Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis study offers a comprehensive analysis of renewable distributed generation (RDG) sizing and siting under N-1 faulty line conditions, with a focus on the IEEE 30-bus power system benchmark. The research addresses the critical issue of power system reliability and quality in the presence of severe faults, particularly F27-29 and F27-30. The integration of a hybrid Crow Search-Firefly Optimizer (CS-FFO) algorithm for optimal power flow (OPF) problem-solving demonstrates an innovative approach.
The paper effectively communicates its objectives, methodologies, and findings. The severity ranking index, considering both voltage deviation and overloading, provides a clear basis for identifying the most critical faulty scenario (F27-30). The integration of a hybrid CS-FFO algorithm to address the OPF problem is well-motivated, leveraging the strengths of both optimization techniques.
The study's findings, including the optimal sizing and allocation of distributed generators (DGs) at critical buses, present valuable insights. The reduction in line overloading, enhanced voltage profiles, and decreased total costs contribute to the significance of the research. The proposed methodology appears robust and effective in addressing the challenges posed by N-1 faulty line conditions.
The manuscript is well-organized, with a structured introduction, detailed methodology, and comprehensive presentation of results. Figures and flowcharts enhance the clarity of the presented concepts. However, some typographical errors and minor grammatical issues could be addressed to enhance the overall readability.
Although this paper presents a valuable scientific contribution, some points need to be addressed to enhance the overall quality of the paper. :
-- Limited Discussion on Algorithm Selection: While the hybrid CS-FFO algorithm is introduced and its integration is justified, a more in-depth discussion on why this specific algorithm was chosen and how it compares to alternative optimization methods could strengthen the paper.
-- Limited Comparison to Existing Literature: The paper lacks a comprehensive discussion comparing its findings or methodologies to existing literature. A brief review or discussion of related works in the field could provide context and highlight the paper's contribution.
-- Simplistic Fault Scenarios: The study focuses on two specific N-1 faulty scenarios (F27-29 and F27-30). Expanding the analysis to include a broader range of fault scenarios could strengthen the paper's applicability to a wider array of real-world situations.
-- Absence of Sensitivity Analysis: The paper does not discuss sensitivity analysis or the robustness of the proposed method under variations in parameters. A brief exploration of the algorithm's sensitivity to parameter changes would enhance the study's credibility.
-- Mention of Cuckoo search algorithm: The authors mention the Cuckoo search algorithm on page 9 (last paragraph), although they used Crow search in their optimization.
Author Response
Please see attached file.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper provides an interesting research!
Meanwhile, some comments are:
1. Page 3, line 136. A typo, the coefficient should be w2.
2. Page 3, line 134, 136. SI or SPI index?
3. Page 3, line 137. Should be w1 and w2 further defined?
4. Page 5, line 164. SPI is calculated for particular w1 and w2, isn’t it? Which w1 and w2 are used?
5. Page 6, lines 211-217. There is a repetition. where a denotes the randomization parameter and e is a vector of random values drawn from a Gaussian distribution. After all generations, the firefly with the maximum brightness, i.e., the best fitness value, is determined to be the best solution to the problem. Here, α represents the randomization parameter, and ε is a vector of random values drawn from a Gaussian distribution. Following all generations, the firefly with the maximum brightness, corresponding to the best fitness value, is identified as the optimal solution to the problem.
6. Page 9, Figure 4. The pre-/post-contingency line flow without RDGs: a) F27-29; b) F27-30. There is only 1 minor breach of the MVA limit for line 38 in case a) and 1 breach for line 37 in case of b). The purpose of the paper is to address these breaches or my understanding is not correct?
7. Page 10, lines 297-298. The paper addresses the most critical N-1 fault scenarios involving faulty lines F27-29 and F27-30 by placing additional DGs “where they can effectively contribute to system stability and reliability.” The solution is found “The optimal sizing of the DG at bus 30 (24.8478 MW) is attained through the attainment of the 306 minimum objective function value.” Thus, the most critical fault scenario has been addressed. From the perspective of dealing with the IEEE 30-bus power distribution system, the reasonable question is the following. What is the next most critical fault scenario (taking into account that the scenario involving faulty lines F27-29 and F27-30 is addressed) in terms of the impact on the system stability and reliability. Can this question be answered in the paper?
8. Page 10, table 3. There are numbers with 4 decimals. Are all these decimals meaningful? Shall just 2 remain?
9. Page 10, table 3. PG2 “67.9428 38.6242”, PG5 “18.6940 24.3639”. Should it be 1 number, not 2 numbers?
10. Page 1. I would propose to review the alignment of the name, aim and body of the paper.
- the name: “Integrating Firefly and Crow Algorithms for Resilient Sizing and Siting of Renewable DG Systems under Faulty Scenarios”.
- the aim: “This study aims to analyze, investigate, and assess renewable distributed generation (RDGs) allocation and size under N-1 faulty line condition in terms of reliability as well as quality for the IEEE 30-bus benchmark power system as a case study.”
From the name it could be concluded that the main novelty is the application of 2 algorithms to a particular task. The aim does not refer to the algorithms but is focused on the allocation and size of renewable DGs. Thus, from the aim, the main result is finding of the location and parameters of the renewable DGs for the benchmark power system (i.e. not the method of finding, but the solution). From the paper body, I concluded (correct me if am wrong) that renewability is a desirable but not necessary property of DGs in the context of the paper. Renewable DGs are one example of the DGs, which should be added to the system to improve its stability.
Author Response
Please see attached file.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors- The objective of this paper is to analyze, investigate, and assess renewable distributed generation allocation and size under N-1 faulty line condition in terms of reliability and quality. The novelty of the work must be clearly addressed and discussed.
- The authors must enhance the Abstract section and add some numerical results in it.
- graphical abstract is needed.
- Please redraw the flowchart indicating the equations.
- Please compare the result for IEEE 30-bus system.
- Please show in table the data used in the system.
- Please explain the methodology of applying combined firefly and crow search optimizers for solving the optimal RDGs sizing and allocation.
- English language should be carefully checked and carefully check paper for language typos.
Author Response
Please see attached file.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsThanks for the authors
Author Response
Please see attached file.
Author Response File: 
Author Response.docx
