Wind Energy Generation Assessment at Specific Sites in a Peninsula in Malaysia Based on Reliability Indices
Round 1
Reviewer 1 Report
The abstract is not clear. It is not sure what the authors want to achieve. Is the objective to select wind farm locations or to improve power system reliability or to determine how many wind turbines with a reference model type?
In the paper, Roy Billinton Test System (RBTS) is considered and tested using wind data from two sites in Peninsular Malaysia,…. (given in Abstract). Why do you use wind data to test this system? There is no description about this system and there is no reason given to tell why this system is to be tested in the research.
The knowledge in Section 2 is all well known.
The title of Section 3 is not adequate. This section gives assessment of the selected generation system adequacy. What do you mean “reliability assessment for generation system adequacy”? In fact, there is nothing shown about using reliability knowledge or methods in this section.
You mentioned “reliability indices” many times in the text. However, these indices are not defined or described. In Section 3, the authors mentioned ‘the reliability level of the power generating systems’, ‘the overall reliability of generating systems adequacy’; there is nothing related to reliability analysis or reliability assessment of power generation system. These indices should be referred to different aspects that affect the power generation system reliability and how to define them.
It needs a lot of clarifications in the text. In addition, there are so many obvious English Grammar errors that make the readers confused about what you wish to talk about.
Author Response
Manuscript ID: processes - 494092
Type of manuscript: Article
Title: Wind Energy Generation Assessment at Specific Sites in Peninsula
Malaysia Based on Reliability indices
Authors: Athraa Ali Kadhem *, Noor Izzri Abdul Wahab *, Ahmed N. Abdalla
Received: 12 April 2019
Dear Reviewers,
The author would like to thank to all reviewers for their valuable comments. We have tried to respond to all the comments by the reviewers. Hopefully, reviewers are satisfied with our comments.
----------- Review 1-----------
Comments and Suggestions for Authors
1. The abstract is not clear. It is not sure what the authors want to achieve. Is the objective to select wind farm locations or to improve power system reliability or to determine how many wind turbines with a reference model type?
Answer:
Thank you for the comments. Corrections are duly made by adding an explanation in the abstract to make it more clear about what can be achieved via this paper, please refer to abstract.
Abstract: This paper presents the statistical analysis of wind speed data that can be extremely useful for installing a wind generation as a stand-alone system. The main objective is to maximize the wind power capacity contribution to generation systems adequacy to select wind farms locations at specific sites in Malaysia. The combined Sequential Monte Carlo simulation (SMCS) technique and the Weibull distribution model is employed to demonstrate the impact of wind power in power system reliability. To study this, Roy Billinton Test System (RBTS) is considered and tested using wind data from two sites in Peninsular Malaysia, Mersing and Kuala Terengganu, and one site Kudat in Sabah. The results showed that Mersing and Kudat were best suitable for wind sites. In addition, the reliability indices are compared prior to and the addition of the two wind farms to the considered RBTS system. The results reveal that the reliability indices are slightly improved for RBTS system with the wind power generation from both the potential sites.
2. In the paper, Roy Billinton Test System (RBTS) is considered and tested using wind data from two sites in Peninsular Malaysia,…. (given in Abstract). Why do you use wind data to test this system? There is no description about this system and there is no reason given to tell why this system is to be tested in the research.
Answer:
Thank you for the comments. Corrections are duly made by adding the explanation and description on the Roy Billinton Test System (RBTS). Please refer to section 3.1.
The RBTS is an essential reliability test system that is the work of the University of Saskatchewan (Canada) for educational and research purposes. The RBTS has 11 conventional generating units each having a power capacity ranging of around 5 - 40 MW, with an installed capacity of 240 MW. Figure 1 shows the single line diagram for the RBTS and the detailed reliability data for the generating units in the test system are shown in Appendix A.
Figure 1: Single line diagrams of the RBTS.
Appendix A
Table A1: The RBTS generating unit ratings and reliability data
Units No. | Unit size | FOR | MTTF | MTTR |
1 | 5 | 0.01 | 4380 | 45 |
2 | 5 | 0.01 | 4380 | 45 |
3 | 10 | 0.02 | 2190 | 45 |
4 | 20 | 0.02 | 3650 | 55 |
5 | 20 | 0.02 | 3650 | 55 |
6 | 20 | 0.02 | 3650 | 55 |
7 | 20 | 0.02 | 3650 | 55 |
8 | 20 | 0.03 | 1752 | 45 |
9 | 40 | 0.02 | 2920 | 60 |
10 | 40 | 0.03 | 1460 | 45 |
11 | 40 | 0.03 | 1460 | 45 |
3. The knowledge in Section 2 is all well known.
Answer:
Thank you for the comments.
4. The title of Section 3 is not adequate. This section gives assessment of the selected generation system adequacy. What do you mean “reliability assessment for generation system adequacy”? In fact, there is nothing shown about using reliability knowledge or methods in this section.
Answer:
Thank you for the comments. We agreed to change the title of section 3 and also, adding the proposed methodology according to the reviewer suggestion to give more description for the reliability assessment for generation systems as follows. Please refer to section 3.
3. Reliability Assessment for Generation Systems
3.1. Fundamental Reliability indices
3.2. Proposed Methodology
5. You mentioned “reliability indices” many times in the text. However, these indices are not defined or described. In Section 3, the authors mentioned ‘the reliability level of the power generating systems’, ‘the overall reliability of generating systems adequacy’; there is nothing related to reliability analysis or reliability assessment of power generation system. These indices should be referred to different aspects that affect the power generation system reliability and how to define them.
Answer:
Thank you for the comments. Yes, we have added some explanations so that these indices were defined more comprehensive. Also, the contribution of this paper is highlighted via wind energy penetration in electrical power systems through employed these indices and shown effect to the overall reliability of generating systems adequacy. Please refer to section 3.1 & 3.2.
3.1. Fundamental Reliability indices
The Load and generation models are conjoined to produce the risk model of the system. Indices that evaluate the system reliability and adequacy can be used to forecast the reliability of the power generating system.
The fundamental reliability indices evaluated in this work are adapted to enable the estimation of the reliability level of the power generating systems comprising of Loss of Load Frequency (LOLF), Loss of Energy Expectation (LOEE), Loss of Load Duration (LOLD), and Loss of Load Expectation (LOLE).
Now, LOLE represents the reliability standard used in many countries [3]. A level of LOLE is usually used as reliability criteria of the generation systems. The standard level to LOLE of one-day-in ten years or less. This does not mean a full day of shortages once every ten years; rather, it refers to the total accumulated time of shortages that should not exceed one day in ten years.
The Sequential Monte Carlo simulation (SMCS) method enables accurate evaluation of reliability indices. The true evaluation of the reliability assessment for the overall reliability of generating systems adequacy containing wind energy, an SMCS method was used technique coupled with Weibull distribution model to generate and repeat the wind speed. The RBTS is an essential reliability test system that is the work of the University of Saskatchewan (Canada) for educational and research purposes. The RBTS has 11 conventional generating units each having a power capacity ranging of around 5 - 40 MW, with an installed capacity of 240 MW. Figure 1 shows the single line diagram for the RBTS and the detailed reliability data for the generating units in the test system are shown in Appendix A. Load model is generally represented as chronological Load Duration Curve (LDC) which used along with different search techniques. The LDC will generate values for each hour, hence, there will be 8,736 individual values recorded for each year. The chronological LDC hourly load model is shown in Figure 2 was utilized, and the system peak load is 185 MW. Besides the traditional generators, the wind farm comprised 53 identical WTG units with rated power of 35 kW, each of which was considered in the current study. A peak load of 1% of penetrated wind energy in RBTS system, which has a peak load of 185 MW.
3.2. Proposed Methodology
The basic simulation procedures for applying the SMCS with Weibull model in calculating reliability indices for the electrical power generating systems with wind energy penetration are based on the steps are briefly summarized:
Step1: The generation of the yearly synthetic wind power time series employing a Weibull model, as follows:
• Set the Weibull distribution parameters k and c.
• Generate a uniformly distributed random number U between [0, 1].
• Determined the artificial wind speed v with Equation (10).
(10) |
• Set the WTG’s Vci, Vr, and Vco wind speeds.
• Determine the constants A, Bx, and Cx with equations below.
• Calculate the WTG output power using equation (11).
(11) |
where, ws = wind speed (m/s), Vci = WTG cut-in speed (m/s), Vco = WTG cut-out speed (m/s), Vr = WTG rated speed (m/s), Pr = WTG rated power output (MW). The constants A, Bx, and Cx have previously been calculated by [3].
Step2: Creating the total available capacity generation by a combination of the synthetic generated wind power time series and chronological conventional generating system model by employing SMCS, as follows:
• Define the maximum number of years (N) to be simulated and set the simulation time (h), (usually one year) to run with SMCS.
• Generated uniform random numbers to operation cycle (up-down-up), for each of the conventional units in the system by using the unit’s annual MTTR (mean time to repair) and λ (failure rate) values.
• The component‘s sequential state transition processes within the time of all components are then added to create the sequential system state.
• Define the system capacity by aggregating the available capacities of all system components by combining the operating cycles of generating units and the operating cycles with the WTG available hourly wind at a given load level.
• Superimpose the available system capacity curve on the sequential hourly load curve to obtain the system available margin. A positive margin denotes sufficient system generation to meet the system load whereas a negative margin suggests system load shedding.
• The reliability indices for a number of sample years (N) can be obtained using equations 12 to 18 respectively.
(12) |
(13) |
where, i = 1, 2… N, N = number of years simulated, Ф(sji) = index function analogous to jth occurrence within the year i, j = 1, 2…, nj(s), nj(s) number of system state occurrences of (sj) in the year i, sj = ssuccess ᴜ sfailure is the set of all possible states (sj) (i.e. the state-space), content two subspaces ssucess of success state and sfailure of failure states.
(14) |
(15) |
where, ∆Pj×T is the amount of curtailing energy in the failed state (sji).
(16) |
(17) |
∆λj, is the sun of the transition rates between sj and all the ssuccess states attained from sj in one transition.
(18) |
• If (N) is equal to the maximum number of years, stop the simulation; otherwise set (N=N+1), (h=0), then return to move 2 and repeat the attempt.
Step3: Evaluating and updating the outcome of the test function for the reliability indices evaluation. The above procedure is detailed in the form of flowchart as represented in Figure 3.
6. It needs a lot of clarifications in the text. In addition, there are so many obvious English Grammar errors that make the readers confused about what you wish to talk about.
Answer:
Thank you again for the comments. The paper had gone through two times proofreading by the co-authors and by the editor language team that we engaged.
Reviewer 2 Report
Dear authors,
Thank you for the changes. This reviewer considers that the paper is much more clear now. Just one small change:
Ref. [23] is bad-written. Some authors are missing: https://ieeexplore.ieee.org/abstract/document/8656477
Author Response
Manuscript ID: processes - 494092
Type of manuscript: Article
Title: Wind Energy Generation Assessment at Specific Sites in Peninsula
Malaysia Based on Reliability indices
Authors: Athraa Ali Kadhem *, Noor Izzri Abdul Wahab *, Ahmed N. Abdalla
Received: 12 April 2019
Dear Reviewers,
The author would like to thank to all reviewers for their valuable comments. We have tried to respond to all the comments by the reviewers. Hopefully, reviewers are satisfied with our comments.
----------- Review 2-----------
Comments and Suggestions for Authors
Thank you for the changes. This reviewer considers that the paper is much more clear now. Just one small change:
Ref. [23] is bad-wrtten. Some authors are missing. https://ieeexplore.ieee.org/abstract/document/8656477
Answer:
Thank you for the comments. We agreed to rewrite the Reference [23] according to the reviewer recommendation as follow:
[23] MOLINA-GARCÍA, A., FERNÁNDEZ-GUILLAMÓN, GÓMEZ-LÁZARO, E., HONRUBIA-ESCRIBANO, A., and BUESO, M.C., 2019. Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm. IEEE Access, 7, pp. 30890-30904.
Reviewer 3 Report
This paper shows the statistical analysis of wind speed data using combined Sequential Monte Carlo simulation technique and the Weibull distribution model. This paper reads well but still missing the methodology details. Reader would like to see the details of the above mentioned techniques. There are many figures that wasn't fully described and I would like to see more details. I would also recommend to use tables as appendix and mention the key points in text.
Author Response
Manuscript ID: processes - 494092
Type of manuscript: Article
Title: Wind Energy Generation Assessment at Specific Sites in Peninsula
Malaysia Based on Reliability indices
Authors: Athraa Ali Kadhem *, Noor Izzri Abdul Wahab *, Ahmed N. Abdalla
Received: 12 April 2019
Dear Reviewers,
The author would like to thank to all reviewers for their valuable comments. We have tried to respond to all the comments by the reviewers. Hopefully, reviewers are satisfied with our comments.
----------- Review 3-----------
Comments and Suggestions for Authors
1. This paper shows the statistical analysis of wind speed data using combined Sequential Monte Carlo simulation technique and the Weibull distribution model. This paper reads well but still missing the methodology details. Reader would like to see the details of the above mentioned techniques. There are many figures that wasn't fully described and I would like to see more details. I would also recommend to use tables as appendix and mention the key points in text.
Answer:
Thank you for the comments. We agreed to add the techniques and proposed methodology details used in this paper according to the reviewer recommendation. Please refer to section 3.2.
3.2. Proposed Methodology
The basic simulation procedures for applying the SMCS with Weibull model in calculating reliability indices for the electrical power generating systems with wind energy penetration are based on the steps are briefly summarized:
Step1: The generation of the yearly synthetic wind power time series employing a Weibull model, as follows:
• Set the Weibull distribution parameters k and c.
• Generate a uniformly distributed random number U between [0, 1].
• Determined the artificial wind speed v with Equation (10).
(10) |
• Set the WTG’s Vci, Vr, and Vco wind speeds.
• Determine the constants A, Bx, and Cx with equations below.
• Calculate the WTG output power using equation (11).
(11) |
where, ws = wind speed (m/s), Vci = WTG cut-in speed (m/s), Vco = WTG cut-out speed (m/s), Vr = WTG rated speed (m/s), Pr = WTG rated power output (MW). The constants A, Bx, and Cx have previously been calculated by [3].
Step2: Creating the total available capacity generation by a combination of the synthetic generated wind power time series and chronological conventional generating system model by employing SMCS, as follows:
• Define the maximum number of years (N) to be simulated and set the simulation time (h), (usually one year) to run with SMCS.
• Generated uniform random numbers to operation cycle (up-down-up), for each of the conventional units in the system by using the unit’s annual MTTR (mean time to repair) and λ (failure rate) values.
• The component‘s sequential state transition processes within the time of all components are then added to create the sequential system state.
• Define the system capacity by aggregating the available capacities of all system components by combining the operating cycles of generating units and the operating cycles with the WTG available hourly wind at a given load level.
• Superimpose the available system capacity curve on the sequential hourly load curve to obtain the system available margin. A positive margin denotes sufficient system generation to meet the system load whereas a negative margin suggests system load shedding.
• The reliability indices for a number of sample years (N) can be obtained using equations 12 to 18 respectively.
(12) |
(13) |
where, i = 1, 2… N, N = number of years simulated, Ф(sji) = index function analogous to jth occurrence within the year i, j = 1, 2…, nj(s), nj(s) number of system state occurrences of (sj) in the year i, sj = ssuccess ᴜ sfailure is the set of all possible states (sj) (i.e. the state-space), content two subspaces ssucess of success state and sfailure of failure states.
(14) |
(15) |
where, ∆Pj×T is the amount of curtailing energy in the failed state (sji).
(16) |
(17) |
∆λj, is the sun of the transition rates between sj and all the ssuccess states attained from sj in one transition.
(18) |
• If (N) is equal to the maximum number of years, stop the simulation; otherwise set (N=N+1), (h=0), then return to move 2 and repeat the attempt.
Step3: Evaluating and updating the outcome of the test function for the reliability indices evaluation. The above procedure is detailed in the form of flowchart as represented in Figure 3.
Thank you for the comments. The editing was done as per the reviewer suggestions, by to use tables as an appendix and with mention to the key points for the content of tables in a text as following, as in section 4.2. Please refer to the appendix section.
The observed wind speed data at the station was converted to 100 m height wind speed data using equation (9) and then the converted data were used to determine the wind potential. The scale c and shape k Weibull parameters were estimated by using the EM. The wind power and energy density were measured respectively, by equations (5) and (6) at height of (43.6 m) & (100 m) in Mersing and is shown in Tables 5 and 6. The rest of the calculation for wind power and energy density at heights (3.5 & 100 m) and (5.2 & 100 m) in Kudat and Kuala Terengganu respectively, can be found in Appendix B
From Tables, it is observed that the maximum power density from an actual wind speed of Mersing, Kudat, and Kuala Terengganu were found to be 52 W/m2, 19 W/m2, and 20 W/m2, respectively. However, the maximum power density of Mersing, Kudat, and Kuala Terengganu when the actual wind speed data converted to 100 m height were calculated to be 180 W/m2, 80 W/m2, and 72 W/m2 respectively. Here, it is evident that Mersing site has higher mean monthly power density compared to Kudat and Kuala Terengganu under various heights.
Appendix B
Table B1. Wind power and energy density characteristics at a height of 3.5 m in Kudat.
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) |
January | 2.65 | 3.77 | 2.931 | 14.347 | 747 | 10.717 |
February | 2.94 | 4.00 | 3.244 | 19.217 | 675 | 12.972 |
March | 2.84 | 3.55 | 3.158 | 18.213 | 747 | 13.605 |
April | 2.31 | 2.87 | 2.594 | 10.905 | 723 | 7.884 |
May | 2.07 | 2.48 | 2.339 | 8.682 | 747 | 6.485 |
June | 2.05 | 1.99 | 2.314 | 10.142 | 723 | 7.333 |
July | 2.66 | 2.43 | 3.004 | 18.648 | 747 | 13.930 |
August | 2.27 | 2.12 | 2.562 | 12.922 | 747 | 9.653 |
September | 2.39 | 2.16 | 2.698 | 14.835 | 723 | 10.725 |
October | 2.74 | 2.86 | 3.077 | 17.015 | 747 | 12.710 |
November | 2.05 | 2.80 | 2.303 | 7.723 | 723 | 5.584 |
December | 2.43 | 3.27 | 2.708 | 11.772 | 747 | 8.794 |
Annual | 2.45 | 1.84 | 2.759 | 18.819 | - | 13.738 |
Table B2. Wind power and energy density characteristics at a height of 100 m in Kudat.
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) |
January | 4.28 | 3.77 | 4.734 | 60.450 | 747 | 45.156 |
February | 4.75 | 4.00 | 5.239 | 80.946 | 675 | 54.639 |
March | 4.59 | 3.55 | 5.100 | 76.709 | 747 | 57.301 |
April | 3.73 | 2.87 | 4.190 | 45.957 | 723 | 33.227 |
May | 3.35 | 2.48 | 3.777 | 36.555 | 747 | 27.307 |
June | 3.31 | 1.99 | 3.738 | 42.753 | 723 | 30.911 |
July | 4.30 | 2.43 | 4.851 | 78.528 | 747 | 58.660 |
August | 3.66 | 2.12 | 4.137 | 54.406 | 747 | 40.641 |
September | 3.86 | 2.16 | 4.356 | 62.433 | 723 | 45.139 |
October | 4.43 | 2.86 | 4.969 | 76.777 | 747 | 57.353 |
November | 3.31 | 2.80 | 3.719 | 32.524 | 723 | 23.515 |
December | 3.92 | 3.27 | 4.373 | 49.574 | 747 | 37.032 |
Annual | 3.95 | 1.84 | 4.456 | 79.284 | - | 57.877 |
Table B3. Wind power and energy density characteristics at a height of 5.2 m in Kuala Terengganu.
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) |
January | 2.6097 | 3.24 | 2.912 | 14.685 | 747 | 10.969 |
February | 2.3543 | 3.10 | 2.632 | 11.020 | 675 | 7.439 |
March | 2.0300 | 2.54 | 2.287 | 7.991 | 747 | 5.969 |
April | 1.9203 | 2.63 | 2.161 | 6.601 | 723 | 4.772 |
May | 1.9381 | 3.16 | 2.165 | 6.089 | 747 | 4.548 |
June | 1.7760 | 2.68 | 1.997 | 5.154 | 723 | 3.726 |
July | 1.7277 | 3.14 | 1.930 | 4.324 | 747 | 3.230 |
August | 1.8242 | 3.61 | 2.024 | 4.774 | 747 | 3.566 |
September | 1.7642 | 3.16 | 1.971 | 4.594 | 723 | 3.322 |
October | 1.7993 | 2.81 | 2.020 | 5.202 | 747 | 3.886 |
November | 1.8165 | 2.87 | 2.038 | 5.288 | 723 | 3.823 |
December | 2.8554 | 2.88 | 3.203 | 20.496 | 747 | 15.310 |
Annual | 2.0338 | 2.09 | 2.293 | 9.390 | - | 6.854 |
Table B4. Wind power and energy density characteristics at a height of 100 m in Kuala Terengganu.
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) |
January | 3.98 | 3.24 | 4.443 | 52.157 | 747 | 38.961 |
February | 3.59 | 3.10 | 4.017 | 39.177 | 675 | 26.445 |
March | 3.10 | 2.54 | 3.490 | 28.396 | 747 | 21.212 |
April | 2.93 | 2.63 | 3.298 | 23.463 | 723 | 16.964 |
May | 2.96 | 3.16 | 3.303 | 21.622 | 747 | 16.152 |
June | 2.71 | 2.68 | 3.048 | 18.324 | 723 | 13.248 |
July | 2.64 | 3.14 | 2.946 | 15.378 | 747 | 11.487 |
August | 2.78 | 3.61 | 3.088 | 16.954 | 747 | 12.664 |
September | 2.69 | 3.16 | 3.007 | 16.314 | 723 | 11.795 |
October | 2.75 | 2.81 | 3.083 | 18.496 | 747 | 13.816 |
November | 2.77 | 2.87 | 3.110 | 18.793 | 723 | 13.587 |
December | 4.36 | 2.88 | 4.887 | 72.799 | 747 | 54.381 |
Annual | 3.10 | 2.09 | 3.499 | 33.366 | - | 24.356 |
Author Response File: 
Author Response.docx
Round 2
Reviewer 1 Report
Comments in detail are given below: 1. There are so many English errors in the text so that the readers cannot understand what the exact meaning is. Given a few examples: a. The main objective is to maximize the wind power capacity contribution to generation systems adequacy to select wind farms locations at specific sites in Malaysia. b. Now, LOLE represents the reliability standard used in many countries [3]. A level of LOLE is usually used as reliability criteria of the generation systems. The standard level to LOLE of one-day-in ten years or less. This does not mean a full day of shortages once every ten years; rather, it refers to the total accumulated time of shortages that should not exceed one day in ten years. (In this paragraph, LOLE is said as reliability standard. A level of LOLE is usually used as reliability criteria of the generation systems. Not sure the meaning. The reader will be confused.) ...... 2. The key words such as 'mean wind speed and Weibull parameters' are not appropriate for Keyword used. 3. The design method used in this research paper needs to be improved. RBTS is a test platform with very large power generated with each generation unit like 20MW, 20MW, 20MW, 40MW, 85MW. Does each generation unit mean a power plant? Is it a renewable power generation or a hydropower, or a nuclear power plant or a coal power plant? Does RBTS with the power generation units shown represent the national power network of Malysia? A lot of deals are not given. 4. With the two wind farms connected in, there is no much impact as the power generated from the two wind farms is very little by comparing to the whole power generated in the test network. 5. The overall purpose is to select and verify the wind farm locations. The authors need to follow the common procedure as published in articles and standards to complete the evaluation. Therefore, the research design method must be improved.
Author Response
Manuscript ID: processes - 494092
Type of manuscript: Article
Title: Wind Energy Generation Assessment at Specific Sites in Peninsula
Malaysia Based on Reliability indices
Authors: Athraa Ali Kadhem *, Noor Izzri Abdul Wahab *, Ahmed N. Abdalla
Received: 12 April 2019
Dear Reviewers,
The author would like to thank to all reviewers for their valuable comments. We have tried to respond to all the comments by the reviewers. Hopefully, reviewers are satisfied with our comments.
----------- Review 1-----------
Comments and Suggestions for Authors
1. There are so many English errors in the text so that the readers cannot understand what the exact meaning is. Given a few examples:
a. The main objective is to maximize the wind power capacity contribution to generation systems adequacy to select wind farms locations at specific sites in Malaysia.
Answer:
Thank you for the comments. We agreed to change the sentences according to the reviewer suggestion as follow:
The main objective is to define the wind power capacity contribution to generation systems adequacy to select wind farms locations at specific sites in Malaysia.
b. Now, LOLE represents the reliability standard used in many countries [3]. A level of LOLE is usually used as reliability criteria of the generation systems. The standard level to LOLE of one-day-in ten years or less. This does not mean a full day of shortages once every ten years; rather, it refers to the total accumulated time of shortages that should not exceed one day in ten years. (In this paragraph, LOLE is said as reliability standard. A level of LOLE is usually used as reliability criteria of the generation systems. Not sure the meaning. The reader will be confused.)
Answer:
Thank you for the comments. We agreed to change the sentences according to the reviewer suggestion as follow:
Now, LOLE represents the reliability index of the electrical power systems used in many countries [3]. The standard level to LOLE of one-day-in ten years or less. This does not mean a full day of shortages once every ten years; rather, it refers to the total accumulated time of shortages that should not exceed one day in ten years. Therefore, a level of LOLE, in this study, is used as a reliability index of the generation systems.
2. The key words such as 'mean wind speed and Weibull parameters' are not appropriate for Keyword used.
Answer:
Thank you for the comments. Corrections are duly made by removing not appropriate words and adding new suitable words for Keyword, to make it more related with the content of the article, please refer to a keyword section.
Keywords: Reliability indices; Wind farms; Sequential Monte Carlo simulation; Malaysia.
3. The design method used in this research paper needs to be improved. RBTS is a test platform with very large power generated with each generation unit like 20MW, 20MW, 20MW, 40MW, 85MW. Does each generation unit mean a power plant? Is it a renewable power generation or a hydropower, or a nuclear power plant or a coal power plant? Does RBTS with the power generation units shown represent the national power network of Malysia? A lot of deals are not given.
Answer:
Thank you for the comments. Corrections are duly made by the explanation and description of the Roy Billinton Test System (RBTS). Please refer to section 3.1 and Appendix A.
The RBTS is an essential reliability test system that is the work of the University of Saskatchewan (Canada) for educational and research purposes. The RBTS has 11 conventional generating units each having a power capacity ranging of around 5 - 40 MW, with an installed capacity of 240 MW and a peak load of 185 MW. Figure 1 shows the single line diagram for the RBTS and the detailed reliability data for the generating units in the test system are shown in Appendix A.
Does RBTS with the power generation units shown represent the national power network of Malaysia? A lot of deals are not given.
Answer:
Thank you for the comments. RBTS system with the power generation units does not show the same detailed of the national power network of Malaysia. So far, RBTS (or IEEE- RTS-79 & IEEE- RTS-96) system with the power generation units shown is a test system used in many studies for test the effect wind power from different sites in the world in the power systems.
Figure 1: Single line diagrams of the RBTS.
Appendix A
Table A1: The RBTS generating unit ratings and reliability data
Units No. | Unit size | FOR | MTTF | MTTR |
1 | 5 | 0.01 | 4380 | 45 |
2 | 5 | 0.01 | 4380 | 45 |
3 | 10 | 0.02 | 2190 | 45 |
4 | 20 | 0.02 | 3650 | 55 |
5 | 20 | 0.02 | 3650 | 55 |
6 | 20 | 0.02 | 3650 | 55 |
7 | 20 | 0.02 | 3650 | 55 |
8 | 20 | 0.03 | 1752 | 45 |
9 | 40 | 0.02 | 2920 | 60 |
10 | 40 | 0.03 | 1460 | 45 |
11 | 40 | 0.03 | 1460 | 45 |
4. With the two wind farms connected in, there is no much impact as the power generated from the two wind farms is very little by comparing to the whole power generated in the test network.
Answer:
Thank you for the comment. We agreed to compare the results obtained by the wind power generating from the wind farm with 53 WTGs after adding to the conventional units of RBTS, with the wind power generating from the wind farm with102 WTGs to the after adding to conventional units of RBTS. Then, compared with results of the base case which shows the reliability indices assessment of the RBTS system before adding WTGs. We have added the results in a rows number 4 & 6 in table 5, please refer to Section 5.2.
To evaluate the contribution of wind energy to the overall reliability of generating systems, Table 5 compares the reliability indices before and after adding the 53 WTGs & 106 WTGs to the conventional units of RBTS. The results obtained were compared with results obtained from SMCS method reported in [31]. The simulation process was terminated after a set number of samples (600 times) had been achieved. The results show that the reliability indices demonstrate a distinuished and slightly improved reliability of RBTS including wind power from both locations (Mersing and Kudat) by the addition of a 1.85 MW and 3.71 MW from the proposed wind farms. The LOLE and LOEE indices are typically employed to gauge the extent of benefit in assessing the wind energy of generating systems. Therefore, after adding the wind generating 1.85 MW to the system, the LOLE index is reduced 1.115 and 1.131 hour/year for Mersing and Kudat respectively, when compared with results from the base case which shows the reliability assessment of power generation system. Additionally, after adding the wind generating 3.71MW to the system, the LOLE index is reduced 0.987and 1.128 hour/year for Mersing and Kudat respectively, when compared with results from the base case which shows the reliability assessment of power generation system.
Table 5. Reliability indices at various sites in Malaysia.
Name of site | Reliability indices | |||
LOLE (hrs/year) | LOEE (MWh/year) | LOLF (occ/year) | LOLD (hrs/occ) | |
Basic RBTS system without wind generation (published) | 1.152 | 11.78 | 0.229 | 4.856 |
Basic RBTS system without wind generation (computed) | 1.161 | 10.191 | 0.230 | 5.05 |
Basic RBTS system and (53× 0.035 = 1.85 MW) wind generators at Mersing site | 1.115 | 9.744 | 0.225 | 4.944 |
Basic RBTS system and (106× 0.035= 3.71 MW) wind generators at Mersing site | 0.987 | 7.357 | 0.220 | 4.486 |
Basic RBTS system and (53 × 0.035 = 1.85 MW) wind generators at Kudat site | 1.131 | 10.948 | 0.225 | 5.012 |
Basic RBTS system and (106 × 0.035 = 3.71 MW) wind generators at Kudat site | 1.128 | 10.018 | 0.236 | 4.779 |
Also, it can be observed that this study was done with a small percentage of peak load reduction at around 1% and a small number of wind turbines, so as to demonstrate the primary effect of wind energy penetration from selected locations in Malaysia in the reliability of generation system for the RBTS.
5. The overall purpose is to select and verify the wind farm locations. The authors need to follow the common procedure as published in articles and standards to complete the evaluation. Therefore, the research design method must be improved.
Answer:
Thank you for the comments. Corrections are duly made by an explanation on the overall purpose of the present paper as follows:
The purpose of the paper is to define the wind power capacity contribution to generation systems adequacy to proposed wind farms locations at specific sites in Malaysia, so, these locations are selected Mersing, Kudat, and Kuala Terengganu, as referred in section 4:
4. Wind Speed Data Analysis at specific sites in Malaysia
Figure 4 shows the map of the locations of Malaysia Meteorological Department (MMD) stations and also depicts the strength of the wind speed distribution in proposed sites in Peninsular Malaysia. The area which showed the highest wind speed value is in red and orange colors, while other areas show moderate wind speed.
Accordingly, in the conclusion section, suggestions for future work, we recommend study how to selected and verify the wind farm locations. Please refer to the conclusion section.
Recommendations for future studies include, the statistical analysis model used for different sites in Malaysia can be extended to include more relevant factors of wind farms and evaluate their impact on wind power potential for these sites, such as; wind speeds at the installation site, types of wind turbine offshore and onshore, and numbers of the wind turbine installed according to the size of the farm.
Reviewer 3 Report
Authors revised but still I didn't see the details of the combined Sequential Monte Carlo Simulation technique. I will propose to merge the table. For example 2,3,4 can be presented as one table and again 5,6 can be one table and present them horizontally due to lots of information. Such, all information can be comparable.
Author Response
Manuscript ID: processes - 494092
Type of manuscript: Article
Title: Wind Energy Generation Assessment at Specific Sites in Peninsula
Malaysia Based on Reliability indices
Authors: Athraa Ali Kadhem *, Noor Izzri Abdul Wahab *, Ahmed N. Abdalla
Received: 12 April 2019
Dear Reviewers,
The author would like to thank to all reviewers for their valuable comments. We have tried to respond to all the comments by the reviewers. Hopefully, reviewers are satisfied with our comments.
----------- Review 3-----------
Comments and Suggestions for Authors
Authors revised but still I didn't see the details of the combined Sequential Monte Carlo Simulation technique. I will propose to merge the table. For example 2, 3, 4 can be presented as one table and again 5, 6 can be one table and present them horizontally due to lots of information. Such, all information can be comparable.
Answer:
Thank you for the comments. Details of the combined Sequential Monte Carlo Simulation technique (or the Monte Carlo simulation method cooperate with Frequency and Duration method for further system operational details) which applied in this search can be found in reference [32]. Please refer to the references section.
[32] Shi, Shuai. (2014). Operation and Assessment of Wind Energy on Power System Reliability Evaluation. PhD thesis. Department of Electronic and Electrical Engineering, University of Strathclyde
Thank you for the comments. Yes, we have agreed to merge tables as follow. The results at Table 2, 3, 4 are merged to present in Table 2 and results at Tables 5, 6 are merge to present in Table 3. Please refer to sections 4.1 & 4.2. Also, the results in Appendix B at Table B1 and B2 are merged to present at Table B1 and the results at Table B3 and B4 are merged to present at Table B2. Please refer to the appendix section.
Table 2. Monthly and annual mean wind speed (m/s) in Mersing, Kudat, and Kuala Terengganu at different heights above ground level.
wind | Months/Year | Annual mean | |||||||||||
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | ||
Mersing | |||||||||||||
mean wind speed | 4.22 | 3.89 | 3.24 | 2.36 | 2.13 | 2.45 | 2.51 | 2.65 | 2.50 | 2.44 | 2.32 | 3.24 | 2.82 |
60 m | 4.94 | 4.57 | 3.81 | 2.77 | 2.50 | 2.87 | 2.95 | 3.11 | 2.94 | 2.87 | 2.73 | 3.80 | 3.31 |
100 m | 6.38 | 5.89 | 4.91 | 3.58 | 3.23 | 3.71 | 3.80 | 4.01 | 3.79 | 3.70 | 3.52 | 4.91 | 4.27 |
Kudat | |||||||||||||
mean wind speed | 2.65 | 2.94 | 2.84 | 2.31 | 2.07 | 2.05 | 2.66 | 2.27 | 2.39 | 2.74 | 2.05 | 2.43 | 2.45 |
60 m | 3.97 | 4.41 | 4.27 | 3.47 | 3.11 | 3.08 | 4.00 | 3.41 | 3.59 | 4.12 | 3.08 | 3.64 | 3.68 |
100 m | 4.28 | 4.75 | 4.59 | 3.73 | 3.35 | 3.31 | 4.30 | 3.66 | 3.86 | 4.43 | 3.31 | 3.92 | 3.95 |
Kuala Terengganu | |||||||||||||
mean wind speed | 2.61 | 2.35 | 2.03 | 1.92 | 1.94 | 1.78 | 1.73 | 1.82 | 1.76 | 1.80 | 1.82 | 2.86 | 2.03 |
60 m | 3.70 | 3.34 | 2.88 | 2.72 | 2.75 | 2.52 | 2.45 | 2.59 | 2.50 | 2.55 | 2.58 | 4.05 | 2.89 |
100 m | 3.98 | 3.59 | 3.10 | 2.93 | 2.96 | 2.71 | 2.64 | 2.78 | 2.69 | 2.75 | 2.77 | 4.36 | 3.10 |
Table 3. Wind power and energy density characteristics at heights of 43.6 m and 100 m in Mersing.
A height of 43.6 m | A height of 100 m | ||||||||||||
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | |
January | 4.22 | 5.37 | 4.572 | 52.070 | 747 | 38.896 | 6.38 | 5.37 | 6.922 | 180.702 | 747 | 134.9841 | |
February | 3.89 | 5.68 | 4.209 | 40.532 | 675 | 27.359 | 5.89 | 5.68 | 6.373 | 140.698 | 675 | 94.97137 | |
March | 3.24 | 3.84 | 3.588 | 26.214 | 747 | 19.582 | 4.91 | 3.84 | 5.432 | 90.960 | 747 | 67.94703 | |
April | 2.36 | 3.89 | 2.612 | 10.086 | 723 | 7.292 | 3.58 | 3.89 | 3.955 | 35.014 | 723 | 25.31527 | |
May | 2.13 | 3.26 | 2.381 | 8.010 | 747 | 5.984 | 3.23 | 3.26 | 3.605 | 27.803 | 747 | 20.76847 | |
June | 2.45 | 3.67 | 2.716 | 11.488 | 723 | 8.306 | 3.71 | 3.67 | 4.112 | 39.866 | 723 | 28.823 | |
July | 2.51 | 3.37 | 2.798 | 12.859 | 747 | 9.606 | 3.80 | 3.37 | 4.237 | 44.653 | 747 | 33.35577 | |
August | 2.65 | 3.00 | 2.968 | 16.014 | 747 | 11.962 | 4.01 | 3.00 | 4.494 | 55.591 | 747 | 41.52655 | |
September | 2.50 | 3.35 | 2.788 | 12.746 | 723 | 9.215 | 3.79 | 3.35 | 4.221 | 44.232 | 723 | 31.97939 | |
October | 2.44 | 3.67 | 2.710 | 11.412 | 747 | 8.525 | 3.70 | 3.67 | 4.103 | 39.605 | 747 | 29.58467 | |
November | 2.32 | 3.88 | 2.568 | 9.590 | 723 | 6.934 | 3.52 | 3.88 | 3.887 | 33.257 | 723 | 24.04445 | |
December | 3.24 | 3.45 | 3.604 | 27.287 | 747 | 20.384 | 4.91 | 3.45 | 5.456 | 94.674 | 747 | 70.72135 | |
Annual | 2.82 | 2.25 | 3.221 | 24.370 | - | 17.790 | 4.27 | 2.25 | 4.877 | 84.595 | - | 61.754 | |
Table B1. Wind power and energy density characteristics at height of 3.5 m and 100 m in Kudat.
A height of 3.5 m | A height of 100 m | ||||||||||||
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | |
January | 2.65 | 3.77 | 2.931 | 14.347 | 747 | 10.717 | 4.28 | 3.77 | 4.734 | 60.450 | 747 | 45.156 | |
February | 2.94 | 4.00 | 3.244 | 19.217 | 675 | 12.972 | 4.75 | 4.00 | 5.239 | 80.946 | 675 | 54.639 | |
March | 2.84 | 3.55 | 3.158 | 18.213 | 747 | 13.605 | 4.59 | 3.55 | 5.100 | 76.709 | 747 | 57.301 | |
April | 2.31 | 2.87 | 2.594 | 10.905 | 723 | 7.884 | 3.73 | 2.87 | 4.190 | 45.957 | 723 | 33.227 | |
May | 2.07 | 2.48 | 2.339 | 8.682 | 747 | 6.485 | 3.35 | 2.48 | 3.777 | 36.555 | 747 | 27.307 | |
June | 2.05 | 1.99 | 2.314 | 10.142 | 723 | 7.333 | 3.31 | 1.99 | 3.738 | 42.753 | 723 | 30.911 | |
July | 2.66 | 2.43 | 3.004 | 18.648 | 747 | 13.930 | 4.30 | 2.43 | 4.851 | 78.528 | 747 | 58.660 | |
August | 2.27 | 2.12 | 2.562 | 12.922 | 747 | 9.653 | 3.66 | 2.12 | 4.137 | 54.406 | 747 | 40.641 | |
September | 2.39 | 2.16 | 2.698 | 14.835 | 723 | 10.725 | 3.86 | 2.16 | 4.356 | 62.433 | 723 | 45.139 | |
October | 2.74 | 2.86 | 3.077 | 17.015 | 747 | 12.710 | 4.43 | 2.86 | 4.969 | 76.777 | 747 | 57.353 | |
November | 2.05 | 2.80 | 2.303 | 7.723 | 723 | 5.584 | 3.31 | 2.80 | 3.719 | 32.524 | 723 | 23.515 | |
December | 2.43 | 3.27 | 2.708 | 11.772 | 747 | 8.794 | 3.92 | 3.27 | 4.373 | 49.574 | 747 | 37.032 | |
Annual | 2.45 | 1.84 | 2.759 | 18.819 | - | 13.738 | 3.95 | 1.84 | 4.456 | 79.284 | - | 57.877 | |
Table B2. Wind power and energy density characteristics at height of 5.2 m and 100 m in Kuala Terengganu.
A height of 5.2 m | A height of 100 m | ||||||||||||
Months/Year | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | ⊽ | k | c | PD (w/m2) | hours | ED(kWh/m2) | |
January | 2.6097 | 3.24 | 2.912 | 14.685 | 747 | 10.969 | 3.98 | 3.24 | 4.443 | 52.157 | 747 | 38.961 | |
February | 2.3543 | 3.10 | 2.632 | 11.020 | 675 | 7.439 | 3.59 | 3.10 | 4.017 | 39.177 | 675 | 26.445 | |
March | 2.0300 | 2.54 | 2.287 | 7.991 | 747 | 5.969 | 3.10 | 2.54 | 3.490 | 28.396 | 747 | 21.212 | |
April | 1.9203 | 2.63 | 2.161 | 6.601 | 723 | 4.772 | 2.93 | 2.63 | 3.298 | 23.463 | 723 | 16.964 | |
May | 1.9381 | 3.16 | 2.165 | 6.089 | 747 | 4.548 | 2.96 | 3.16 | 3.303 | 21.622 | 747 | 16.152 | |
June | 1.7760 | 2.68 | 1.997 | 5.154 | 723 | 3.726 | 2.71 | 2.68 | 3.048 | 18.324 | 723 | 13.248 | |
July | 1.7277 | 3.14 | 1.930 | 4.324 | 747 | 3.230 | 2.64 | 3.14 | 2.946 | 15.378 | 747 | 11.487 | |
August | 1.8242 | 3.61 | 2.024 | 4.774 | 747 | 3.566 | 2.78 | 3.61 | 3.088 | 16.954 | 747 | 12.664 | |
September | 1.7642 | 3.16 | 1.971 | 4.594 | 723 | 3.322 | 2.69 | 3.16 | 3.007 | 16.314 | 723 | 11.795 | |
October | 1.7993 | 2.81 | 2.020 | 5.202 | 747 | 3.886 | 2.75 | 2.81 | 3.083 | 18.496 | 747 | 13.816 | |
November | 1.8165 | 2.87 | 2.038 | 5.288 | 723 | 3.823 | 2.77 | 2.87 | 3.110 | 18.793 | 723 | 13.587 | |
December | 2.8554 | 2.88 | 3.203 | 20.496 | 747 | 15.310 | 4.36 | 2.88 | 4.887 | 72.799 | 747 | 54.381 | |
Annual | 2.0338 | 2.09 | 2.293 | 9.390 | - | 6.854 | 3.10 | 2.09 | 3.499 | 33.366 | - | 24.356 | |
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
There exists big flaw in this manuscript. The title does not correlate to the contents presented. The so-called reliability indices are not related to the wind power generation system reliability. They are reflecting the impact of wind intermittent characteristic on the power production. It needs more clarifications here and there in the text. The below gives a few examples of them. 1. There is no evidence to agree with the statement “The value of the scale parameter c of Weibull distribution is close to the mean wind speed in actual wind speed data, and because of that, the Weibull distribution is a reasonable fit for data.” as given in Section 2.1. 2. The description just above Equation (6) is not appropriate to Equation (6). ...... In addition, there are some obvious English Gramma errors such as the following: 3. The sentence “Though the power generation from wind intermittent in nature but very reliable for long-term and also energy policy viewpoint” in the 2nd paragraph in Introduction needs to be rephrased. 4. Last sentence in the last paragraph in Introduction needs to be revised. Overall, the research motivation is not strong enough and there is not a clear contribution to be shown in terms of research methods or techniques. It looks like a project report. Significant improvement is, therefore, required.Reviewer 2 Report
This paper presents statistical analysis of wind speed data to maximize the wind power capacity of specific sites of Malaysia. It seems to be interesting and the authors should be commended for that. However, some issues arise with the presentation of this paper.
i) In my opinion, introduction need to be rewriten. This reviewer advise the authors to include four parts: motivation, literature survey, contributions proposed in the paper and the organization of manuscript. Especially, emphasize the third part, as it is difficult to identify the novelty of this study with the previous approaches.
ii) With regard to section 2.3 (Extrapolation of wind speed at different heights), authors should include more models appart from the power law model. See for instance https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8656477, where a novelty clustering method is applied to characterization and identification patterns of wind speed.
iii) Please, consider to improve the quality of all the figures. It is difficult to read the values of X and Y axis, as well as the legend.
iv) Finally, those references previous to 2010 should be updated.
