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ieijqp 2021, 10(3): 97-108 Back to browse issues page
Optimal Restoration of Active Distribution Systems for Enhancing Resilience Considering the Uncertainty of Renewable Sources
Abouzar Samimi * , Mehdi Nikzad
Department of Electrical Engineering, Arak University of Technology, Arak, Iran
Abstract:   (198 Views)
Smart grids all over the world aim at providing reliable and resilient power to customers. During major contingencies of large-scale natural disasters, Distributed Generations (DGs) play a key role in delivering a resilient and reliable supply of loads. Major natural disasters such as floods and hurricanes often cause lengthy interruptions in electricity distribution systems and degrade the level of service to end-users. The utilities have mainly focused on restoring the distribution system since the power grid is susceptible to natural disasters. A resilient system's primary purpose is to allow the restoration of out-of-service loads as soon as possible after an extreme event. The resilience of a power system can be defined as “the ability of the system to prepare and plan for absorbing the damage and adapting/recovering in order to prevent the impacts of similar events in the future”. Therefore, the resilience of a power system is briefly attributed to three aspects of prevention, survivability, and recovery.  Improvements in any or all of these features can enhance the overall resilience of the power system. This paper presents a self-healing restoration algorithm for power distribution systems exposed to extreme natural disasters. Indeed, an improved restoration algorithm in distribution systems in the presence of renewable and dispatchable DGs is proposed for enhancing the survivability of out-of-service loads due to extreme events, like natural disasters. The algorithm can analyze the effects of multiple faults, which arise due to a low-probability, high-impact event like a natural disaster. In the presented method, an optimal strategy is introduced to restore maximum loads with minimum switching operations and maximum load restoration under fault conditions. In order to consider uncertain parameters, a stochastic scenario-based approach is considered and the expected values as objectives are minimized to consider the effect of all scenarios. In the proposed method, the Genetic Algorithm (GA) is utilized as a powerful algorithm in optimization and how to implement and solve the proposed model by the GA is introduced. An evaluation of the proposed approach is conducted through a typical case study. A modified IEEE 33-node system is considered for this reason. The simulated results indicate that in the presence of microgrids and an automated switching-based distribution system, the system's resilience is improved significantly. However, the present study did not address microgrids' dynamic response. In the event of extreme natural disasters, utilities can use the proposed algorithm to improve the recovery of out-of-service loads.
Keywords: Resiliency, Optimal Restoration, Distributed Generations, Genetic Algorithm, Scenario Generation, Uncertainty
Full-Text [PDF 858 kb]   (19 Downloads)    
Type of Study: Research |
Received: 2021/01/5 | Accepted: 2021/08/4 | Published: 2021/09/11
References
1. [1] M. A. Mohamed, T. Chen, W. Su, and T. Jin, "Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review," IEEE Access, vol. 7, pp. 163778-163795, 2019. [DOI:10.1109/ACCESS.2019.2952362]
2. [2] B. Hu, K. Xie, and H. Tai, "Inverse Problem of Power System Reliability Evaluation: Analytical Model and Solution Method," IEEE Transactions on Power Systems, vol. 33, pp. 6569-6578, 2018. [DOI:10.1109/TPWRS.2018.2839841]
3. [3] C. Buque and S. Chowdhury, "Distributed generation and microgrids for improving electrical grid resilience: Review of the Mozambican scenario," in 2016 IEEE Power and Energy Society General Meeting (PESGM), 2016, pp. 1-5. [DOI:10.1109/PESGM.2016.7741488]
4. [4] K. Sandhya and K. Chatterjee, "A review on the state of the art of proliferating abilities of distributed generation deployment for achieving resilient distribution system," Journal of Cleaner Production, vol. 287, p. 125023, 2021/03/10/ 2021. [DOI:10.1016/j.jclepro.2020.125023]
5. [5] W. Yuan, J. Wang, F. Qiu, C. Chen, C. Kang, and B. Zeng, "Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters," IEEE Transactions on Smart Grid, vol. 7, pp. 2817-2826, 2016. [DOI:10.1109/TSG.2015.2513048]
6. [6] L. Che, M. Khodayar, and M. Shahidehpour, "Only Connect: Microgrids for Distribution System Restoration," IEEE Power and Energy Magazine, vol. 12, pp. 70-81, 2014. [DOI:10.1109/MPE.2013.2286317]
7. [7] J. M. Solanki, S. Khushalani, and N. N. Schulz, "A Multi-Agent Solution to Distribution Systems Restoration," IEEE Transactions on Power Systems, vol. 22, pp. 1026-1034, 2007. [DOI:10.1109/TPWRS.2007.901280]
8. [8] C. P. Nguyen and A. J. Flueck, "Agent Based Restoration With Distributed Energy Storage Support in Smart Grids," IEEE Transactions on Smart Grid, vol. 3, pp. 1029-1038, 2012. [DOI:10.1109/TSG.2012.2186833]
9. [9] Y. Jiang and J. Jiang, "An Object-oiented Framework with Multi-objective Cellular Evolutionary Algorithm for Service Restoration of Shipboard Power Networks," Electric Power Components and Systems, vol. 40, pp. 898-914, 2012/04/30 2012. [DOI:10.1080/15325008.2012.666619]
10. [10] J. Hou, Z. Xu, Z. Y. Dong, and K. P. Wong, "Permutation-based Power System Restoration in Smart Grid Considering Load Prioritization," Electric Power Components and Systems, vol. 42, pp. 361-371, 2014 . [DOI:10.1080/15325008.2013.862326]
11. [11] C. Yuan, M. S. Illindala, and A. S. Khalsa, "Modified Viterbi Algorithm Based Distribution System Restoration Strategy for Grid Resiliency," IEEE Transactions on Power Delivery, vol. 32, pp. 310-319, 2017. [DOI:10.1109/TPWRD.2016.2613935]
12. [12] R. K. Mathew, A. Sankar, K. Sundaramoorthy, and A. N. Jayadeebhai, "An Improved Algorithm for Power Distribution System Restoration Using Microgrids for Enhancing Grid Resiliency," Electric Power Components and Systems, vol. 46, pp. 1731-1743, 2018/10/21 2018. [DOI:10.1080/15325008.2018.1527868]
13. [13] T. T. H. Pham, Y. Besanger, and N. Hadjsaid, "New Challenges in Power System Restoration With Large Scale of Dispersed Generation Insertion," IEEE Transactions on Power Systems, vol. 24, pp. 398-406, 2009. [DOI:10.1109/TPWRS.2008.2009477]
14. [14] G. Strbac, N. Hatziargyriou, J. P. Lopes, C. Moreira, A. Dimeas, and D. Papadaskalopoulos, "Microgrids: Enhancing the Resilience of the European Megagrid," IEEE Power and Energy Magazine, vol. 13, pp. 35-43, 2015. [DOI:10.1109/MPE.2015.2397336]
15. [15] Y. Xu, C. Liu, K. P. Schneider, F. K. Tuffner, and D. T. Ton, "Microgrids for Service Restoration to Critical Load in a Resilient Distribution System," IEEE Transactions on Smart Grid, vol. 9, pp. 426-437, 2018. [DOI:10.1109/TSG.2016.2591531]
16. [16] H. Gao, Y. Chen, Y. Xu, and C. Liu, "Resilience-Oriented Critical Load Restoration Using Microgrids in Distribution Systems," IEEE Transactions on Smart Grid, vol. 7, pp. 2837-48, 2016 [DOI:10.1109/TSG.2016.2550625]
17. [17] Y. Xu, C. Liu, Z. Wang, K. Mo, K. P. Schneider, F. K. Tuffner, et al., "DGs for Service Restoration to Critical Loads in a Secondary Network," IEEE Transactions on Smart Grid, vol. 10, pp. 435-447, 2019. [DOI:10.1109/TSG.2017.2743158]
18. [18] Z. Wang, C. Shen, Y. Xu, F. Liu, X. Wu, and C. Liu, "Risk-Limiting Load Restoration for Resilience Enhancement With Intermittent Energy Resources," IEEE Transactions on Smart Grid, vol. 10, pp. 2507-2522, 2019. [DOI:10.1109/TSG.2018.2803141]
19. [19] Y. Xu, C. Liu, K. P. Schneider, and D. T. Ton, "Placement of Remote-Controlled Switches to Enhance Distribution System Restoration Capability," IEEE Transactions on Power Systems, vol. 31, pp. 1139-1150, 2016. [DOI:10.1109/TPWRS.2015.2419616]
20. [20] J. Li, X. Ma, C. Liu, and K. P. Schneider, "Distribution System Restoration With Microgrids Using Spanning Tree Search," IEEE Transactions on Power Systems, vol. 29, pp. 3021-3029, 2014. [DOI:10.1109/TPWRS.2014.2312424]
21. [21] m. i. alizadeh, r. ghaffarpour, and a. m. ranjbar, "Tri-level Robust Resiliency-driven SCUC in Power Systems with High Penetration Rate of Renewables," jiaeee, vol. 17, pp. 113-121, 2020.
22. [22] A. Kwasinski, "Technology Planning for Electric Power Supply in Critical Events Considering a Bulk Grid, Backup Power Plants, and Micro-Grids," IEEE Systems Journal, vol. 4, pp. 167-178, 2010. [DOI:10.1109/JSYST.2010.2047034]
23. [23] P. Chen and M. Kezunovic, "Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management," IEEE Transactions on Smart Grid, vol. 7, pp. 2827-2836, 2016. [DOI:10.1109/TSG.2016.2576282]
24. [24] A. Zangeneh, S. Jadid, and A. Rahimi-Kian, "A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran," Energy Policy, vol. 37, pp. 5752-5763, 2009/12/01/ 2009. [DOI:10.1016/j.enpol.2009.08.045]
25. [25] R. Saberi, H. Falaghi, and M. Esmaeeli, "Resilience-Based Framework for Distributed Generation Planning in Distribution Networks," ieijqp, vol. 9, pp. 35-49, 2020. [DOI:10.29252/ieijqp.9.4.35]
26. [26] Z. Ye, C. Chen, B. Chen, and K. Wu, "Resilient Service Restoration for Unbalanced Distribution Systems With Distributed Energy Resources by Leveraging Mobile Generators," IEEE Transactions on Industrial Informatics, vol. 17, pp. 1386-1396, 2021. [DOI:10.1109/TII.2020.2976831]
27. [27] S. Yao, P. Wang, and T. Zhao, "Transportable Energy Storage for More Resilient Distribution Systems With Multiple Microgrids," IEEE Transactions on Smart Grid, vol. 10, pp. 3331-3341, 2019. [DOI:10.1109/TSG.2018.2824820]
28. [28] W. Yang, F. Shanshan, W. Bing, H. Jinhui, and W. Xiaoyang, "Towards optimal recovery scheduling for dynamic resilience of networked infrastructure," Journal of Systems Engineering and Electronics, vol. 29, pp. 995-1008, 2018. [DOI:10.21629/JSEE.2018.05.11]
29. [29] M. Borghei and M. Ghassemi, "Optimal planning of microgrids for resilient distribution networks," International Journal of Electrical Power & Energy Systems, vol. 128, p. 106682, 2021/06/01/ 2021. [DOI:10.1016/j.ijepes.2020.106682]
30. [30] S. A. Sedgh, M. Doostizadeh, F. Aminifar, and M. Shahidehpour, "Resilient-enhancing critical load restoration using mobile power sources with incomplete information," Sustainable Energy, Grids and Networks, vol. 26, p. 100418, 2021/06/01/ 2021. [DOI:10.1016/j.segan.2020.100418]
31. [31] H. Wang, Y. Liu, J. Fang, J. He, Y. Tian, and H. Zhang, "Emergency sources pre-positioning for resilient restoration of distribution network," Energy Reports, vol. 6, pp. 1283-1290, 2020/12/01/ 2020. [DOI:10.1016/j.egyr.2020.11.042]
32. [32] J. Zhu, Y. Yuan, and W. Wang, "An exact microgrid formation model for load restoration in resilient distribution system," International Journal of Electrical Power & Energy Systems, vol. 116, p. 105568, 2020/03/01/ 2020. [DOI:10.1016/j.ijepes.2019.105568]
33. [33] M. Nikzad and A. Samimi, "Integration of Optimal Time-of-Use Pricing in Stochastic Programming for Energy and Reserve Management in Smart Micro-grids," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020/05/09 2020.
34. [34] M. Sedighizadeh, M. Esmaili, A. Jamshidi, and M.-H. Ghaderi, "Stochastic multi-objective economic-environmental energy and reserve scheduling of microgrids considering battery energy storage system," International Journal of Electrical Power & Energy Systems, vol. 106, pp. 1-16, 2019/03/01/ 2019. [DOI:10.1016/j.ijepes.2018.09.037]
35. [35] G. Papaefthymiou and D. Kurowicka, "Using Copulas for Modeling Stochastic Dependence in Power System Uncertainty Analysis," IEEE Transactions on Power Systems, vol. 24, pp. 40-49, 2009. [DOI:10.1109/TPWRS.2008.2004728]
36. [36] F. Chen, F. Li, W. Feng, Z. Wei, H. Cui, and H. Liu, "Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms," Electric Power Systems Research, vol. 166, pp. 73-82, 2019/01/01/ 2019. [DOI:10.1016/j.epsr.2018.09.023]
37. [37] D. E. Knuth, The Art of Computer Programming, The: Combinatorial Algorithms, Part 1 vol. 4A: Addison Wesley, 2001.


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Samimi A, Nikzad M. Optimal Restoration of Active Distribution Systems for Enhancing Resilience Considering the Uncertainty of Renewable Sources. ieijqp. 2021; 10 (3) :97-108
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Volume 10, Issue 3 (10-2021) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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