Multi-Objective Optimization of Back-to-Back Starting for Pumped Storage Plants under Low Water Head Conditions Based on the Refined Model
Round 1
Reviewer 1 Report
This manuscript needs some revisions, see the followings:
*The introduction should be improved (The literature review is not good).
*English should be improved.
*The Abstract and Conclusion should be improved. Outstanding results should be defined as quantified in the abstract and conclusions.
*The References should be updated.
*The novelty of this article is not clear. Please more explain it.
*Better description and explanation of figures 21 to 24. The description of the results of the figures is not sufficient and convincing.
*There are some typing errors and inaccuracies in the manuscript. Please, check the paper again for any possible misprints.
*The quality of figures should be improved.
* How has the validity of the results been examined? Please mention in the text of the full article.
*The similarity index should be reduced to less than 10%.
* Introduction part needs to be extended by some of the recently published papers to show the importance of application of Multi-objective optimization in Energy systems in the good journals. The following references should be included in this manuscript:
[1] Ghazvini, M., Pourkiaei, S. M., & Pourfayaz, F. (2020). Thermo-economic assessment and optimization of actual heat engine performance by implemention of NSGA II. Renewable Energy Research and Applications, 1(2), 235-245.
[2] Beiranvand, A., Ehyaei, M. A., Ahmadi, A., & Silvaria, J. L. (2021). Energy, exergy, and economic analyses and optimization of solar organic Rankine cycle with multi-objective particle swarm algorithm. Renewable Energy Research and Applications, 2(1), 9-23.
[3] Sabbagh, O., Fanaei, M. A., Arjomand, A., & Ahmadi, M. H. (2021). Multi-objective optimization assessment of a new integrated scheme for co-production of natural gas liquids and liquefied natural gas. Sustainable Energy Technologies and Assessments, 47, 101493.
[4] Pourkiaei, S. M., Pourfayaz, F., Mehrpooya, M., & Ahmadi, M. H. (2021). Multi-objective optimization of tubular solid oxide fuel cells fed by natural gas: an energetic and exergetic simultaneous optimization. Journal of Thermal Analysis and Calorimetry, 145(3), 1575-1583.
*I hope that the authors refer to more published papers in Sustainability.
Author Response
Dear Reviewer,
Thanks for your constructive suggestions.
Concern#1: This manuscript needs some revisions, see the followings:
The introduction should be improved (The literature review is not good).
Author response: Thanks for your constructive suggestions. We added the corresponding literature review.
Author action: We updated the manuscript and the recommended and other literatures has been added. You can find the changes at line 36 and 84 in the article.
For control optimization, there are many applications of the MOPSO algorithm in energy systems, A.Beiranvand applied the MOPSO algorithm in the optimization analysis of thermal power generation[21], and M. Ghazvini proposed the Coupled multi-objective Evolutionary Approaches[22]. There are also multi-objective optimization cases in the field of materials and chemicals [23,24], which give some inspiration to this paper. However, the BTBS model is more complicated. There is no report on the optimization of the BTBS control scheme, and only some research on the start-up strategy of PSP under turbine condition have been involved. Xu et al. [25] studied the optimal control for the turbine start-up process of PSP under low head conditions…
Concern#2: English should be improved.
Author response: Thanks for your constructive suggestions. The article has been modified by the native English speaker.
Concern#3: The Abstract and Conclusion should be improved. Outstanding results should be defined as quantified in the abstract and conclusions.
Author response: Thanks for your constructive suggestions. We have revised the descriptions in the abstract and conclusions.
Author action:
Abstract: …The results show that: 1) The parameters of hydraulic-mechanical-electrical system have a significant impact on BTBS process, and the most unfavorable working condition corresponds to the lowest water head; 2) In the control schemes, a novel constant excitation voltage strategy is proposed based on the multi-objective optimization scheme. Compared with the constant excitation current strategy or single-objective, the optimization strategy proposed can considerably improve the speed overshoot and the speed stable time by at least 68.27% and 3.22% under the worst working condition. 3) It is further verified that the problem of trapping in the S-shaped region under various working conditions can be avoided by the obtained optimal control scheme. The results give prominence to the effectiveness of the proposed optimization strategy to maintain the safety and stabilization of PSP operation.
Conclusions:
The following conclusions can be drawn from the simulation results.
1) The given value and control way of excitation current, the control scheme of the governor, and the water head have great influence on the transient process of BTBS. The control scheme of excitation current and guide vane should be selected as the decision variables in the BTBS optimization; the worst BTBS condition can be identified by the lowest water head.
2) The overshoot and stable time of the speed are contradictory. The traditional single-objective optimization scheme merely considers the single objective, which is very easy to cause the unit to fall into the S-shaped area, resulting in severe fluctuations of speed and power.
3) Compared with the single-objective, the optimization strategy proposed can considerably improve the speed overshoot and the speed stable time by at least 68.27% and 3.22% under the worst working condition. The optimization results show that the multi-objective scheme is a better choice than the single-objective scheme.
4) Compared with the MOCEC scheme, when the MOCEV scheme is adopted, the overshoot, rise time and stable time are improved respectively by 68.35%, 3.7% and 3.2% in PSU-1, and 45.4%, 3.7% and 3.2% in PSU-2. So, the MOCEV scheme is better.
5) The proposed MOCEV optimal control scheme can effectively keep away from the S-shaped area and is verified by a real PSU.
Concern#4: The References should be updated.
Author response: Thanks for your constructive suggestions. We have updated the References.
Concern#5: The novelty of this article is not clear. Please more explain it.
Author response: Thanks for your constructive suggestions. The innovation of this paper has been written in the introduction.
The innovations of this paper are as follows.
(1) It is the first time to establish a nonlinear PSP model combining the electrical subsystem with the fine hydraulic and mechanical subsystem for BTBS;
(2) The effects of the hydraulic-mechanical-electrical parameters on the BTBS are comprehensively investigated on the basis of the model mentioned above;
(3) An innovative multi-objective optimization scheme is proposed for the control strategy of BTBS at low water head conditions for the first time, which is proved to be suitable for a variety of working conditions.
Concern#6: Better description and explanation of figures 21 to 24. The description of the results of the figures is not sufficient and convincing.
Author response: Thanks for your constructive suggestions. The meaning and explanation of figures 21-24 has been written into the article
Author action: We updated the manuscript
By observing Figure 21(a), it can be found that the speed overshoot of single-objective schemes is large, while the speed overshoot of multi-objective schemes is small, and the speed fluctuation of multi-objective schemes is small and the curves are more stable. Figure 21 (b) shows the PSU-2’s speed transition processes of the four schemes. It can be seen that the speed curves of single-objective schemes fluctuate more, and the speed curves of the multi-objective schemes are more stable. Figure 21(c)-(h) show the transition curves of other physical quantities. In Figure 21(c)-(d), the field current regulation processes of the four schemes have a certain fluctuation, but the current transition process curve of the MOCEV scheme can reach the steady state fastest and the fluctuation is the smallest. In Figure 21(e), the GVO adjustment process of the MOCEV scheme is faster and the oscillation times are the least. As can be seen from Figure 21(f), the peak value of rotor angle difference of the four schemes is basically the same, but the rotor angle fluctuation range of the MOCEV scheme is the smallest. As can be seen from Figure 21(g), the terminal voltage rise processes of the four schemes are slightly different. The terminal voltage rise of the single-objective optimization methods is faster than those of the multi-objective optimization methods, and the impulse generated during voltage control switching of the CEC control scheme is smaller than that of the CEV control scheme. By observing Figure 21 (h), it can be concluded that during the BTBS under low water head conditions, the multi-objective optimization method can help the PSU avoid going deep into the S-shape zone, so as to avoid the repeated oscillation of active power and falling into chaos. …
Concern#7: There are some typing errors and inaccuracies in the manuscript. Please, check the paper again for any possible misprints.
Author response: Thanks for your constructive suggestions. We have gone over the manuscript carefully and corrections were made to the manuscript.
Concern#8: The quality of figures should be improved.
Author response: Thanks for your constructive suggestions. All figures are source files, and some blurred figures have been reset in the manuscript.
Author action:
Figure 1. The flowchart of the research in this paper.
And other figures are changed, you can check the fill sustainability-1848192.
Concern#9: How has the validity of the results been examined? Please mention in the text of the full article.
Author response: Thanks for your constructive suggestions. At present, the starting mode of domestic large capacity reversible pumped storage units under pumping conditions is mainly SFC synchronous starting, and back-to-back synchronous starting is used as backup. In general, only when the power grid or the frequency converter is faulty, can the back-to-back method be used to make the unit quickly connected to the grid pumping or pumping phase modulation. Due to the high experimental cost and long debugging time, the author could not immediately verify the correctness of the results through relevant experiments. However, the numerical model in this paper has a solid theoretical basis. In the relevant literature, the models of the speed regulating system and the excitation system have been experimentally verified [31]. The model data in this paper are also derived from the real machine data, and the parameters and ranges of the numerical simulation can better reflect the actual situation. On this basis, the research on multi-objective optimization of back-to-back start-up of pumped storage units with low head has certain guiding significance for practice. Future research will add experimental validation.
Concern#10: The similarity index should be reduced to less than 10%.
Author response: Thanks for your constructive suggestions. We have further reduced the similarity index. The similarity index of the text on Turnitin platform is 5%, except for the charts and references.
Concern#11: Introduction part needs to be extended by some of the recently published papers to show the importance of application of Multi-objective optimization in Energy systems in the good journals. The following references should be included in this manuscript:
Author response: Thanks for your constructive suggestions. We updated the manuscript and the recommended literatures has been added. You can find the changes at line 84 in the article.
Best regards,
Guilin li et al.
Reviewer 2 Report
This paper proposes the multi-objective optimization of the back-to-back pumped storage plant. The paper is well organized and simulation results are well discussed. It needs some revisions:
1. Abstract: please shorten the abstract. It should be more concise and informative. The few starting lines can be shortened. The key simulation results in the last lines are perfect and should be kept.
2. In the nomenclature table, please split the symbols into variables, parameters, indices, etc.
3. There are some typos and punctuation errors. Please proofread the manuscript.
4. Section 5: proper citations of the case study are expected. Also, please elaborate on it.
5. The unit KV should be kV. Please check the entire manuscript, for example, Table A1.
Author Response
Concern#1: This paper proposes the multi-objective optimization of the back-to-back pumped storage plant. The paper is well organized and simulation results are well discussed. It needs some revisions:
Abstract: please shorten the abstract. It should be more concise and informative. The few starting lines can be shortened. The key simulation results in the last lines are perfect and should be kept.
Author response: Thanks for your constructive suggestions. We have shortened the first few lines of the summary.
Author action:
Abstract: Pumped storage plants (PSP) needs to switch frequently between various working conditions. Moreover, it is easy to fall into an S-shaped zone under low water head conditions, especially when back-to-back starting (BTBS), which reduces the stability and safety of unit operation. In this paper, a nonlinear PSP model for BTBS is established by combining electrical subsystem with a refined hydraulic-mechanical subsystem. The influences of the hydraulic-mechanical-electrical factors on the BTBS process are investigated quantitatively. Taking the speed overshoot and speed stable time as the optimization objectives, considering a variety of constraints, the multi-objective particle swarm optimization (MOPSO) algorithm is introduced to study and optimize two typical start-up strategies.
Concern#2: In the nomenclature table, please split the symbols into variables, parameters, indices, etc.
Author response: Thanks for your constructive suggestions. Symbols in the Nomenclature are divided into variables and parameters.
Author action:
Concern#3: There are some typos and punctuation errors. Please proofread the manuscript.
Author response: Thanks for your constructive suggestions. We have gone over the manuscript carefully.
Concern#4: Section 5: proper citations of the case study are expected. Also, please elaborate on it.
Author response: Thanks for your constructive suggestions. The data of the case study in this paper are from the real machine data. The strategies compared in this paper are proposed based on the analysis results of back-to-back influencing factors in Chapter 3, rather than comparing with the methods in other literatures, because there is no comparative analysis and research on excitation speed regulation strategies in back-to-back starting process. This section makes corresponding citations to the multi-objective decision method based on the relative proximity of the target.
Concern#5: The unit KV should be kV. Please check the entire manuscript, for example, Table A1.
Author response: Thanks for your constructive suggestions. The errors mentioned have been corrected.
Reviewer 3 Report
See comments attached
Comments for author File: 
Comments.pdf
Author Response
Reviewer#3, Concern#1: A very interesting research and manuscript, overall well-written and duly illustrated.
The manuscript however needs revisions on the following points.
Please check the language use throughout the text (mostly use of articles and conjugations). A few examples are given below, but an in-depth correction throughout the manuscript is required.
Author response: Thanks for your constructive suggestions. We have gone over the manuscript carefully.
Reviewer#3, Concern#2. Please define acronyms in full text when first used e.g.MO, Line 145 (it is not very common for the reviewer to have to revert to the Nomenclature)
Author response: Thanks for your constructive suggestions. We have corrected the corresponding errors.
Reviewer#3, Concern#3. Abstract. Only language corrections.
Line 12: systems
Line 13: into a S-shaped...
Line 26: based on the multi-objective...
Author response: Thanks for your constructive suggestions. We have corrected the corresponding errors.
Reviewer#3, Concern#4. Nomenclature
-general: pu is not a common expression of a dimension. In fact it is commonly used as abbreviation of polyurethane. Please define more clearly (or add pu in the abbreviations) in many of the Symbols' units.
Author response: Thanks for your constructive suggestions. We have added an explanation of pu in the nomenclature.
作者操作：
|
Nomenclature |
Td |
differential time constant (s) |
||
|
|
|
Te |
exciter time constant (s) |
|
|
Abbreviations |
Tf |
damping time constant (s) |
||
|
PSP |
pumped storage plant |
Tj |
mechanical time constant (s) |
|
|
BTBS |
back-to-back starting |
Ty |
main servomotor response time (s) |
|
|
PSU |
pump storage unit |
Ty1 |
assistant servomotor response time (s) |
|
|
CEV |
constant excitation voltage |
transient and sub-transient time constants of open-circuit d-axis (s) |
||
|
CEC |
constant excitation current |
transient and sub-transient time constants of open-circuit q-axis (s) |
||
|
LCP |
logarithmic curve projection |
synchronous, transient and sub-transient reactance of q-axis (pu) |
||
|
MOC |
method of characteristics |
synchronous, transient and sub-transient reactance of q-axis (pu) |
||
|
GVO |
gate valve opening |
|
||
|
MOCEC |
multi-objective CEC |
Variables |
||
|
MOCEV |
multi-objective CEV |
the transient and sub-transient internal EMF of d-axis (pu) |
||
|
SOCEC |
single-objective CEC |
Efd |
excitation EMF (pu) |
|
|
SOCEV |
single-objective CEV |
the transient and sub-transient internal EMF of q-axis (pu) |
||
|
ITAE |
integrated time and absolute error |
H |
piezometric head (m) |
|
|
pu |
per-unit value |
Ht |
the working head of pump-turbine (m) |
|
|
|
the current of d- and q-axis (pu) |
|||
|
Symbols |
ifd |
excitation current (pu) |
||
|
Parameters |
excitation current setting value of generator and motor (pu) |
|||
|
A |
cross sectional area of pipeline (m2) |
Ka |
amplifier coefficient (pu) |
|
|
a |
wave velocity (m/s) |
Me |
electromagnetic torque (pu) |
|
|
bp |
permanent difference coefficient (pu) |
Mt |
the moment of pump-turbine (pu) |
|
|
D |
the diameter of the turbine runner (m) |
M11 |
unit torque (N/m3) |
|
|
d |
pipeline diameter (m) |
N |
the rotational speed of the turbine (r/min) |
|
|
f |
friction coefficient (pu) |
N11 |
unit speed (m1/2/s) |
|
|
g |
acceleration of gravity (m/s2) |
N11r |
rated unit speed (m1/2/s) |
|
|
Kd |
differential gain (pu) |
Q |
the water flow rate (m3/s) |
|
|
Ke |
self-excitation coefficient of exciter (pu) |
Qt |
the flow of pump-turbine (m3/s) |
|
|
Kf |
damping coefficient (pu) |
Q11 |
unit flow (m1/2/s) |
|
|
k0 |
forward amplification factor (pu) |
Q11r |
rated unit flow (m1/2/s) |
|
|
Ki |
integral gain (pu) |
u |
controller output signal (pu) |
|
|
Kp |
proportional gain (pu) |
excitation voltage setting value of generator and motor (pu) |
||
|
Ra |
resistance of stator winding (pu) |
d- and q-axis component of the voltage (pu) |
||
|
Se |
exciter saturation factor (pu) |
Y |
guide vane opening (deg) |
|
|
Ta |
amplifier time constant (s) |
y |
main servomotor output signal (pu) |
|
|
Tb |
lead lag time constant (s) |
ω, ω* |
relative and given value angular shaft velocity (m) |
|
|
Tc |
lead lag time constant (s) |
the internal EMF of d- and q-axis (pu) |
||
|
x |
distance calculated from upstream (m) |
rotor angle (rad) |
||
Reviewer#3,Concern#5. Introduction
-generally O.K. (but check use o articles, please)-Lie 98: avoid using "we" = i was found....
Figure 1 is too complex for the readers, since also already including results. Limit to a real logic diagram (only titles of the sub-sections)
Author response: Thanks for your constructive suggestions. We have corrected the corresponding errors.
Author action: We have reset Figure 1 in the new manuscript.
Reviewer#3, Concern#6. Refined modeling
-generally O.K.
-Line 158: The equations are as follows (these are not formulas)
-Line 219: drop that and add comma after Table 1.
Author response: Thanks for your constructive suggestions. We have corrected the corresponding errors.
Reviewer#3, Concern#7. Analysis
Very much appreciated, but please use articles, e.g. Line 244: Control way of the excitation system (and a lot more in various Lines)
Author response: Thanks for your constructive suggestions. More controls and references have been added to the manuscript.
Usually, there are four control modes of excitation system: constant terminal voltage regulation, constant excitation current regulation, constant reactive power regulation, and constant power factor regulation [32]. However, in the BTBS process of large a PSU, the excitation mode is usually constant excitation current regulation or constant terminal voltage regulation.
Reviewer#3, Concern#8. Optimization
No comments, except language imperfections.
Author response: Thanks for your constructive suggestions. The article has been modified by the native English speaker.
Reviewer#3, Concern#9. Case study
-Line 396: A real.....for the simulation...
Author response: Thanks for your constructive suggestions. We have corrected the corresponding errors.
Reviewer#3, Concern#10. Conclusions
Rather descriptive. Readers want quantified findings in the Conclusions.
Author response: Thanks for your constructive suggestions. We have revised the descriptions in the abstract and conclusions.
Author action:
The following conclusions can be drawn from the simulation results.
1) The given value and control way of excitation current, the control scheme of the governor, and the water head have great influence on the transient process of BTBS. The control scheme of excitation current and guide vane should be selected as the decision variables in the BTBS optimization; the worst BTBS condition can be identified by the lowest water head.
2) The overshoot and stable time of the speed are contradictory. The traditional single-objective optimization scheme merely considers the single objective, which is very easy to cause the unit to fall into the S-shaped area, resulting in severe fluctuations of speed and power.
3) Compared with the single-objective, the optimization strategy proposed can considerably improve the speed overshoot and the speed stable time by at least 68.27% and 3.22% under the worst working condition. The optimization results show that the multi-objective scheme is a better choice than the single-objective scheme.
4) Compared with the MOCEC scheme, when the MOCEV scheme is adopted, the overshoot, rise time and stable time are improved respectively by 68.35%, 3.7% and 3.2% in PSU-1, and 45.4%, 3.7% and 3.2% in PSU-2. So, the MOCEV scheme is better.
5) The proposed MOCEV optimal control scheme can effectively keep away from the S-shaped area and is verified by a real PSU.
Round 2
Reviewer 1 Report
This article can be accepted.
