Improved Low-Cost Home Energy Management Considering User Preferences with Photovoltaic and Energy-Storage Systems
Round 1
Reviewer 1 Report (Previous Reviewer 3)
The authored covered all my comments and the paper can be accepted in its current form.
Author Response
Thank you for the valuable comments and suggestions to improve the paper quality from the beginning until now.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (Previous Reviewer 1)
The nomenclature is incomplete, acronyms like “Smart HAs”, “EMC”, “DEP”, “SR”, just to name a few are not included. Please include all your acronyms in the same nomenclature table, otherwise, I don’t understand the purpose of such table.
The authors make extensive use of acronyms across the paper, without a proper nomenclature table it is very difficult to navigate the document as well as follow its content.
Minor English improvements required, good job.
Author Response
Reviewer-2
Comments and Suggestions:
- The nomenclature is incomplete, acronyms like “Smart HAs”, “EMC”, “DEP”, “SR”, just to name a few are not included. Please include all your acronyms in the same nomenclature table, otherwise, I don’t understand the purpose of such table.
Response to the reviewer:
- The nomenclature table has been rearranged and all the symbols and the acronyms are shown in order to obtain a better understanding as suggested.
Comments and Suggestions:
- The authors make extensive use of acronyms across the paper, without a proper nomenclature table it is very difficult to navigate the document as well as follow its content.
Response to the reviewer:
- Sometimes it may be necessary to use numerous abbreviations to shorten the text length in the paper. However, as a result of the suggestions and comments made by the reviewer, the least possible number of abbreviations were used in the text to make the article as much clear as it should be.
The authors would like to thank the reviewer for his/her valuable comments and suggestions to improve the quality of this paper from the beginning until now.
Author Response File: Author Response.pdf
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
The paper is aiming to present the technical benefits of demand side management programs based on PV and storage devices installed at the customer side. The motivation of the research is not clear, is this approach aimed at economic savings at the customer side? Is it looking for relieving the power grid from thermal or voltage violations? Is it looking for reducing technical losses? All these topics are briefly mentioned at the introduction but unfortunately, instead of offering a clear description of the problem and the project’s motivation, ends up by making the topic discussed in the paper vague. I would suggest to the authors to focus on a specific topic for describing the project’s motivation, later, mentioning the other benefits observed/expected from the project.
In this paper, the authors present 3 algorithms for evaluating the best economic performance for a household while coordinating appliances utilization for 24 hours. However, I wonder how uncertainty fits in this evaluation since, the operation is presented as predictable and the demand cycles looking at the best conditions for power generation (PV).
Otherwise, the paper looks interesting, the practical applicability of the content presented here is still debatable given the control level required as well as the origin of the price signals. I think this publication can work for enabling a discussion around this topic.
Author Response
Reviewer1
The paper is aiming to present the technical benefits of demand side management programs based on PV and storage devices installed at the customer side.
Comments and Suggestions:
The motivation of the research is not clear. Is this approach aimed at economic savings at the customer side? Is it looking for relieving the power grid from thermal or voltage violations? Is it looking for reducing technical losses?
All these topics are briefly mentioned at the introduction but unfortunately, instead of offering a clear description of the problem and the project’s motivation, ends up by making the topic discussed in the paper vague.
Response:
The motivation of the research is explained in clearly in the abstract and section 1.2.
This approach is used to reduce daily energy consumption cost at the customer side as well as peak-to-average ratio (PAR) and user’s discomfort (UD).
The paper is reworded and all necessary changes are made and clearly explained in Abstract, Sections 1.2, 2.1, 2.4, 2.5, and 3.0. The changes made are highlighted in the paper.
Comments and Suggestions:
I would suggest to the authors to focus on a specific topic for describing the project’s motivation, later, mentioning the other benefits observed/expected from the project.
Response:
The paper actually focuses on the search on a specific topic in home energy management system (HEMS). In this study, we designed a HEMS with a 10 kWh energy storage system and a 2 kWp photovoltaic system. That is, the research problem is clearly defined, the motivation is clearly explained, research outcomes and benefits are emphasized in Abstract of the paper.
Comments and Suggestions:
In this paper, the authors present 3 algorithms for evaluating the best economic performance for a household while coordinating appliances utilization for 24 hours. However, I wonder how uncertainty fits in this evaluation since, the operation is presented as predictable and the demand cycles looking at the best conditions for power generation (PV). Otherwise, the paper looks interesting, the practical applicability of the content presented here is still debatable given the control level required as well as the origin of the price signals. I think this publication can work for enabling a discussion around this topic.
Response:
Although solar radiation values and electricity prices contain uncertainty, these data are obtained directly from the meteorology data centre and the smart grid utility, respectively. All calculations are regularly made according to this data acquired. Practical application and controlling the system operation are explained in the relevant sections as much as possible and all the explanations are highlighted.
Author Response File: Author Response.pdf
Reviewer 2 Report
The authors presented Improved low-cost home energy management considering users’ preferences for photovoltaic and energy storage systems.
Overall, the paper is not well-written from an optimization standpoint. It remains totally unclear what the decision variables are. Because the mathematical programming models are never explicitly stated, it was not possible for me to identify what type of model the authors were proposing. For example, how is relation (2) modelled? Do you use binary decision variables? I got the impression that all constraints are linear, thus, MILP models are proposed.
The authors fail to justify the use of non-linear relationships in (14), 15), (16) (17). Why not use a MILP model, for example, introducing penalties via piecewise linear models? The choice of the power of 2, power of n and the division of the total power consumption seem arbitrary and not "naturally" given.
The authors used a heuristic method (genetic-based SFLA) to solve the resulting non-convex MINLPs. However, there are exact solution methods such as BARON, ANTIGONE, LINDOGLOBAL or SCIP. Why not use those methods and see if the (rather small) models can be solved globally?
It remains also unclear why you keep on stating that you are using genetic-based SFLA for the electricity load scheduling. It is not relevant how you obtain the electricity prices - after all, you are not proposing to forecast these prices prior to scheduling. Any method to obtain such price forecasts would suffice the purpose of your paper. Therefore, why not just use any available price forecasts?
Conclusions should be extended
It is assumed that each number in the time horizon represents one hour. Nevertheless, there are equipment that are used for less than one hour.
A lot of acronyms are defined in the Abstract and the whole manuscript. Some of them are not even used. The Abstract is too long.
Some document parts are difficult to read (e.g. section 3). Some sentences are not well connected, and there are many grammar mistakes.
This paper is difficult to read for the reviewer because the definitions of many equations and symbols are unclear. So, this paper should rewrite for publication.
Author Response
Reviewer 2
Comments and Suggestions for Authors
The authors presented improved low-cost home energy management considering users’ preferences for photovoltaic and energy storage systems.
Comments and Suggestions:
Overall, the paper is not well-written from an optimization standpoint. It remains totally unclear what the decision variables are. Because the mathematical programming models are never explicitly stated, it was not possible for me to identify what type of model the authors were proposing. For example, how is relation (2) modelled? Do you use binary decision variables? I got the impression that all constraints are linear, thus, MILP models are proposed.
Response:
-The relevant section of the paper is rewritten from an optimization standpoint in order to obtain a better understanding. The decision variables are stated in the first paragraph in Section 2.5.
- Mathematical models were reviewed again and unclear points were clarified. For instance, in Equation 2, charging or discharging of the ESS at any time interval, or none at all, is expressed depending on the constraints and charge and discharge efficiencies. During the operation of the whole system, we have three cases occurred in the ESS. 1) ESS charging, 2) ESS discharging, 3) ESS neither charging nor discharging. In the model given in Equation 2, these three cases are modelled with the values of 1, -1 and 0, respectively, of u(t). In fact, what is used here is a set of decision variables, consisting of -1, 1, and 0, rather than binary numbers. Similar problems have been solved with different approaches using MILP in the literature. One of them is even given in the reference section. However, in the solution of the problem discussed here, it is seen from the literature research that metaheuristic methods give better results specific to the problem.
Comments and Suggestions:
The authors fail to justify the use of non-linear relationships in (14), 15), (16) (17). Why not use a MILP model, for example, introducing penalties via piecewise linear models? The choice of the power of 2, power of n and the division of the total power consumption seem arbitrary and not "naturally" given.
Response:
The results obtained from the proposed method are compared with the results of a study that solved the same problem with MILP with different approaches. Perhaps it would be more appropriate for another study to revisit this problem to solve it with MILP.
Comments and Suggestions:
The authors used a heuristic method (genetic-based SFLA) to solve the resulting non-convex MINLPs. However, there are exact solution methods such as BARON, ANTIGONE, LINDOGLOBAL or SCIP. Why not use those methods and see if the (rather small) models can be solved globally?
Response:
As it is known, metaheuristic methods are mostly preferred for the solution of these and similar problems in the literature. It would be more appropriate to use the proposed methods such as BARON, ANTIGONE, LINDOGLOBAL, SCIP in another study for the solution of the problem under consideration, in terms of confirming the results of the study.
Comments and Suggestions:
It remains also unclear why you keep on stating that you are using genetic-based SFLA for the electricity load scheduling. It is not relevant how you obtain the electricity prices - after all, you are not proposing to forecast these prices prior to scheduling. Any method to obtain such price forecasts would suffice the purpose of your paper. Therefore, why not just use any available price forecasts?
Response:
It is assumed that the hourly electricity prices used in the solution of the aforementioned problem are sent one day in advance by the smart grid utility. For this reason, price estimation is not included in this study.
Comments and Suggestions:
Conclusions should be extended.
Response:
Conclusion is extended from various aspects.
Comments and Suggestions:
It is assumed that each number in the time horizon represents one hour. Nevertheless, there are equipment that are used for less than one hour.
Response:
In reality, there may be smart home appliances that work less than an hour. It is certain that this will reduce the daily energy consumption cost but the selection of 1 hour in this study is for one-to-one comparison with the work done in the literature.
Comments and Suggestions:
A lot of acronyms are defined in the Abstract and the whole manuscript. Some of them are not even used. The Abstract is too long.
Response:
The number of acronyms used in the study has been reduced as much as possible. The abstract has been rearranged.
Comments and Suggestions:
Some document parts are difficult to read (e.g. section 3). Some sentences are not well connected, and there are many grammar mistakes.
Response:
Section 3 has been revised to make it easier to understand. The symbols used in the equations are defined in an understandable way after the equation.
Comments and Suggestions:
This paper is difficult to read for the reviewer because the definitions of many equations and symbols are unclear. So, this paper should rewrite for publication.
Response:
In order to make the text of the article easier to understand for the readers, necessary revision and improvement were made within the framework of suggestions and comments. All these are highlighted in the paper.
Author Response File: Author Response.pdf
Reviewer 3 Report
This paper introduced an optimization algorithm for rescheduling the operation of home appliances for minimum cost, user discomfort, and peak-to-average ratio. This paper can be considered for publication after considering the following comments:
1- The main contribution of this paper is the use of a management strategy for rescheduling home appliance by minimizing cost, UD, and PAR using genetic-based SFLA optimization algorithm. As the authors know the management strategy has been introduced in many studies and the optimization algorithm is not new too, so please try to concentrate to show the innovation introduced in this paper. This innovation should be clearly stated in the abstract and in the end of the introduction section.
2- The author should clearly state the optimization variables, I think these variables are the starting and end time of the operation of each appliance which is not clearly stated in the paper. Moreover, Eqn. (6) is not clear and the variables shown in this equation should be clearly defined it is better to draw an example that represents these variables to help readers understand them. Moreover, why you added “-1” in the constraint of Eqn. (6)?! This part is not clear at all.
3- The authors did not show why they used the genetic-based SFLA instead of other optimization algorithms and what are the benefits compared to the other optimization algorithms in terms of convergence time and failure rate.
4- The authors did not show how they implement this strategy and how it can help customers in optimal operating their appliances. Do they should run the program hourly to know the optimal time to schedule their appliance? All these points are not clear and it will give the readers a very hard time following.
5- In line 352, the authors suggested using the ratio between the purchased and sold cost by one for simplicity, what is the complexity involved if it is taken as a dynamic value which is the main target of the smart grid and smart home applications? Moreover, why the maximum possible total daily cost that is 837.4¢? as shown in Line 353?
6- What is the logic the authors follow to choose the weight coefficients of Eqn. (14)? And what is the performance of the optimization in case these values are different?
7- Please explain in more detail what you mean by the sentence shown in Lines 568-570 “It should be noted here that the capacity of the ESS, the amount of solar radiation, and the installed capacity of the PV system are key parameters in finding optimal solutions.” Did you use this optimization to optimally design of the PV and ESS capacity?
8- As the authors know lead-acid batteries are fast degraded and their capacity reduces very fast but unfortunately, the authors did not consider this important issue in the management of the power and energy or in the cost estimation which for sure gives inaccurate results.
9- The paper contains many typos which need more revision from the authors and proofreading from language experts, for example, line # 101 “problem must be solved by s well-tuned technique.”
Author Response
Reviewer 3
Comments and Suggestions for Authors:
This paper introduced an optimization algorithm for rescheduling the operation of home appliances for minimum cost, user discomfort, and peak-to-average ratio. This paper can be considered for publication after considering the following comments:
Comments and Suggestions:
1-The main contribution of this paper is the use of a management strategy for rescheduling home appliance by minimizing cost, UD, and PAR using genetic-based SFLA optimization algorithm. As the authors know the management strategy has been introduced in many studies and the optimization algorithm is not new too, so please try to concentrate to show the innovation introduced in this paper. This innovation should be clearly stated in the abstract and in the end of the introduction section.
Response:
The novelty in this study is clearly expressed in the abstract as well as in section 1.2.
Comments and Suggestions:
2-The author should clearly state the optimization variables, I think these variables are the starting and end time of the operation of each appliance which is not clearly stated in the paper. Moreover, Eqn. (6) is not clear and the variables shown in this equation should be clearly defined it is better to draw an example that represents these variables to help readers understand them. Moreover, why you added “-1” in the constraint of Eqn. (6)?! This part is not clear at all.
Response:
-The optimization variable is operation start time of each smart home appliance and it is stated in the paper.
-Equation 6 was rearranged and made more understandable with examples.
-In Equation 6, the meaning of “-1” is explained.
Comments and Suggestions:
3-The authors did not show why they used the genetic-based SFLA instead of other optimization algorithms and what are the benefits compared to the other optimization algorithms in terms of convergence time and failure rate.
Response:
In the problem at hand, the operation of a controllable smart home appliance can be represented by 1 and not by 0. Thus, the operation of any smart electrical home appliance in an operation range can be expressed by a binary string, and random possible binary solutions can be generated for each electrical household appliance. It can be said that the ability to solve this non-convex complex optimization problem with binary-coded GA is better than other methods due to its advanced operators. The main reason for this is that there are two advanced genetic operators in GA, such as crossover and mutation. With these operators, the possibility of producing different individuals in the population, especially by crossover, is higher than other methods so that; the specific crossover operator can be developed for the problem. With mutation, it is possible to overcome problems such as early convergence in the genetic process. Although SFLA is similar to other metaheuristic methods, it converges to the global best individual in a short time by continuously improving the worst individual in the population. Thus, it presents an advantageous condition in finding the global optimal. A new crossover technique specifically developed and combining these prominent advantageous features of GA and SFLA as GA-SFLA and it is presented as a new approach to solution to the problem. It is seen that metaheuristic methods such as PSO, GWO, BFO give similar results to the results obtained with SFLA or GA but GA-SFLA.
The rate of convergence and failure rate in GA-SFLA vary according to the nature of the problem. Considering this problem, the convergence rate is less than 1 minute in offline operation, depending on the required parameter values. It is known from the literature that the problem solving capacity of GA is higher than other metaheuristic methods. Here, it has been tried to make it better with problem-specific improvements.
Comments and Suggestions:
4-The authors did not show how they implement this strategy and how it can help customers in optimal operating their appliances. Do they should run the program hourly to know the optimal time to schedule their appliance? All these points are not clear and it will give the readers a very hard time following.
Response:
If the user desires to optimally schedule the smart home appliances, he/she acquires the optimal scheduling results by running the software after receiving the hourly electricity prices and solar radiation data for the next day from the smart grid utility and the meteorological data station. These results are then transmitted to the energy controller unit and presented to the user's approval on the screen. If the user desires to comply with this schedule for the next day, he/she performs the optimal scheduling by giving the necessary approval on the energy controller screen shown in Figure 1. If the user does not approve, he/she continues to use the electrical home appliances manually. Optimal scheduling is done daily basis, and each household appliance starts working at the starting time specified in the schedule and works for the operation length specified.
Comments and Suggestions:
5- In line 352, the authors suggested using the ratio between the purchased and sold cost by one for simplicity, what is the complexity involved if it is taken as a dynamic value which is the main target of the smart grid and smart home applications? Moreover, why the maximum possible total daily cost that is 837.4¢? as shown in Line 353?
Response:
Equation 8 expresses the total cost proportionally and is also expressed as an objective function for the single optimization process. The value of 837.4¢ is the maximum daily total cost possible corresponding to the highest value of 27.5¢ among the current hourly electricity prices. Very short-term changes in electricity prices such as 1 or 5 minutes, hourly or less optimal scheduling, performance of the battery depending on the environmental effects, temperature changes, surface contamination, shading of the PV require a complex calculation in finding the daily cost and makes the solution of the problem difficult.
Comments and Suggestions:
6- What is the logic the authors follow to choose the weight coefficients of Eqn. (14)? And what is the performance of the optimization in case these values are different?
Response:
The selection of weight coefficients was chosen at these values in order to compare with a previous study. The logic used in this selection is that in the optimization problem here, the weights of the existing objectives were determined arbitrarily. As the weight coefficient decreases, the optimal value of the objective function in question increases, and in the opposite case, the optimal value decreases. The effects of weight coefficients are shown in Tables 8 and 9.
Comments and Suggestions:
7-Please explain in more detail what you mean by the sentence shown in Lines 568-570 “It should be noted here that the capacity of the ESS, the amount of solar radiation, and the installed capacity of the PV system are key parameters in finding optimal solutions.” Did you use this optimization to optimally design of the PV and ESS capacity?
Response:
What is meant to be here is that; optimal values will change with the change of ESS capacity or daily amount of solar radiation or PV system capacity. For this reason, it should be said that the three factors mentioned above are effective together with the proposed method for lower cost or PAR values. However, this statement seems to have led to misunderstanding so that it was reworded.
No. This optimization was not used for optimal design of PV and ESS capacity.
Comments and Suggestions:
8-As the authors know lead-acid batteries are fast degraded and their capacity reduces very fast but unfortunately, the authors did not consider this important issue in the management of the power and energy or in the cost estimation which for sure gives inaccurate results.
Response:
It is apparent that the ESS is not charged and discharged intensively in the system under consideration. For this reason, the more costly Li-Fe-PO4 battery was not preferred in the first place. As is known, lead-acid batteries are currently used in PV systems to store energy in homes. However, while lead-acid batteries provide advantages in terms of cost, their lifetime, charge and discharge characteristics and performance are disadvantageous compared to Li-FePO4 batteries. However, even though the unit cost of LIFePO4, which is produced in accordance with standards for long-term use and more dynamic systems, is 2 times compared to lead acid batteries, it would be more appropriate to prefer LIFePO4 battery.
Comments and Suggestions:
9-The paper contains many typos which need more revision from the authors and proofreading from language experts, for example, line # 101 “problem must be solved by s well-tuned technique.”
Response:
The article has been thoroughly checked and grammatical and spelling errors have been corrected.
Author Response File: Author Response.pdf
Reviewer 4 Report
The authors present interesting research on Improving Low-Cost Home Energy Management considering user preferences with photovoltaic and energy storage systems. The optimization proposal is relevant and may be attractive to the specialty literature. I consider that the article can be published.
Author Response
I thank the reviewer for his/her kind consideration.
