A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems
Abstract
:1. Introduction
- Probabilistic methods: used when the historical data is available. It can be divided into numerical and analytical approaches [9].
- Possibilistic methods [10]: used when the historical data is unavailable. it can be divided into defuzzification method and alpha-cut method.
- Combined possibilistic and probabilistic methods [11]: these methods mix between the features of the possibilistic and probabilistic methods such as possibilistic-Monte Carlo approach.
2. RESs Uncertainty
2.1. WT Modeling
2.2. PV Modeling
2.2.1. Accurate PV Modeling
2.2.2. Approximate PV Modeling
3. Proposed Reduction Criteria for RESs Uncertainty
- Establishing the base matrix that consists of 8784 rows and 4 columns; the rows represent the year hours, and the columns represent the uncertainty parameters ().
- All the matrix row values are rearranged according to the G values when it is arranged in ascending order.
- Forming the G sub-groups as follows: G sub-groups are the groups which are formed by dividing the practical irradiance range into three equal sub-groups limited by four boundaries () as shown in Figure 3, where G0 is the lowest practical solar irradiance, is the highest practical solar irradiance, is the boundary between the first and second sub-groups, and is the boundary between the second and third sub-groups. The values of and are calculated as shown in Figure 3.
- Using the formed sub-groups to divide the base matrix into three sub-matrices (M1, M2, M3), where each sub-group forms a sub-matrix.
- Rearrange the rows of the sub-matrices () according to the values of T in ascending order.
- Follow the sub-grouping technique used in Stage 1 to form sub-matrices (, ) from () according to T values as shown in Figure 4.
- Rearrange the rows of the sub-matrices () according to the values of in ascending order.
- Follow the sub-grouping technique used in Stage 1 to form sub-matrices (, , ) from () according to values as shown in Figure 5.
- Complete the same steps mentioned in the third stage on (, , ) but according to the values of LR as shown in Figure 6.
4. Implementation of the Proposed Reduction Strategy for Distribution Systems Operation
4.1. Energy Loss Error
4.2. The Lowest and Highest Voltage Value Errors
4.3. Two-Voltages out Limits Error
5. Applications
5.1. Description of Test Systems
5.2. Reduction Strategy for Uncertainty
5.2.1. Twenty-Seven Uncertainty Cases Reduction Strategy
5.2.2. Sixty-Four Uncertainty Cases Reduction Strategy
5.2.3. Eighty-One Uncertainty Cases Reduction Strategy
5.3. Applications to Distribution Systems
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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System | 69-Bus | 118-Bus | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Plant type | PV | WT | PV | WT | ||||||
Place | 61 | 46 | 18 | 47 | 4 | 74 | 93 | 24 | 40 | 69 |
Nunits (PV modules or wind turbines ) | 2000 | 1000 | 30 | 100 | 2000 | 1000 | 100 | 40 | 80 | 70 |
Parameter | A) | A) | a1 | a2 | |||
value | 0.5593 | 23.6471 | 9.9664 | 4.3606 | 8 | 1.5 | 2 |
Parameter | (m/s) | (m/s) | (m/s) | (KW) |
value | 3 | 25 | 10 | 11 |
System | Modeling Method | ||||
---|---|---|---|---|---|
69-bus | All year data | 0.8784 | 1.0446 | 2.5543 | 108.1319 |
81 cases | 0.9063 | 1.0445 | 2.4597 | 99.7397 | |
27 cases | 0.9121 | 1.0368 | 2.6527 | 88.7837 | |
64 cases | 0.8995 | 1.0468 | 2.6776 | 97.3506 | |
118-bus | All year data | 0.8760 | 1.0286 | 1.2142 | 217.2986 |
81 cases | 0.9058 | 1.0277 | 1.1743 | 193.3500 | |
27 cases | 0.9091 | 1.0228 | 1.1663 | 180.8850 | |
2-6 | 64 cases | 0.8981 | 1.0283 | 1.1776 | 192.2738 |
System | Modeling Method | ||||
---|---|---|---|---|---|
69-bus | All year data | - | - | - | - |
81 cases | 3.1742 | 0.0161 | 3.7059 | 7.7610 | |
27 cases | 3.8372 | 0.7440 | 3.8518 | 17.8932 | |
64 cases | 2.3998 | 0.2108 | 4.8266 | 9.9705 | |
118-bus | All year data | - | - | - | - |
81 cases | 3.4042 | 0.0861 | 3.2889 | 11.0210 | |
27 cases | 3.7788 | 0.5623 | 3.9425 | 16.7574 | |
64 cases | 2.5230 | 0.0262 | 3.0167 | 11.5163 |
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Ali, E.S.; El-Sehiemy, R.A.; Abou El-Ela, A.A.; Tostado-Véliz, M.; Kamel, S. A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems. Appl. Sci. 2022, 12, 623. https://doi.org/10.3390/app12020623
Ali ES, El-Sehiemy RA, Abou El-Ela AA, Tostado-Véliz M, Kamel S. A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems. Applied Sciences. 2022; 12(2):623. https://doi.org/10.3390/app12020623
Chicago/Turabian StyleAli, Eman S., Ragab A. El-Sehiemy, Adel A. Abou El-Ela, Marcos Tostado-Véliz, and Salah Kamel. 2022. "A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems" Applied Sciences 12, no. 2: 623. https://doi.org/10.3390/app12020623
APA StyleAli, E. S., El-Sehiemy, R. A., Abou El-Ela, A. A., Tostado-Véliz, M., & Kamel, S. (2022). A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems. Applied Sciences, 12(2), 623. https://doi.org/10.3390/app12020623