Optimal Modeling for Dynamic Response of Energy Storage Systems

Round 1

Reviewer 1 Report

This study designed the process to find suitable parameters for real energy storage systems. Using model created by  WECC and reduced it according the application condition to decrease the burden of optimization process.The results look encouraging and motivating. But some contents need be revised in order to meet the requirements of publish.

(1)The abstract should be improved. Your point is your own work that should be further highlighted.

(2)The parameters in expressions are given and explained.

(3) The method in the context of the proposed work should be written in detail

(4) In section 4, the values of parameters could be a complicated problem itself,  the authors should give the values of parameters in the used methods.

(5) The literature review is poor in this paper. You must review all significant similar works that have been done. I hope that the authors can add some new references in order to improve the reviews and the connection with the literatures. For example, 10.1109/TR.2022.3215243; 10.1109/TSMC.2020.3030792; 10.3389/fendo.2022.1057089; 10.1016/j.marstruc.2022.103338 and so on.

(6)  In Figure 19~23, the  difference is hard to see.

 

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.pdf

Reviewer 2 Report

1、 The process description of some actual energy storage systems to find suitable parameters is not detailed enough, and the actual application of WECC model and how to simplify it are not specific.

2、 The introduction is not coherent and needs to be carefully revised.

3、 Figure 14 (a) can not clearly and intuitively reflect the advantages and disadvantages of the four algorithms.

4、 The specific meaning represented by each parameter of the model has not been reasonably explained.

5、 In the flow chart part, the flow chart of IPSO and IGA itself is currently drawn. It is recommended to modify it to the flow chart of the whole process of using IPSO and IGA to realize the parameter search of the general model of the energy storage system.

6、 In the comparison of PSO, IPSO, GA and IGA, only the number of iterations is compared and analyzed, and the best fitness of the model is not considered.

7、 There are too many legends in Figure 15 and Figure 16. It is recommended to keep only the legends applied in the figure.

8、 The number of references is slightly small.

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear Authors,

please find a list of comments:

Using model created by 17 WECC and reduced it according the application condition
to decrease the burden of optimization 18 process.

-> This is not a complete prepositional phrase, please modify it.

so that they are not yet enough to fully substitute fossil fuels
-> so that they are not yet enough RELIABLE to fully substitute fossil fuels

Most internal plants of simulation software use the model defined by 46 WECC,
if the model made by the council is used as the energy storage system model,
the 47 model response is approximated to the real system response only by
adjusting internal 48 parameters, the reality is sufficient, and the modeling
time and difficulty level can be re-49 duced.
-> Most internal plants of simulation software use the model defined by 46 WECC.
The model simulates the real system response by adjusting internal parameters.

as there are considerable limitations in the appli-56 cation of these battery models
-> as there are considerable limitations in their application

In addition, the models are free of the residual capacity 57 SOC of energy storage systems,
so they are inapplicable[3]. This study used an energy 58 storage system model composed of REPC,
REEC, and REGC modules
-> NOTE: all acronyms should be defined the very first time are used
-> please change 'are free of'

The mathematical model of more complex systems has more parameters[6-9],
and 137 more parameters will result in problems in the efficiency of the
model in subsequent ap-138 plications, such as simulation and optimization
operations.
-> this sentence is unclear and/or obvious. It should be changed or deleted.

The Improved Particle Swarm Optimization (IPSO) is extended from traditional
Particle Swarm Optimization (PSO), the problems of too fast convergence and
easy falling 152 into local optimal solution and the problems derived when the
search position exceeds 153 the preset range are improved, the global optimal
solution can be found more efficiently 154 after improvement[13-16].
-> The Improved Particle Swarm Optimization (IPSO) improves the traditional
Particle Swarm Optimization (PSO). The problems of too fast convergence and
easy falling 152 into local optimal solution and the problems derived when the
search position exceeds 153 the preset range are fixes. The global optimal
solution can be found more efficiently [13-16].


Additionally, the IPSO modifies the particle position exceeding the boundary,
the 176 boundary was used as the coordinates exceeding boundary dimensions in the past,
to en-177 hance the search diversity, Ref.[14] proposed the method of Equation (6)
-> Additionally, the IPSO modifies the particle position exceeding the boundary.
It was set as the coordinates exceeding boundary dimensions in the past,
to enhance the search diversity. Ref.[14] proposed the method of Equation (6)


The GA is derived from one of the bottommost theories of biology,
the evo-192 lutionary theory, the evolution refers to the development
of organisms under the effect of 193 such natural selection as heredity
and mutation, it is the process of species elimination 194 and generation.
The evolution of organisms performs Selection, Reproduction, Crossover,
195 and Mutation of genetic factors, so that one offspring generation of
the race is more 196 adapted to the living environment than the next generation.
The genetic algorithm can 197 advance towards a better direction in learning
adaptation or searching for an optimal so-198 lution in the concept of simulated
evolution. 199
GA is a computing mode which simulates the habit of natural ecology.
It uses a ran-200 dom search mechanism, selects multiple random initial values
in the feasible solution 201 space, and evaluates the fitness of each initial
value by multi-point search. As a result, the 202 selection, reproduction,
crossover, and mutation of the next generation can be evaluated 203 to avoid
falling into the local optimal solution. However, the multi-point search will
in-204 crease the computational load, hence, how to accelerate calculation
and increase the effi-205 ciency should be considered.
-> This section should be entirely removed


In the building of a universal model of energy storage systems, the objective was
the 223 measured output active/reactive power response capability of the energy
storage system.
-> This sentence should explain the trategy of modelling, however, it is quite generic. What do you
mean with 'output active/reactive power response capabilitypower response'?

224 The universal model of energy storage systems was built by
using control system simula-225 tion tools, such as MATLAB Simulink and LabVIEW,
and the simulation model output 226 was matched with the actual measurement result
by parameter setting.
-> 224 The universal model of energy storage systems was IMPLEMENTED by
using control system simula-225 tion tools, such as MATLAB Simulink and LabVIEW,
and the simulation output 226 was matched with the actual measurement results
by tuning the parameter setting.

The objective func-227 tion of the model was established to
minimize the measured active/reactive power output 228 response of the actual energy
storage system.
-> a clear definition of "response of the actual energy
storage system" is needed

...to simulate the output response of uni-229 versal model in the case
of the same input.
-> please clarify


This study used the Rastrigin functions of two variables and 30 variables as the test-242 ing targets,
with the former being applicable to 3D mapping and easy for the users to 243
identify. The latter one increases the number of variables to increase the problem's com-244 plexity, so as to verify which algorithm
can search out the optimal solution under more 245 complex conditions.
-> very hard to follow. Please clarify

... and the lower the fitness is, the closer the model is to the response of 231 actual
energy storage system.
-> ... and the lower the fitness, the closer the model is to the response of 231 actual
energy storage system.

According to the figure, the method of PSO type excessively depends on the opti-271 mum
position of individuals and the optimum position of groups and lacks effective
mu-272 tability, so it is likely to fall into local optimal solutions.
-> very hard to follow: please define 'individuals', 'groups', 'mutability' before using them

This study assumed that the capacity of the energy storage system was good during 300
simulation, the output response was temporarily free from the limitation
of system capac-301 ity, and the response speed should be as high as possible.
-> what do you mean with "capacity of the energy storage system was good during 300
simulation, the output response was temporarily free from the limitation
of system capac-301 ity, and the response speed should be as high as possible" ?

There 310 were two classes of target parameters of key parameter analysis; the initial value
of one 311 class was not 0, and the initial value of the other class was 0
-> why the initial values are important? please comment

With the addition of 313 the original initial value, there were a total of seven experiments.
-> you are not doing any experiments, just simulations...

The first simulation result shows that the actual energy storage system equipment 380
output was not 0 when the reactive power output command of the reactive power step 381 was 0.
This is because the energy storage system must check the voltage synchronously 382
with the power grid. The step rising parts were almost overlapped.
-> this comment comes out the blue. It is a very important condition to consider, however it appears only
ath the end of the paper

Therefore, the parameters of the optimization algorithm could reflect
the response of ac-402 tual energy storage systems to some extent.
-> Very weird sentence.... Could reflect? To some extent ?

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

OK

Reviewer 3 Report

Dear Authors,

thank you for the new version.