighted in yellow in the revised paper.

“Abstract: Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell-CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal microgrid operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.”

Comment # 2: More work related to hydrogen storage or energy storage can be discussed, e.g. An integrated model with stable numerical methods for fractured underground gas storage. Journal of Cleaner Production, 2023, 393, 136268.  A covering liquid method to intensify self-preservation effect for safety of methane hydrate storage and transportation. Pet. Sci. 19, Pages 1411-1419 (2022).

Authors’ Response: Thanks to the esteemed reviewer for these valuable recommendations. References cited were added to the revised paper. Please see the changes highlighted in yellow in the revised paper.

“In [22], a novel model for fractured underground gas storage (UGS) is presented, optimizing stability and efficiency. The integrated approach addresses depleted oil/gas reservoir challenges, determining maximum gas storage capacity and analyzing wellbore and reservoir conditions. In the ref [23], a covering liquid method is explored for enhancing the self-preservation effect in methane storage. Experiments show a significant reduction in CH4 hydrate decomposition with various covering liquids, particularly below the freezing point of water.”

[22] Wendi Xue, Yi Wang, Zhe Chen, Hang Liu, An integrated model with stable numerical methods for fractured underground gas storage, Journal of Cleaner Production. 2023 March 20;393:136268.

[23] Jun Chen, Yao-Song Zeng, Xing-Yu Yu, Qing Yuan, Tao Wang, Bin Deng, Ke-Le Yan, Jian-Hong Jiang, Li-Ming Tao, Chang-Zhong Chen, A covering liquid method to intensify self-preservation effect for safety of methane hydrate storage and transportation, Petroleum Science. 2022 June;19:1411-1419.

Comment # 3: Lines 233-248, for the meerkat optimization algorithm, the author introduced the meerkat as an animal. Animal encyclopedia is very interesting, but it appears somewhat redundant in scientific papers. Can you remove unnecessary introductions to make this section more concise?

Authors’ Response: Thanks to the esteemed reviewer for this valuable advice. The introduction of the proposed algorithm was summarized. Please see the changes highlighted in yellow in the revised paper.

“The meerkat, a small mammal with a distinct brown striped coat, inhabits deserts and varied terrains. As a diurnal species, it has a slender physique with a supportive tail for an upright stance. Notably, dark spots around its eyes function like sunglasses, enabling clear vision in intense sunlight. This adaptation aids in countering aerial predators exploiting sun glare. Leveraging these behavioral patterns, we introduce the Meerkat Optimization Algorithm (MOA), with the subsequent mathematical framework explaining its essence.”

Comment # 4: The innovation summary of this article is too long, and many concepts are repeatedly mentioned. In addition, from the overall perspective of the entire text, a lot of space is devoted to introducing and improving the MOA algorithm, while there is little mention of the MCS algorithm used to solve the uncertainty of PHEV and RER, as well as the MINLP method used to solve the hydrogen storage strategy in the PEMFC-CHP device, which makes it difficult to pay attention to these methods in the text. Please revise the paper or explain the reasons for this phenomenon.

Authors’ Response: Thank you very much for your comment. The items mentioned in the text of the revised paper were corrected. Please see the changes highlighted in yellow in the revised paper.

“This study employs Monte Carlo simulation to estimate both the photovoltaic (PV) power and the aggregate charging load of electric vehicles (EVs) [24-25]. The charging load profile for each individual EV is derived based on the commencement time and travel distance of the vehicle. Subsequently, the overall charging load profile is synthesized by overlaying the load profiles of all participating electric vehicles. A visual representation of the charging load calculation process is illustrated in Figure1, depicted in the accompanying flowchart.

Figure 1. diagram for calculating the charging load of electric vehicles.”

Author Response File: Author Response.docx

Reviewer 2 Report

The paper deals with an interesting topic related to the energy management of MGs and RERs in distribution networks.

The paper is correctly written and well presented, despite the difficulty related to the choice of words used, which are not necessarily easy to assimilate in the context of the research and can make the understanding more difficult - at least please make the writing of the abstract smother.

The paper is correctly written and well presented, despite the difficulty related to the choice of words used, which are not necessarily easy to assimilate in the context of the research and can make the understanding more difficult - at least please make the writing of the abstract smother.

Author Response

Dear Reviewer

We would like to thank you for your accurate attention and valuable recommendation. We are glad to hear the positive feedback and We have addressed your comments point by point below, and in the attachment.

Authors’ Response to Reviewers’ Comments

Reviewer 2:

The paper deals with an interesting topic related to the energy management of MGs and RERs in distribution networks.

Authors’ Response: Firstly, we would like to thank you for your accurate attention and valuable recommendation. We are glad to hear the positive feedback and We have addressed your comments point by point below.

Comment # 1: The paper is correctly written and well presented, despite the difficulty related to the choice of words used, which are not necessarily easy to assimilate in the context of the research and can make the understanding more difficult - at least please make the writing of the abstract smother.

Authors’ Response: Thank you very much for your suggestion. Corrections were made to the revised paper and the abstract became more fluent. Please see the changes highlighted in yellow in the revised paper.

“Abstract: Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell-CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal microgrid operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.”

Comment # 2: The paper is correctly written and well presented, despite the difficulty related to the choice of words used, which are not necessarily easy to assimilate in the context of the research and can make the understanding more difficult - at least please make the writing of the abstract smother.

Authors’ Response: Thank you very much for your suggestion. Corrections were made to the revised paper and the abstract became more fluent. Please see the changes highlighted in yellow in the revised paper.

“Abstract: Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell-CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal microgrid operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.”

Author Response File: Author Response.docx

Reviewer 3 Report

The work presents a methodology for managing the energy resources present in a microgrid. Management methods based on optimization algorithms are proposed that involve the combination of mathematical programming algorithms, MINLP, Monte Carlo simulations and an optimization meta-heuristic based on behaviors observed in nature.
When modeling the microgrid, different types of energy sources and resources are considered, with special emphasis on the consideration of storage in the form of hydrogen.

The state of the art was well described and the bibliographic references used are very representative. The objectives of the work are well defined and the points of innovation in relation to other related work are also well defined.

In section 2 it is possible to check the description of the objective function of the problem and the details of the parameters and variables used in the modeling are well specified.

Section 3 describes the MOA optimization algorithm.

In section 4 a numerical example is provided, through the definition of a microgrid used as a case study. In this same section, the results obtained with the optimization algorithm proposed for managing the microgrid in the case study are compared with other algorithms and an analysis of the behavior of each of the energy resources is carried out.

In my view, there is a gap between sections 3 and 4. The details of how the integration between the different optimization algorithms proposed in the modeling are carried out are not shown, this compromises the work as a whole, making it impossible to understand what is being proposed.

In section 4, when one would expect a schematization of the microgrid management process to be carried out, nothing is shown and the results are only displayed in the format of graphs and tables. It is necessary to provide a detailed overview of the microgrid management system. Furthermore, the texts that deal with the analysis of the graphics obtained as output from the optimization process are meaningless, as if there were writing guidelines there, this is a gross error in writing and needs to be completely rewritten.

Therefore, unless a broad review based on these observations is carried out, the work cannot be published.

Author Response

Dear Reviewer

We would like to thank you for your accurate attention and valuable recommendation. We are glad to hear the positive feedback and We have addressed your comments point by point below, and in the attachment.

Authors’ Response to Reviewers’ Comments

Reviewer 3:

The work presents a methodology for managing the energy resources present in a microgrid. Management methods based on optimization algorithms are proposed that involve the combination of mathematical programming algorithms, MINLP, Monte Carlo simulations and an optimization meta-heuristic based on behaviors observed in nature.

When modeling the microgrid, different types of energy sources and resources are considered, with special emphasis on the consideration of storage in the form of hydrogen.

The state of the art was well described and the bibliographic references used are very representative. The objectives of the work are well defined and the points of innovation in relation to other related work are also well defined.

Authors’ Response: Firstly, we would like to thank you for your accurate attention and valuable recommendation. We are glad to hear your feedback and We have addressed your comments point by point below.

Comment # 1: In section 2 it is possible to check the description of the objective function of the problem and the details of the parameters and variables used in the modeling are well specified.

Authors’ Response: Thank you very much for your suggestion. The details of the objective function were mentioned. Please see the changes highlighted in yellow in the revised paper.

“The objective function (1) is designed to address the dual objectives of profit maximization and the assurance of energy resource availability, considering reliability factors.  The first term aims to maximize profit by subtracting the total cost from the revenue generated. Operational costs include those associated with generators, storage units, and the grid, while revenue is obtained from tariff charges and power sales. The second term ensures energy resource availability with a reliability consideration. The availability of generated power from generators and storage units, considering the reliability factor and penalizing deviations in unit voltage levels from the previous time step. The availability of power from the grid incorporates the product of grid power and the corresponding availability factor. The third term imposes a penalty for generators falling below a predefined reliability threshold. The penalty is proportional to the deviation of the generator's reliability from the established threshold. This objective function provides a comprehensive framework for optimizing microgrid operations, balancing economic considerations with the reliability of energy resources. The weighting factors offer flexibility in prioritizing objectives based on specific system requirements or goals.”

Comment # 2: Section 3 describes the MOA optimization algorithm.

In section 4 a numerical example is provided, through the definition of a microgrid used as a case study. In this same section, the results obtained with the optimization algorithm proposed for managing the microgrid in the case study are compared with other algorithms and an analysis of the behavior of each of the energy resources is carried out.

In my view, there is a gap between sections 3 and 4. The details of how the integration between the different optimization algorithms proposed in the modeling are carried out are not shown, this compromises the work as a whole, making it impossible to understand what is being proposed.

Authors’ Response: Thank you very much for your good comment. The relationship between the third and fourth sections and the optimization method of the proposed algorithm has been added to the revised paper. Please see the changes highlighted in yellow in the revised paper.

“In our proposed modeling approach, the entire optimization process is exclusively executed using the Meerkat Optimization Algorithm (MOA). This algorithm serves as the sole optimization engine for the microgrid system, addressing multiple objectives and constraints simultaneously. The framework involves a structured procedure to harness the capabilities of MOA for comprehensive microgrid management:

   - The optimization process commences with the initialization of microgrid parameters. The multi-objective optimization problem is decomposed into sub-problems, each corresponding to specific facets of the microgrid operation targeted by MOA. For instance, MOA is adept at maximizing market profits, ensuring reliability, and optimizing various energy resources.

   - MOA operates iteratively, dynamically exploring the vast solution space to discover configurations that optimize objectives. Its unique exploration-exploitation balance allows it to effectively navigate uncertainties and complexities inherent in microgrid management. MOA's contributions extend to optimizing generator and storage unit reliability, intelligent charging protocols for electric vehicles, and addressing hydrogen storage challenges in fuel cell systems.

   - The integration framework includes mechanisms for adaptive adjustments within MOA, allowing the algorithm to dynamically adapt its strategies based on the evolving solution landscape. This adaptability ensures robustness in optimizing the microgrid under changing conditions and requirements.

   - The solutions obtained through MOA are harmonized to form a comprehensive solution set that optimally balances conflicting objectives. This process ensures that each solution contributes positively to the overall performance of the microgrid.

By detailing the optimization process exclusively driven by MOA, we aim to provide a clear understanding of how this algorithm autonomously and comprehensively addresses the objectives of microgrid management. The integration strategy leverages MOA's strengths, creating a holistic approach that maximizes the synergies between various facets of microgrid operation, all achieved through the proposed algorithm.”

Comment # 3: In section 4, when one would expect a schematization of the microgrid management process to be carried out, nothing is shown and the results are only displayed in the format of graphs and tables. It is necessary to provide a detailed overview of the microgrid management system. Furthermore, the texts that deal with the analysis of the graphics obtained as output from the optimization process are meaningless, as if there were writing guidelines there, this is a gross error in writing and needs to be completely rewritten.

Therefore, unless a broad review based on these observations is carried out, the work cannot be published.

Authors’ Response: Thank you very much for your suggestion. Section 4 was completely modified according to the valuable advice of the respected reviewer. Please see the changes highlighted in yellow in the revised paper.

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

All observations and questions made were answered by the authors and the work is now more objective, presenting a good level of maturity for publication.