آمار

تعداد نشریات26
تعداد شماره‌ها308
تعداد مقالات2,649
تعداد مشاهده مقاله3,979,318
تعداد دریافت فایل اصل مقاله2,681,931
Karimi, M., Eslamian, M.. (1402). A Resilience-Oriented Graph-Based Method for Restoration of Critical Loads in Distribution Networks Using Microgrids. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2024.12110.1898
M. Karimi; M. Eslamian. "A Resilience-Oriented Graph-Based Method for Restoration of Critical Loads in Distribution Networks Using Microgrids". سامانه مدیریت نشریات علمی, , , 1402, -. doi: 10.22098/joape.2024.12110.1898
Karimi, M., Eslamian, M.. (1402). 'A Resilience-Oriented Graph-Based Method for Restoration of Critical Loads in Distribution Networks Using Microgrids', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2024.12110.1898
Karimi, M., Eslamian, M.. A Resilience-Oriented Graph-Based Method for Restoration of Critical Loads in Distribution Networks Using Microgrids. سامانه مدیریت نشریات علمی, 1402; (): -. doi: 10.22098/joape.2024.12110.1898

A Resilience-Oriented Graph-Based Method for Restoration of Critical Loads in Distribution Networks Using Microgrids

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 08 بهمن 1402    اصل مقاله (729.24 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.12110.1898
نویسندگان
M. Karimi؛ M. Eslamian*
Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
چکیده
This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used to determine the shortest paths between microgrids and critical loads, and then the feasible restoration trees are established by combining the restorable paths. A mixed-integer linear programming (MILP) model is then used to find the optimal selection of feasible restoration trees to make a restoration scheme. The service restoration is implemented with the objectives of maximizing the energy delivered to the critical loads and minimizing the number of switching operations. The limited fuel storage of the generation sources in microgrids, the operational constraints of the network and microgrids, as well as the radiality constraint of the restored sub-networks, are considered the constraints of the optimization problem. The presented method can be used for optimal restoration of critical loads including the number of switching operations which is essential for the ease of implementation of a restoration plan. The results of simulations on a 118-bus distribution network demonstrate the efficiency of the procedure.
کلیدواژه‌ها
Electric vehicles؛ optimization؛ particle swarm optimization؛ cuckoo search algorithm؛ load demand
مراجع
  1. J. Campbell and S. Lowry, “Weather-related power outages and electric system resiliency,” Congressional Research Service, Library of Congress Washington, DC, 2012.
  2. M. Salman, Y. Li, and M. G. Stewart, “Evaluating system reliability and targeted hardening strategies of power distribution systems subjected to hurricanes,” Reliab. Eng. Syst. Saf., vol. 144, pp. 319–333, 2015.
  3. Panteli and P. Mancarella, “Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies,” Electr. Power Syst. Res., vol. 127, pp. 259–270, 2015.
  4. Chen, J. Wang, F. Qiu, and D. Zhao, “Resilient distribution system by microgrids formation after natural disasters,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 958–966, 2015.
  5. Wang and J. Wang, “Self-healing resilient distribution systems based on sectionalization into microgrids,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3139–3149, 2015.
  6. C. López, J. F. Franco, and M. J. Rider, “Optimisationbased switch allocation to improve energy losses and service restoration in radial electrical distribution systems,” IET Gener. Transm. Distrib., vol. 10, no. 11, pp. 2792–2801, 2016.
  7. Liu, M. Shahidehpour, Z. Li, X. Liu, Y. Cao, and Z. Bie, “Microgrids for enhancing the power grid resilience in extreme conditions,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 589–597, 2016.
  8. Sekhavatmanesh and R. Cherkaoui, “Optimal infrastructure planning of active distribution networks complying with service restoration requirements,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6566–6577, 2017.
  9. Sharma, D. Srinivasan, and A. Trivedi, “A decentralized multi-agent approach for service restoration in uncertain environment,” IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3394–3405, 2016.
  10. Toune, H. Fudo, T. Genji, Y. Fukuyama, and Y. Nakanishi, “Comparative study of modern heuristic algorithms to service restoration in distribution systems,” IEEE Trans. Power Delivery, vol. 17, no. 1, pp. 173–181, 2002.
  11. Kumar, B. Das, and J. Sharma, “Multiobjective, multiconstraint service restoration of electric power distribution system with priority customers,” IEEE Trans. Power Delivery, vol. 23, no. 1, pp. 261–270, 2007.
  12. -T. Hsiao and C.-Y. Chien, “Enhancement of restoration service in distribution systems using a combination fuzzyga method,” IEEE Trans. Power Syst., vol. 15, no. 4, pp. 1394–1400, 2000.
  13. T. Marques, A. C. B. Delbem, and J. B. A. London, “Service restoration with prioritization of customers and switches and determination of switching sequence,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 2359–2370, 2017.
  14. Li, X.-Y. Ma, C.-C. Liu, and K. P. Schneider, “Distribution system restoration with microgrids using spanning tree search,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 3021– 3029, 2014.
  15. Dimitrijevic and N. Rajakovic, “Service restoration of distribution networks considering switching operation costs and actual status of the switching equipment,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1227–1232, 2015.
  16. Khederzadeh and S. Zandi, “Enhancement of distribution system restoration capability in single/multiple faults by using microgrids as a resiliency resource,” IEEE Syst. J., vol. 13, no. 2, pp. 1796–1803, 2019.
  17. Gao, Y. Chen, Y. Xu, and C.-C. Liu, “Resilience-oriented critical load restoration using microgrids in distribution systems,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2837– 2848, 2016.
  18. Xu, C.-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 Trans. Smart Grid, vol. 9, no. 1, pp. 426–437, 2016.
  19. Wang, C. Chen, C. Li, Y. Cao, Y. Li, B. Zhou, and X. Dong, “A multi-stage restoration method for mediumvoltage distribution system with dgs,” IEEE Trans. Smart Grid, vol. 8, no. 6, pp. 2627–2636, 2016.
  20. Ding, Y. Lin, Z. Bie, and C. Chen, “A resilient microgrid formation strategy for load restoration considering masterslave distributed generators and topology reconfiguration,” Appl. Energy, vol. 199, pp. 205–216, 2017.
  21. -J. Yang, Y. Zhao, C. Wang, P. Gao, and J.-H. Hao, “Resilience-oriented hierarchical service restoration in distribution system considering microgrids,” IEEE Access, vol. 7, pp. 152729–152743, 2019.
  22. Poudel and A. Dubey, “Critical load restoration using distributed energy resources for resilient power distribution system,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 52–63, 2018.
  23. Afsari, S. SeyedShenava, and H. Shayeghi, “A milp model incorporated with the risk management tool for self-healing oriented service restoration,” J. Oper. Autom. Power Eng., vol. 12, no. 1, pp. 1–13, 2024.
  24. Ghasemi, A. Khodabakhshian, and R. Hooshmand, “Active distribution networks restoration after extreme events,” J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 152–163, 2020.
  25. Ghasemi, M. Mohammadi, and J. Moshtagh, “A new look-ahead restoration of critical loads in the distribution networks during blackout with considering load curve of critical loads,” Electr. Power Syst. Res., vol. 191, p. 106873, 2021.
  26. T. Nguyen, J. Muhs, and M. Parvania, “Preparatory operation of automated distribution systems for resilience enhancement of critical loads,” IEEE Trans. Power Delivery, vol. 36, no. 4, pp. 2354–2362, 2020.
  27. S. Kahnamouei and S. Lotfifard, “Enhancing resilience of distribution networks by coordinating microgrids and demand response programs in service restoration,” IEEE Syst. J., vol. 16, no. 2, pp. 3048–3059, 2021.
  28. Deo, Graph theory with applications to engineering and computer science. Courier Dover Publications, 2017.
  29. E. Leiserson, R. L. Rivest, T. H. Cormen, and C. Stein, Introduction to algorithms, vol. 3. MIT press Cambridge, MA, USA, 1994.
  30. Zhang, Z. Fu, and L. Zhang, “An improved ts algorithm for loss-minimum reconfiguration in large-scale distribution systems,” Electr. Power Syst. Res., vol. 77, no. 5-6, pp. 685–694, 2007.
  31. E. M.-S. R. D. Zimmerman and R. J. Thomas, “Matpower: Steady-state operations, planning and analysis tools for power systems research and education,” IEEE Trans. Power Syst., 2020.
آمار
تعداد مشاهده مقاله: 114
تعداد دریافت فایل اصل مقاله: 86


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب