Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems
, Tulika Chakrabarti 2, Prasun Chakrabarti 3, Martin Margala 4, Amit Gupta 5, S. Phani Praveen 6, Sivaneasan Bala Krishnan 7 and Bhuvan Unhelkar 8Round 1
Reviewer 1 Report
The paper presents a new variant of particle swarm optimization techniques, which the authors claim that it is effective and efficient to find out optimum solutions for single (ELD) and multi-objective (ELD + EELD) problems.Here are my concerns
1. The authors claim that classical methods are ineffective because they have many limitations and take a long computation time to solve nonlinear optimization problems. Instead, they propose Heuristic methods. I agree with Heuristic methods' simplicity and application advantages, but not about their speed. Probably, the most important drawback of Heuristic methods is their high computation times.
2. The name of the horizontal axis in Fig.1 should be iteration not maximum iteration.
3. Table 1 should be reorganized with the units of the parameters. Also, the reason for zero d_i should be stated, as the environmental cost becomes a linear function of the generator output, not quadratic.
4. When I check Table 2, the final total fuel cost obtained by the proposed MPSO is quite close to the values obtained by HS, DE, HQPSO (less than 0.01%). They are within the error limits from a practical point of view and probably originated from improper parameter adjustment or convergence criteria. Similarly, the results for the second case study are also too close to each other. Therefore, additional test system applications are required to claim the superiority of the proposed method.
5. ELD problem is an online process and involves fast computation speed. Therefore, the authors are expected to compare the computational speeds of several Heuristic methods.
6. This reviewer believes that Eq. 14 (Case study-3) is not a formulation of a multi-objective optimization problem. Instead, a linear combination of two objectives, where the results depend on the coefficients (here h_i). A multi-objective is a vector optimization type ; no single solution optimizes each objective simultaneously. The Pareto-front concept is used for the evaluation of the results. Please refer to the following references for the details of constrained multi-objective optimization formulations.
Mohammad-Ali Hamidan, Farzaneh Borousan, Optimal planning of distributed generation and battery energy storage systems simultaneously in distribution networks for loss reduction and reliability improvement, The Journal of Energy Storage, Feb. 2022.
Ahmadi, B[WK1] ., Ceylan, O., Ozdemir, A., “Distributed energy resource allocation using multi-objective grasshopper optimization algorithm,” Electric Power Systems Research, Volume 201, Article Number107564, December 2021,
Tawhid, M.A., Savsani, V., 2019. Multi-objective sine-cosine algorithm (mo-sca) for multi-objective engineering design problems. Neural Computing and Applications 31, 915–929.
Ahmadi, B[WK2] ., Ceylan, O., Ozdemir, A., Fotuhi-Firuzabad, M., “A multi-objective framework for distributed energy resources planning and storage management,” Applied Energy, 314(1):118887, May 2022
7. Again, the ELD + EELD problem is an online process involving fast and accurate computation. Therefore, a comparison of computation speed must be done.
8. Please check lines 456 and 455. The same abbreviations (e_i and f_i) are used for the two different parameters.
9. Since the paper concentrates on the solution method, it would be better to perform the computations by traditional PSO and compare them with MPSO to validate the performance of their proposal.
The quality of English is acceptable.
Author Response
Respected Sir, Thank you for your valuable suggestions and guidance. I try my best to modify the paper as per your valuable suggestions.
Kindly give your attention, and if you need any more changes, kindly guide me.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper introduced a new variant of particle swarm optimization techniques called modified particle swarm optimization which is effective and efficient to find out optimum solutions for single as well as multi-objective economic load dispatch problems. Although, the idea behind the paper is original in order to be published the manuscript requires modifications and improvements. First of all, literature review section could be improved with more relevant literature. Furthermore, I kindly ask the authors to elaborate why they have not included demand response constraints in the ELD formulation. Furthermore, the authors should explain why only deterministic simulation cases are analyzed.
Moderate English language editing is required.
Author Response
Respected Sir, Thank you for your valuable suggestions and guidance. I try my best to modify the paper as per your valuable suggestions.
Kindly give your attention, and if you need any more changes, kindly guide me.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper introduces a modified particle swarm optimization technique for economic load dispatch problems, but lacks clarity in objectives, justification for the chosen technique, quantitative evaluation, and discussion on limitations. Relative quantification of results and poor graphical presentation impact reading.
1. The objective of economic load dispatch (ELD) is to satisfy the lowest generation cost and comply with various constraints. However, it doesn't clearly state what specific constraints or objectives are considered in the study, making it difficult to assess the significance of the proposed approach.
2. The paper introduces a modified particle swarm optimization (MPSO) technique for solving the ELD problem but fails to provide a clear rationale for why this technique is chosen over other existing optimization methods. It would be beneficial to provide a comparative analysis or justification for the selection of MPSO and its advantages over classical optimization techniques.
3. The paper briefly mentions three case studies involving different ELD scenarios, including valve point loading effect, ramp rate limits, and economic emission dispatch (EELD) as a multi-objective problem. However, it lacks details on the specific data, system configurations, and performance metrics used in these case studies. Providing more comprehensive information about the case studies would help in evaluating the robustness and applicability of the proposed techniques.
4. Lack of quantitative evaluation: While the paper claims that the suggested strategy produces superior optimization outcomes compared to alternative methods, it doesn't provide any specific quantitative results or performance metrics to support this claim. Including some numerical comparisons or statistical analyses would enhance the credibility of the proposed approach.
5. Absence of discussion on limitations: The paper does not mention any limitations or potential challenges associated with the proposed MPSO technique or the application of economic load dispatch. Addressing the limitations and discussing possible areas for improvement would provide a more comprehensive understanding of the research and its practical implications.
Author Response
Respected Sir, Thank you for your valuable suggestions and guidance. I try my best to modify the paper as per your valuable suggestions.
Kindly give your attention, and if you need any more changes, kindly guide me.
Author Response File: Author Response.pdf
Reviewer 4 Report
Dear authors,
I have completed the review on your paper. The report is attached.
Best wishes,
the reviewer
Comments for author File: Comments.pdf
No.
Author Response
Respected Sir, Thank you for your valuable suggestions and guidance. I try my best to modify the paper as per your valuable suggestions.
Kindly give your attention, and if you need any more changes, kindly guide me.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The authors have responded to some of my concerns. However, some important ones should be held to accept the paper from my side.
- The main disadvantage of heuristic methods is that they are often slower and less efficient than gradient-based methods. They can also require more parameters and computational resources to tune and run. Therefore I do not agree with the statement, “Classical methods are ineffective because they have many limitations and take a long computation time to solve nonlinear optimization problems”. The answer “During the optimization, I found that the proposed PSO took very little time, and hence I claim its computation time is very little” may be correct for just one application and can not be generalized.
- Table 1 still suffers from the parameters without units. For example, P_imax should follow by [MW] or [kW], whichever is appropriate. Similarly, Table 3,4,5… suffers from the variables without incorporated units.
- Thanks for upgrading Table-2. However, I insist on the idea that the final total fuel cost obtained by the proposed MPSO is quite close to the values obtained by HS, DE, HQPSO (less than 0.01%). The authors' reply claims that the differences become significant if decided within a year. I agree with it. My objection is that the “relative” differences between the values are less than 0.01%, which is within the error limits from a practical point of view and probably originated from improper parameter adjustment or convergence criteria. On the other hand, I couldn’t understand why most of the entries in the PSO column are empty. I have doubts regarding the high final costs determined by PSO, even for a considerably longer computation period. MSO.
- Let me state again that Eq. 14 (Case study-3) is a linear combination of two objectives, where the partial improvements of each objective depend on the penalty factor hi. A multi-objective optimization involves the minimization of a vector of partial objective functions (f1(x), F2(x),…); and there is no unique solution optimizing each objective simultaneously. The Pareto-front concept is used for the evaluation of the results. Please refer to the following references for the details of constrained multi-objective optimization formulations. The authors can combine two objectives in a single objective, like Eq.14, but they can not find the optimum solution for both objectives since there is no such point.
- It will be better to reorganize the conclusions section so that it can give a general sense of the contribution of the paper instead of repeating the case study results.
Author Response
Respected Sir
Thank you, sir, for your valuable suggestions. I tried my best, edited, and included suggestions. I hope the revised article fulfills the queries and is up to par.
Author Response File: Author Response.pdf
Reviewer 2 Report
In my opinion the paper can be accepted in present form.
Moderate English editing is required.
Author Response
Respected Sir
Thank you, sir, for your valuable suggestions. I tried my best, edited, and included suggestions. I hope the revised article fulfills the queries and is up to par.
Author Response File: 
Author Response.docx
Reviewer 3 Report
The current version has been greatly improved, and the paper can now be accepted after consideration by the editor.
Author Response
Respected Sir
Thank you, so much for accepting my article.
Reviewer 4 Report
Dear authors,
The authors have completed the revisions according to the review comments. I suggest publishing the paper.
Best regards,
the reviewer
Author Response
Respected Sir
Thank you, for accepting my article.
Author Response File: Author Response.docx
