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Power distribution system restoration based on soft open points and islanding by distributed generations

  • Gholam-Reza Kamyab ORCID logo EMAIL logo
Published/Copyright: October 2, 2024
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International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 26 Issue 4

Abstract

A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

Keywords: power distribution system restoration; soft open point; distributed generation
Corresponding author: Gholam-Reza Kamyab, Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran, E-mail:

Appendix: Network test data and modeling parameter values

Table A.1 presents the details of the lines and loads in the IEEE 69-bus test network. In this table, lines 74, 75, and 76 are virtual lines with zero impedance, effectively representing three tie switches that simulate the linkage of three DGs to buses 12, 65, and 27. Furthermore, lines 77 and 78 are virtual lines with zero impedance that model the SOP positioned between buses 50 and 59, in accordance with the specified SOP modeling.

Table A.1:

Load and line data overview for the IEEE 69-bus network.

Line numb. Type From bus To bus Resistance (Ω) Reactance (Ω) Limit (KVA) Active power at the to bus (KW) Reactive power at the to bus (KVAR)
1 Line 1 2 0.0005 0.0012 10,761 0 0
2 Line 2 3 0.0005 0.0012 10,761 0 0
3 Line 3 4 0.0015 0.0036 10,761 0 0
4 Line 4 5 0.0251 0.0294 5,823 0 0
5 Line 5 6 0.3660 0.1864 3,600 2.6 2.2
6 Line 6 7 0.3811 0.1941 3,600 40.4 30
7 Line 7 8 0.0922 0.0470 3,600 75 54
8 Line 8 9 0.0493 0.0251 3,600 30 22
9 Line 9 10 0.8190 0.2707 1,455 28 19
10 Line 10 11 0.1872 0.0619 1,455 145 104
11 Line 11 12 0.7114 0.2351 1,455 145 104
12 Line 12 13 1.0300 0.3400 1,455 8 5
13 Line 13 14 1.0440 0.3450 1,455 8 5.5
14 Line 14 15 1.0580 0.3496 1,455 0 0
15 Line 15 16 0.1966 0.0650 1,455 45.5 30
16 Line 16 17 0.3744 0.1238 1,455 60 35
17 Line 17 18 0.0047 0.0016 2,200 60 35
18 Line 18 19 0.3276 0.1083 1,455 0 0
19 Line 19 20 0.2106 0.0696 1,455 1 0.6
20 Line 20 21 0.3416 0.1129 1,455 114 81
21 Line 21 22 0.0140 0.0046 1,455 5 3.5
22 Line 22 23 0.1591 0.0526 1,455 0 0
23 Line 23 24 0.3463 0.1145 1,455 28 20
24 Line 24 25 0.7488 0.2475 1,455 0 0
25 Line 25 26 0.3089 0.1021 1,455 14 10
26 Line 26 27 0.1732 0.0572 1,455 14 10
27 Line 3 28 0.0044 0.0108 10,761 26 18.6
28 Line 28 29 0.0640 0.1565 10,761 26 18.6
29 Line 29 30 0.3978 0.1315 1,455 0 0
30 Line 30 31 0.0702 0.0232 1,455 0 0
31 Line 31 32 0.3510 0.1160 1,455 0 0
32 Line 32 33 0.8390 0.2816 2,200 14 10
33 Line 33 34 1.7080 0.5646 1,455 19.5 14
34 Line 34 35 1.4740 0.4873 1,455 6 4
35 Line 3 36 0.0044 0.0108 10,761 26 18.55
36 Line 36 37 0.0640 0.1565 10,761 26 18.55
37 Line 37 38 0.1053 0.1230 5,823 0 0
38 Line 38 39 0.0304 0.0355 5,823 24 17
39 Line 39 40 0.0018 0.0021 5,823 24 17
40 Line 40 41 0.7283 0.8509 5,823 1.2 1
41 Line 41 42 0.3100 0.3623 5,823 0 0
42 Line 42 43 0.0410 0.0478 5,823 6 4.3
43 Line 43 44 0.0092 0.0116 5,823 0 0
44 Line 44 45 0.1089 0.1373 5,823 39.22 26.3
45 Line 45 46 0.0009 0.0012 6,709 39.22 26.3
46 Line 4 47 0.0034 0.0084 10,761 0 0
47 Line 47 48 0.0851 0.2083 10,761 79 56.4
48 Line 48 49 0.2898 0.7091 10,761 384.7 274.5
49 Line 49 50 0.0822 0.2011 10,761 384.7 274.5
50 Line 8 51 0.0928 0.0473 1,899 40.5 28.3
51 Line 51 52 0.3319 0.1114 2,200 3.6 2.7
52 Line 9 53 0.1740 0.0886 2,400 4.35 3.5
53 Line 53 54 0.2030 0.1034 2,400 26.4 19
54 Line 54 55 0.2842 0.1447 2,400 24 17.2
55 Line 55 56 0.2813 0.1433 2,400 0 0
56 Line 56 57 1.5900 0.5337 2,400 0 0
57 Line 57 58 0.7837 0.2630 2,400 0 0
58 Line 58 59 0.3042 0.1006 2,400 100 72
59 Line 59 60 0.3861 0.1172 2,400 0 0
60 Line 60 61 0.5075 0.2585 2,400 1,244 888
61 Line 61 62 0.0974 0.0496 1,899 32 23
62 Line 62 63 0.1450 0.0738 1,899 0 0
63 Line 63 64 0.7105 0.3619 1,899 227 162
64 Line 64 65 1.0410 0.5302 1,899 59 42
65 Line 11 66 0.2012 0.0611 1,455 18 13
66 Line 66 67 0.0047 0.0014 1,455 18 13
67 Line 12 68 0.7394 0.2444 1,455 28 20
68 Line 68 69 0.0047 0.0016 1,455 28 20
69 Tie line 11 43 0.5 0.5 566 – –
70 Tie line 13 21 0.5 0.5 566 – –
71 Tie line 15 46 1 1 400 – –
72 SOP 50 59 2 2 283 – –
73 Tie line 27 65 1 1 400 – –
74 DG to ref. 1 12 0 0 555.6 – –
75 DG to ref. 1 65 0 0 555.6 – –
76 DG to ref. 1 27 0 0 555.6 – –
77 SOP to ref. 1 50 0 0 5,000 – –
78 SOP to ref. 1 59 0 0 5,000 – –

The modeling parameters are detaild in Table A.2.

Table A.2:

Values of the modeling parameters.

Parameter Value
The weighting coefficient of customers’ priority at bus i ( w d i ) for all buses 1
The weighting coefficient of cost-effectiveness of DG i ( w g i ) for all DGs 10
The operation cost of switch on line i–j (C ij ) for all lines 1
Lost load cost compensation factor (K L ) 100
Microgrid cost compensation factor (K m ) 10
Switching cost compensation factor (K s ) 1
Constraints compensation factor (K c ) 1016

Acknowledgments

The author has no specific acknowledgments to make, as no assistance was received from others.

  1. Research ethics: This declaration is not applicable. The research was conducted in accordance with ethical standards and guidelines.

  2. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Use of Large Language Models, AI and Machine Learning Tools: In the preparation of this manuscript, the author utilized AI tools specifically to enhance the clarity and coherence of the text. The primary purpose was to improve the language, ensuring that ideas were communicated effectively. No other AI or machine learning tools were employed in the design process.

  4. Conflict of interest: The author declares that there are no conflicts of interest regarding the publication of this paper.

  5. Research funding: This research did not receive any specific financial support from funding organizations.

  6. Data availability: This declaration is not applicable. All data related to this research are publicly available.

References

1. JieJian, PL, Yu, H, Jia, H, Ji, J, Song, G, Yan, J, et al.. Multi-stage supply restoration of active distribution networks with SOP integration. Sustainable Energy Grids Network 2022;29:100562. https://doi.org/10.1016/j.segan.2021.100562.Search in Google Scholar

2. Peng, L, Song, G, Ji, H, Zhao, J, Wang, C, Wu, J. A supply restoration method of distribution system based on soft open point. In: 2016 IEEE Innovative Smart Grid Technologies – Asia (ISGT-Asia), Melbourne, Australia; 2016.Search in Google Scholar

3. Abu Saaklayen, M, Liang, X, Faried, SO, Martirano, L, Sutherland, PE. Soft open point-based service restoration coordinated with distributed generation in distribution networks. IEEE Trans Ind Appl 2024;60:2554–66. https://doi.org/10.1109/tia.2023.3332584.Search in Google Scholar

4. Zhang, T, Yu, X, Mu, Y, Jia, H, Hou, K, Jin, X. Multiple sources restoration for soft open points in distribution networks with a two-stage accelerated algorithm. IEEE Trans Sustain Energy 2023;14:892–905. https://doi.org/10.1109/tste.2022.3226750.Search in Google Scholar

5. Ghasemi, S, Mohammadi, M, Moshtagh, J. A new look-ahead restoration of critical loads in the distribution networks during blackout with considering load curve of critical loads. Elec Power Syst Res 2021;191:106873. https://doi.org/10.1016/j.epsr.2020.106873.Search in Google Scholar

6. Faria, WR, Carvalho, EOP, Dantas, LB, Maciel, CD, Alberto, LFC, London, JBAJr., et al.. Service restoration in modern distribution systems addressing grid-connected and islanded operations. Elec Power Syst Res 2021;196:107238. https://doi.org/10.1016/j.epsr.2021.107238.Search in Google Scholar

7. Yang, L, Xu, Y, Sun, H, Chow, M, Zhou, J. A multiagent system based optimal load restoration strategy in distribution systems. Electr Power Energy Syst 2021;124:106314. https://doi.org/10.1016/j.ijepes.2020.106314.Search in Google Scholar

8. Rodríguez-Montañés, M, Rosendo-Macías, JA, Gómez-Expósito, A. A systematic approach to service restoration in distribution networks. Elec Power Syst Res 2020;189:106539. https://doi.org/10.1016/j.epsr.2020.106539.Search in Google Scholar

9. Shen, F, Wu, Q, Xu, Y, Li, F, Teng, F, Strbac, G. Hierarchical service restoration scheme for active distribution networks based on ADMM. Electr Power Energy Syst 2020;118:105809. https://doi.org/10.1016/j.ijepes.2019.105809.Search in Google Scholar

10. Wang, F, Xiao, X, Sun, Q, Chen, C, Bin, F, Chen, S, et al.. Service restoration for distribution network with DGs based on stochastic response surface method. Electr Power Energy Syst 2019;107:557–68. https://doi.org/10.1016/j.ijepes.2018.12.015.Search in Google Scholar

11. Sekhavatmanesh, H, Cherkaoui, R. Distribution network restoration in a multiagent framework using a convex OPF model. IEEE Trans Smart Grid 2019;10:2618–28. https://doi.org/10.1109/TSG.2018.2805922.Search in Google Scholar

12. Sun, W, Kadel, N, Alvarez-Fernandez, I, Roofegari Nejad, R, Golshani, A. Optimal distribution system restoration using PHEVs. IET Smart Grid 2019;2:42–9. https://doi.org/10.1049/iet-stg.2018.0054.Search in Google Scholar

13. Koutsoukis, NC, Georgilakis, PS, Hatziargyriou, ND. Service restoration of active distribution systems with increasing penetration of renewable distributed generation. IET Gener, Transm Distrib 2019;13:3177–87. https://doi.org/10.1049/iet-gtd.2018.6153.Search in Google Scholar

14. Peralta, RAV, Leite, JB, Mantovani, JRS. Automatic restoration of large-scale distribution networks with distributed generators, voltage control devices and heating loads. Elec Power Syst Res 2019;176:105925. https://doi.org/10.1016/j.epsr.2019.105925.Search in Google Scholar

15. Khederzadeh, M, Zandi, S. Enhancement of distribution system restoration capability in single/multiple faults by using microgrids as a resiliency resource. IEEE Syst J 2019;13:1796–803. https://doi.org/10.1109/JSYST.2019.2890898.Search in Google Scholar

16. Chen, B, Ye, Z, Chen, C, Wang, J, Ding, T, Bie, Z. Toward a synthetic model for distribution system restoration and crew dispatch. IEEE Trans Power Syst 2019;34:2228–39. https://doi.org/10.1109/TPWRS.2018.2885763.Search in Google Scholar

17. Poudel, S, Dubey, A. Critical load restoration using distributed energy resources for resilient power distribution system. IEEE Trans Power Syst 2019;34:52–63. https://doi.org/10.1109/TPWRS.2018.2860256.Search in Google Scholar

18. Sekhavatmanesh, H, Cherkaoui, R. Analytical approach for active distribution network restoration including optimal voltage regulation. IEEE Trans Power Syst 2019;34:1716–28. https://doi.org/10.1109/TPWRS.2018.2889241.Search in Google Scholar

19. Abu-Elanien, AEB, Salama, MMA, Shaban, KB. Modern network reconfiguration techniques for service restoration in distribution systems: a step to a smarter grid. Alex Eng J 2018;57:3959–67. https://doi.org/10.1016/j.aej.2018.03.011.Search in Google Scholar

20. Riahinia, S, Abbaspour, A, Moeini-Aghtaie, M, Khalili, S. Load service restoration in active distribution network based on stochastic approach. IET Gener Transm Distrib 2018;12:3028–36. https://doi.org/10.1049/iet-gtd.2017.0684.Search in Google Scholar

21. Hafez, AA, Omran, WA, Hegazy, YG. A decentralized technique for autonomous service restoration in active radial distribution networks. IEEE Trans Smart Grid 2018;9:1911–19. https://doi.org/10.1109/TSG.2016.2602541.Search in Google Scholar

22. Arif, A, Wang, Z. Networked microgrids for service restoration in resilient distribution systems. IET Gener Transm Distrib 2017;11:3612–19. https://doi.org/10.1049/iet-gtd.2017.0380.Search in Google Scholar

23. Wang, F, Chen, C, Li, C, Cao, Y, Yong Li, BZ, Dong, AX, et al.. A multi-stage restoration method for medium-voltage distribution system with DGs. IEEE Trans Smart Grid 2017;8:2627–36. https://doi.org/10.1109/TSG.2016.2532348.Search in Google Scholar

24. Borges, TT, Carneiro, SJr., Garcia, PAN, Pereira, JLR. A new OPF based distribution system restoration method. Electr Power Energy Syst 2016;80:297–305. https://doi.org/10.1016/j.ijepes.2016.01.024.Search in Google Scholar

25. Li, J, Ma, X-Y, Liu, C-C, Schneider, KP. Distribution system restoration with microgrids using spanning tree search. IEEE Trans Power Syst 2014;29:3021–9. https://doi.org/10.1109/TPWRS.2014.2312424.Search in Google Scholar

26. Pham, TTH, Bésanger, Y, Hadjsaid, N. New challenges in power system restoration with large scale of dispersed generation insertion. IEEE Trans Power Syst 2009;24:398–406. https://doi.org/10.1109/TPWRS.2008.2009477.Search in Google Scholar

27. Bastami, H, Shakarami, MR, Doostizadeh, M. A decentralized cooperative framework for multi-area active distribution network in presence of inter-area soft open points. Appl Energy 2021;300:117416. https://doi.org/10.1016/j.apenergy.2021.117416.Search in Google Scholar

28. Zhang, J, Foley, A, Wang, S. Optimal planning of a soft open point in a distribution network subject to typhoons. Int J Electr Power Energy Syst 2021;129:106839. https://doi.org/10.1016/j.ijepes.2021.106839.Search in Google Scholar

29. Wang, J, Zhou, N, Wang, Q. Data-driven stochastic service restoration in unbalanced active distribution networks with multi-terminal soft open points. Electr Power Energy Syst 2020;121:106069. https://doi.org/10.1016/j.ijepes.2020.106069.Search in Google Scholar

30. Li, Z, Ye, Y, Ma, X, Lin, X, Xu, F, Wang, C, et al.. Single-phase-to-ground fault section location in flexible resonant grounding distribution networks using soft open points. Electr Power Energy Syst 2020;122:106198. https://doi.org/10.1016/j.ijepes.2020.106198.Search in Google Scholar

31. Ji, H, Wang, C, Li, P, Ding, F, Wu, J. Robust operation of soft open points in active distribution networks with high penetration of photovoltaic integration. IEEE Trans Sustain Energy 2019;10:280–9. https://doi.org/10.1109/TSTE.2018.2833545.Search in Google Scholar

32. Nguyen, TT, Nguyen, TT, Nguyen, HP. Optimal soft open point placement and open switch position selection simultaneously for power loss reduction on the electric distribution network. Expert Syst Appl 2024;238:121743. https://doi.org/10.1016/j.eswa.2023.121743.Search in Google Scholar

33. Kirkpatrik, S, Gellat, C, Vecchi, J. Optimization by simulated annealing. Science 1983;220:671–80. https://doi.org/10.1126/science.220.4598.671.Search in Google Scholar PubMed

34. Kamyab, G-R. Optimal feeder routing and DG placement using Kruskal’s algorithm. Eur J Electr Eng 2020;22. https://doi.org/10.18280/ejee.220109.Search in Google Scholar
Received: 2023-08-26
Accepted: 2024-09-12
Published Online: 2024-10-02

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijeeps-2023-0306
Keywords for this article
power distribution system restoration; soft open point; distributed generation

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Improving islanded distribution system stability with adaptive decision-making framework
  4. Sensorless control method of induction motors with new feedback gain matrix and speed adaptive law for low speed range
  5. An improved CB-DPWM strategy with NP voltage balance and switching loss reduction for 3-L NPC converter
  6. Single-ended protection scheme for three-terminal hybrid DC transmission system based on refractive coefficients
  7. Long-distance transmission conductor condition sensing based on distributed fiber optic sensing technology
  8. Data integrity cyber-attack mitigation using linear quadratic regulator based load frequency control in hybrid power system
  9. Investigation of DG units influence on 66 kV sub-transmission system network considering region load growth: a case study
  10. Influence of increasing Integration of Solar photovoltaic on Small Signal and Transient stability of Nigeria Power System
  11. Implementation of SOC-based power management algorithm in a grid-connected microgrid with hybrid energy storage devices
  12. Experimental studies on insulating oils for power transformer applications
  13. Power distribution system restoration based on soft open points and islanding by distributed generations
  14. Power coordination and control of DC Microgrid with PV and hybrid energy storage system
  15. An investigation on NGR failure in Indian smart cities while replacing the existing overhead lines by underground cables
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