Research on Risk Evaluation of Transnational Power Networking Projects Based on the Matter-Element Extension Theory and Granular Computing
Abstract
:1. Introduction
- A risk evaluation index system and the corresponding judging criteria of transnational networking projects are established in this paper. Based on the characteristics of transnational networking projects, a comprehensive risk evaluation index system, including four first-level indicators and eleven secondary indicators, and the corresponding judging criteria are established.
- The Global Peace Index and the National Governance Index are employed during the quantization of the related qualitative indexes. The Global Peace Index and the National Governance Index are respectively introduced in this paper during the quantization of “War or terrorist attack” and “Change of government or statute”, which are two important qualitative indicators in the index system.
- A combination weighting method is proposed in the weight determination part. This paper employs a combination of granular computing and the order relation analysis method to determine the weight of each indicator, aiming at improving the reliability of the results, which can finally help to improve the rationality of the evaluation result.
2. Method
2.1. Research Procedure
2.2. Weight Determination
- Step 1:
- Providing the index system and the evaluation criteria for the experts. All members of the expert group will assign a mark to each index independently to indicate the importance of each indicator. Suppose there are “n” experts and “m” indicators, the original data “X” can be obtained, where X = {x1, x2, ..., xn}, xi = {yi1, yi2, ..., yim}, i = 1, 2, ..., n.
- Step 2:
- Calculating the similarity relation between xi and xj, where i and j represent different experts. Then, as shown in Equation (1), the fuzzy similarity matrix of the samples can be obtained:
- Step 3:
- The quotient space family with inclusion relation, , can be calculated and different values of stand for different granular spaces.
- Step 4:
- Calculating the importance of each index in different granular spaces. The importance can be gained by:
- Step 5:
- The comprehensive importance can be calculated by Equation (3):
- Step 1:
- Obtaining the order relation. According to the available information, experts give marks to the indicators based on his own experience, the original order relation can be obtained as follows:
- Step 2:
- Calculating the importance ratio. On basis of the order relation obtained in step 1, the importance ratio of two adjacent indicators can be calculated according to the original marks provided by the expert:
- Step 3:
- Determining the weight of each indicator:
- Step 4:
- Results integration. Differences may exist between the weights determined by different experts and it is essential to make a comprehensive analysis of the results obtained from different experts. The final result of the weight can be calculated by:
2.3. Matter-Element Extension Evaluation Model
- Step 1:
- Determine the classical domain, joint domain and the object to be evaluated:
- Step 2:
- Obtain the weight of indicator. This paper employs a combination of granular computing and G1 Method to determine the weight of each indicator.
- Step 3:
- Normalization. The way of normalization is shown in Equations (12) and (13):
- Step 4:
- Calculate the correlation degree:
- Step 5:
- Draw the conclusions. According to the principle of maximum relevance, the level of the object to be evaluated can be determined based on the results obtained in Step 4.
3. Index System
3.1. Political Risk
3.2. Social and Natural Risk
3.3. Economic Risk
3.4. Technical Risk
4. Case Study
4.1. Basic Information of the Project to Be Evaluated
4.2. Weight Determination
- Step 1:
- Standardizing the original data. According to the scores of the first-level indicators, distance between different samples can be calculated by using the MATLAB. Different kinds of distance between classes, including “ward”, “complete”, “average”, “centroid” and “single”, are employed for clustering according to the characteristics of the original data. Then, the composite coefficient “Q” can be calculated to determine the optimal distance between classes, and the closer Q is to 1, the better the clustering is. The composite coefficient of first-level indicators can be calculated by using MATLAB and the results are as follows: Q = {0.8355, 0.8501, 0.8607, 0.7960, 0.7692}. The maximum value of Q is “0.8607”, which means the distance of “average” between classes is much more appropriate (the distance between samples is Euclidean distance). The result of clustering is shown in Figure 2, and the families of quotient space can also be obtained according to the clustering result (shown in Table 10).
- Step 2:
- The discrete interval provided by the expert group is applied in data discretization and then, the corresponding equivalence class can be obtained when one of the indexes is deleted:U/ind (S-{A}) = [{x1},{x2},{x3},{x4},{x5},{x6,x9,x10},{x7},{x8}]U/ind (S-{B}) = [{x1,x4},{x2,x6,x10},{x3},{x5},{x7},{x8},{x9}]U/ind (S-{C}) = [{x1},{x2},{x3},{x4},{x5},{x6,x7,x9,x10},{x8}]U/ind (S-{D}) = [{x1},{x2},{x3},{x4,x7},{x6,x10},{x8},{x9}]
- Step 3:
- On the basis of the results obtained above, the importance of each first-level index in different granularity spaces can be calculated and at the same time, the weight of first-level indicators can be determined after normalization. Table 11 shows the attribute significance and the weight of the first-level index.
- Step 4:
- Calculation of the weight of the secondary indicators. The process of determining the weight of secondary indexes by granular computing is the same as the steps above, and due to the length limitation, no more details will be repeated. Table 12 shows the weight of indicators obtained by using granular computing.
- Step 1:
- Determining the order relation. The order of first-level indexes can be obtained in the light of the scoring results.
- Step 2:
- Calculating the importance of each index. For example, an order of the four first-level indexes can be determined according to expert’s scores: BDCA. “R” represents the relative importance of two adjacent indicators, and then a series of results can be calculated according to the original data: R2 = A/C = 1.12, R3 = C/D=1.06, R4 = D/B = 1.11; w(B) = 1/(1 + R2 × R3 × R4 + R3 × R4 + R4) = 0.217, w(D) = 0.217 × 1.11 = 0.241, w(C) = 0.241 × 1.06 = 0.256, w(A) = 0.256 × 1.12 = 0.286.
- Step 3:
- Repeating the previous steps to gain the weight determined by all experts. Each expert will determine an order relation between the first-level indicators according to their own experience, and it is necessary to repeat the steps above to obtain the weights determined by different experts.
- Step 4:
- Obtaining the comprehensive weight of the first-level indicator. The comprehensive weight can be obtained directly by seeking the arithmetic mean of the data obtained in Step 3. And the results are as follows:w(A) = 0.258, w(B) = 0.237, w(C) = 0.249, w(D) = 0.256.
- Step 5:
- Repeating the steps above to gain the weight of each secondary indicator. And the weights calculated by G1 Method are listed in Table 13.
4.3. Risk Evaluation of Two Investment Schemes
- (1)
- Classifying the risk level of the system. In this paper, the risk level of the transnational HVDC transmission project is divided into five different grades, including “Extremely Low”, “Low”, “General”, “High” and “Extremely High”.
- (2)
- Determining the classical domain of each index. The classical domain of each indicator is listed as follows (except indicator “B1”): the classical domain of “Extremely Low” is (0,0.2); “Low” is (0.2,0.4); “General” is (0.4,0.6); “High” is (0.6,0.8); “Extremely High” is (0.8,1). The classical domain of different risk levels and the matter-element to be evaluated are shown as follows:
- (3)
- Determining the weight of each indicator. Utilizing the weights obtained above, the final comprehensive weight of each secondary indicator can be calculated by synthesizing the results obtained in Section 4.2., which can effectively improve the rationality of the final result. The final comprehensive weight is listed in Table 14.
- (4)
- (5)
- Computing the correlation degree. According to the results obtained above, the correlation degree of two schemes with different risk levels can be calculated, respectively.The correlation degree of R′ (Table 15, Scheme 1):The correlation degree of R″ (Table 15, Scheme 2):
- (6)
- Determining the risk level. According to the correlation degree calculated above, the comprehensive risk level of two investment schemes can be determined: the risk level of Scheme 1 is “Low” since K2 is the maximum value among the five correlation degrees of R′; the risk level of Scheme 2 is “General” because K3 is the maximum value among the five correlation degrees of R″.
4.4. Analysis of the Evaluation Result
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Year | Project | Status |
---|---|---|
2015 | The 500 kV HVDC project between Thailand and China | In Planning |
2015 | The 500 kV HVDC project between Vietnam and China | In Planning |
2015 | China State Grid’s Extra High Voltage (EHV) projects plan based on the Belt and Road Initiative (Transmission corridor along the Silk Road economic belt) | In Planning |
2016 | The 500 kV Direct Current (DC) networking project between Ethiopia and Kenya | Under Construction |
2016 | The ±250 kV DC networking project between Malaysia and Indonesia | Under Construction |
2016 | The 400 kV Alternating Current (AC) networking project between Nepal and India | Under Construction |
2016 | Northeast Asia power interconnection project | Under Preparation |
Method | Advantages | Disadvantages |
---|---|---|
Expert evaluation method |
|
|
AHP |
|
|
Fuzzy synthetic evaluation method |
|
|
ANP |
|
|
The Monte Carlo method |
|
|
First-Level Indicator | Secondary Indicator |
---|---|
Political Risk (A) | State relation (A1) War or terrorist attack (A2) Change of government or statute (A3) |
Social and Natural Risk (B) | Public acceptance (B1) Extreme weather or natural disaster (B2) |
Economic Risk (C) | Net present value rate (C1) Internal rate of return (C2) Exchange fluctuations (C3) |
Technical Risk (D) | Parameters of power network (D1) Reliability of power supply (D2) Construction risk (D3) |
Index | Criteria and the Score | |||||
---|---|---|---|---|---|---|
A1 | Type Score | Union (0,2] | Strategic Partnership (2,4] | Partnership (4,6] | Friendship (6,8] | Diplomatic Relations (8,10] |
Rank | Extremely Low | Low | General | High | Extremely High | |
A2 | Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] |
Rank | Extremely Low | Low | General | High | Extremely High | |
A3 | Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] |
Rank | Extremely Low | Low | General | High | Extremely High |
Index | Criteria and the Score | |||||
---|---|---|---|---|---|---|
B1 | Acceptance | 100% ≤ PA < 90% | 90% ≤ PA < 80% | 80% ≤ PA <6 0% | 60% ≤ PA < 40% | 40% ≤ PA ≤ 0% |
Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] | |
B2 | Rank | Extremely Low | Low | General | High | Extremely High |
Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] | |
Rank | Extremely Low | Low | General | High | Extremely High |
Index | Criteria and the Score | ||||||
---|---|---|---|---|---|---|---|
C1 | NPVR | NPVR ≥ 0 | NPVR < 0 | ||||
Score | (0,6] | (6,10] | |||||
Rank | Acceptable | Unacceptable | |||||
C2 | IRR | IRR ≥ 7% | IRR < 7% | ||||
Score | (0,6] | (6,10] | |||||
Rank | Acceptable | Unacceptable | |||||
C3 | Range | 0 ≤ F < 1% | 1% ≤ F < 2% | 2% ≤ F < 3% | 3% ≤ F < 5% | 5% ≤ F < 10% | |
Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] | ||
Rank | Extremely Low | Low | General | High | Extremely High |
Index | Criteria and the Score | |||||
---|---|---|---|---|---|---|
D | Score | (0,2] | (2,4] | (4,6] | (6,8] | (8,10] |
Rank | Extremely Low | Low | General | High | Extremely High |
Item | Scheme 1 | Scheme 2 |
---|---|---|
Total investment (Million USD) | 4471.80 | 3095.81 |
Project life cycle | 28 years | 28 years |
Transmission line capacity | 8000 MW | 4400 MW |
Voltage level | ±800 kV | ±660 kV |
Cable type | 6 × 1250 mm2 | 6 × 720 mm2 |
Annual Transmitted Power | 35,449 GWh | 35,449 GWh |
Interruption time/Observation time | 0.0144 h/1000 h | 0.0352 h/1000 h |
On-grid price (Supply-side) | 47.23 USD/MWh | 47.23 USD/MWh |
Free on board | 51.89 USD/MWh | 51.02 USD/MWh |
On-grid price (Receive-side) | 68.0 USD/MWh | 68.0 USD/MWh |
Net present value of the project (Million USD) | 3092.76 | 3715.77 |
Net present value rate (NPVR) | 73.6% | 97.4% |
Internal rate of return (IRR) | 12% | 13.55% |
Public acceptance | 78% | 65% |
Benchmark yield | 7% | |
Composite depreciation rate | 4.46% | |
Import value-added tax | 8.5% |
Indicator | Scheme 1 | Scheme 2 | ||
---|---|---|---|---|
Ob | St | Ob | St | |
A1 (State relation) | 3 | 0.3 | 3 | 0.3 |
A2 (War or terrorist attack) | 3.9 | 0.39 | 3.9 | 0.39 |
A3 (Change of government or statute) | 5.3 | 0.53 | 5.3 | 0.53 |
B1 (Public acceptance) | 78% | 0.22 | 65% | 0.35 |
B2 (Extreme weather or natural disaster) | 4.5 | 0.45 | 4.5 | 0.45 |
C1 (NPVR) | 73.6% | 0.4 | 97.4% | 0.3 |
C2 (IRR) | 12% | 0.4 | 13.55% | 0.3 |
C3 (Exchange fluctuations) | 1.6% | 0.3 | 1.6% | 0.3 |
D1 (Parameters of power network) | 3.3 | 0.33 | 6.2 | 0.62 |
D2 (Reliability of power supply) | 2.3 | 0.23 | 4.9 | 0.49 |
D3 (Construction risk) | 7.2 | 0.72 | 5.8 | 0.58 |
Space Family | Clustering Result | Clustering Number |
---|---|---|
1 | [{x1},{x2},{x3},{x4},{x5},{x6},{x7},{x8},{x9},{x10}] | 10 |
2 | [{x1},{x2,x6},{x3},{x4},{x5},{x7},{x8},{x9},{x10}] | 9 |
3 | [{x1},{x2,x6,x10},{x3},{x4},{x5},{x7},{x8},{x9} | 8 |
4 | [{x1},{x2,x6,x10},{x3},{x4},{x5,x7},{x8},{x9}] | 7 |
5 | [{x1},{x2,x6,x10},{x3,x4},{x5,x7},{x8},{x9}] | 6 |
6 | [{x1},{x2,x6,x10},{x3,x4,x8},{x5,x7},{x9}] | 5 |
7 | [{x1},{x2,x6,x9,x10},{x3,x4,x8},{x5,x7}] | 4 |
8 | [{x1},{x2,x5,x6,x7,x9,x10},{x3,x4,x8}] | 3 |
9 | [{x1},{x2,x3,x4,x5,x6,x7,x8,x9,x10}] | 2 |
10 | [{x1,x2,x3,x4,x5,x6,x7,x8,x9,x10}] | 1 |
Family/Index | A | B | C | D |
---|---|---|---|---|
1 | 3/10 | 5/10 | 4/10 | 4/10 |
2 | 3/10 | 5/10 | 4/10 | 4/10 |
3 | 3/10 | 2/10 | 4/10 | 2/10 |
4 | 3/10 | 2/10 | 4/10 | 2/10 |
5 | 3/10 | 2/10 | 4/10 | 2/10 |
6 | 3/10 | 2/10 | 4/10 | 2/10 |
7 | 0 | 2/10 | 4/10 | 2/10 |
8 | 0 | 2/10 | 0 | 2/10 |
9 | 0 | 2/10 | 0 | 0 |
10 | 0 | 0 | 0 | 0 |
Importance | 0.18 | 0.24 | 0.28 | 0.2 |
Weight | 0.2 | 0.267 | 0.311 | 0.222 |
First-Level Indicators | Secondary Indicators | Comprehensive Weight |
---|---|---|
A (0.200) | A1 (0.298) | 0.0596 |
A2 (0.319) | 0.0638 | |
A3 (0.383) | 0.0766 | |
B (0.267) | B1 (0.571) | 0.1525 |
B2 (0.429) | 0.1145 | |
C (0.311) | C1 (0.260) | 0.0809 |
C2 (0.466) | 0.1449 | |
C3 (0.274) | 0.0852 | |
D (0.222) | D1 (0.339) | 0.0753 |
D2 (0.268) | 0.0595 | |
D3 (0.393) | 0.0872 |
First-Level Indicators | Secondary Indicators | Comprehensive Weight |
---|---|---|
A (0.258) | A1 (0.346) | 0.0893 |
A2 (0.321) | 0.0828 | |
A3 (0.333) | 0.0859 | |
B (0.237) | B1 (0.523) | 0.1239 |
B2 (0.477) | 0.1131 | |
C (0.249) | C1 (0.322) | 0.0801 |
C2 (0.342) | 0.0853 | |
C3 (0.336) | 0.0836 | |
D (0.256) | D1 (0.339) | 0.0867 |
D2 (0.351) | 0.0900 | |
D3 (0.310) | 0.0794 |
First-Level Indicator | Secondary Indicator | Granular Computing | G1 Method | Comprehensive Weight |
---|---|---|---|---|
A | A1 | 0.0596 | 0.0893 | 0.0745 |
A2 | 0.0638 | 0.0828 | 0.0733 | |
A3 | 0.0766 | 0.0859 | 0.0813 | |
B | B1 | 0.1525 | 0.1239 | 0.1381 |
B2 | 0.1145 | 0.1131 | 0.1138 | |
C | C1 | 0.0809 | 0.0801 | 0.0805 |
C2 | 0.1449 | 0.0853 | 0.1150 | |
C3 | 0.0852 | 0.0836 | 0.0844 | |
D | D1 | 0.0753 | 0.0867 | 0.0810 |
D2 | 0.0595 | 0.0900 | 0.0748 | |
D3 | 0.0872 | 0.0794 | 0.0833 |
Indicator | R′ (Scheme 1) | R″ (Scheme 2) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
d1i | d2i | d3i | d4i | d5i | d1i | d2i | d3i | d4i | d5i | |
A1 | 0.10 | −0.10 | 0.10 | 0.30 | 0.50 | 0.10 | −0.1 | 0.10 | 0.30 | 0.50 |
A2 | 0.19 | −0.01 | 0.01 | 0.21 | 0.41 | 0.19 | −0.01 | 0.01 | 0.21 | 0.41 |
A3 | 0.33 | 0.13 | −0.07 | 0.07 | 0.27 | 0.33 | 0.13 | −0.07 | 0.07 | 0.27 |
B1 | 0.12 | 0.02 | −0.02 | 0.18 | 0.38 | 0.25 | 0.15 | −0.05 | 0.05 | 0.25 |
B2 | 0.25 | 0.05 | −0.05 | 0.15 | 0.35 | 0.25 | 0.05 | −0.05 | 0.15 | 0.35 |
C1 | 0.20 | 0 | 0 | 0.20 | 0.40 | 0.10 | −0.10 | 0.10 | 0.30 | 0.50 |
C2 | 0.20 | 0 | 0 | 0.20 | 0.40 | 0.10 | −0.10 | 0.10 | 0.30 | 0.50 |
C3 | 0.10 | −0.1 | 0.10 | 0.30 | 0.50 | 0.10 | −0.10 | 0.10 | 0.30 | 0.50 |
D1 | 0.13 | −0.07 | 0.07 | 0.27 | 0.47 | 0.42 | 0.22 | 0.02 | −0.02 | 0.18 |
D2 | 0.03 | −0.03 | 0.17 | 0.37 | 0.57 | 0.29 | 0.09 | −0.09 | 0.11 | 0.31 |
D3 | 0.52 | 0.32 | 0.12 | −0.08 | 0.08 | 0.38 | 0.18 | −0.02 | 0.02 | 0.22 |
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Li, J.; Wu, F.; Li, J.; Zhao, Y. Research on Risk Evaluation of Transnational Power Networking Projects Based on the Matter-Element Extension Theory and Granular Computing. Energies 2017, 10, 1523. https://doi.org/10.3390/en10101523
Li J, Wu F, Li J, Zhao Y. Research on Risk Evaluation of Transnational Power Networking Projects Based on the Matter-Element Extension Theory and Granular Computing. Energies. 2017; 10(10):1523. https://doi.org/10.3390/en10101523
Chicago/Turabian StyleLi, Jinying, Fan Wu, Jinchao Li, and Yunqi Zhao. 2017. "Research on Risk Evaluation of Transnational Power Networking Projects Based on the Matter-Element Extension Theory and Granular Computing" Energies 10, no. 10: 1523. https://doi.org/10.3390/en10101523