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Panjeie, S., Fakharian, A., Sedighizadeh, M., Sheikhi fini, A.. (1403). Robust Scheduling of Unbalanced Microgrids for Enhancing Resilience by Outage Management Strategy. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2024.11267.1841
S. Panjeie; A. Fakharian; M. Sedighizadeh; A. Sheikhi fini. "Robust Scheduling of Unbalanced Microgrids for Enhancing Resilience by Outage Management Strategy". سامانه مدیریت نشریات علمی, , , 1403, -. doi: 10.22098/joape.2024.11267.1841
Panjeie, S., Fakharian, A., Sedighizadeh, M., Sheikhi fini, A.. (1403). 'Robust Scheduling of Unbalanced Microgrids for Enhancing Resilience by Outage Management Strategy', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2024.11267.1841
Panjeie, S., Fakharian, A., Sedighizadeh, M., Sheikhi fini, A.. Robust Scheduling of Unbalanced Microgrids for Enhancing Resilience by Outage Management Strategy. سامانه مدیریت نشریات علمی, 1403; (): -. doi: 10.22098/joape.2024.11267.1841

Robust Scheduling of Unbalanced Microgrids for Enhancing Resilience by Outage Management Strategy

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 05 تیر 1403    اصل مقاله (7.04 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.11267.1841
نویسندگان
S. Panjeie1؛ A. Fakharian* 1؛ M. Sedighizadeh2؛ A. Sheikhi fini3
1Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
2Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran.
3Department of Power Systems Operation and Planning, Niroo Research Institute, Tehran, Iran.
چکیده
Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration (DFR) and scheduling of the distributed energy resources (DERs). Later happening a line fault, the radial network topology is determined by the proposed model using an evaluation of the inci-dence matrix. The presented work handles the uncertain behavior of non-dispatchable DERs and the electrical loads which model by the robust optimization approach. To expand the flexibility of the proposed model, the demand response program (DRP) is treated as the curtailed demand. The aim of optimization is the minimization of the total cost for dispatchable DER operation and electrical load decrease. The recommended robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. Applying the suggested model in the 69-bus unbalanced test system demonstrate that the proposed model averagely decreases total operation cost and execution time by 10.62% and 22.23% on all scenarios in comparison with the de-terministic model.
کلیدواژه‌ها
Distributed energy resource؛ distribution feeder reconfiguration؛ resilience؛ robust optimization؛ outage management strategy
مراجع
  1. A. Dehghani, M. Sedighizadeh, and F. Haghjoo, “An overview of the assessment metrics of the concept of resilience in electrical grids,” Int. Trans. Electr. Energy Syst., vol. 31, no. 12, p. e13159, 2021.
  2. S. Behzadi, A. Bagheri, and A. Rabiee, “Resilience-oriented operation of micro-grids in both grid-connected and isolated conditions within sustainable active distribution networks,” J. Oper. Autom. Power Eng., 2023.
  3. M. Abdelmalak and M. Benidris, “Proactive generation redispatch to enhance power system resilience during hurricanes considering unavailability of renewable energy sources,” IEEE Trans. Ind. Appl., vol. 58, no. 3, pp. 3044– 3053, 2022.
  4. N. 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.
  5. F. Jabari, M. Zeraati, M. Sheibani, and H. Arasteh, “Robust self-scheduling of pvs-wind-diesel power generation units in a standalone microgrid under uncertain electricity prices,” J. Oper. Autom. Power Eng., vol. 12, no. 2, pp. 152–162, 2024.
  6. Q. Shi, W. Liu, B. Zeng, H. Hui, and F. Li, “Enhancing distribution system resilience against extreme weather events: Concept review, algorithm summary, and future vision,” Int. J. Electr. Power Energy Syst., vol. 138, p. 107860, 2022.
  7. S. Ma, B. Chen, and Z. Wang, “Resilience enhancement strategy for distribution systems under extreme weather events,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1442– 1451, 2016.
  8. Y. Lin and Z. Bie, “Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and dg islanding,” Appl. Energy, vol. 210, pp. 1266–1279, 2018.
  9. M. Sedighizadeh, M. Ghalambor, and A. Rezazadeh, “Reconfiguration of radial distribution systems with fuzzy multi-objective approach using modified big bang-big crunch algorithm,” Arabian J. Sci. Eng., vol. 39, pp. 6287–6296, 2014.
  10. M. Sedighizadeh, G. Shaghaghi-shahr, M. Esmaili, and M. R. Aghamohammadi, “Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties,” J. Energy Storage, vol. 21, pp. 58–71, 2019.
  11. Z. 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.
  12. H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, “Enhancing power system resilience through hierarchical outage management in multi-microgrids,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2869–2879, 2016.
  13. S. Ma, L. Su, Z. Wang, F. Qiu, and G. Guo, “Resilience enhancement of distribution grids against extreme weather events,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 4842– 4853, 2018.
  14. M. Sedighizadeh, M. Esmaili, and M. Mahmoodi, “Reconfiguration of distribution systems to improve reliability and reduce power losses using imperialist competitive algorithm,” Iran. J. Electr. Electron. Eng., vol. 13, no. 3, p. 287, 2017.
  15. H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, “Role of outage management strategy in reliability performance of multi-microgrid distribution systems,” IEEE Trans. Power Syst., vol. 33, no. 3, pp. 2359–2369, 2017.
  16. C. 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.
  17. Y. Wang, Y. Xu, J. He, C.-C. Liu, K. P. Schneider, M. Hong, and D. T. Ton, “Coordinating multiple sources for service restoration to enhance resilience of distribution systems,” IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 5781–5793, 2019.
  18. B. Chen, Z. Ye, C. Chen, and J. Wang, “Toward a milp modeling framework for distribution system restoration,” IEEE Trans. Power Syst., vol. 34, no. 3, pp. 1749–1760, 2018.
  19. S. Yao, T. Zhao, P. Wang, and H. Zhang, “Resilience-oriented distribution system reconfiguration for service restoration considering distributed generations,” in 2017 IEEE Power Energy Soc. Gen Meet., pp. 1–5, IEEE, 2017.
  20. J. Liu, C. Qin, and Y. Yu, “Enhancing distribution system resilience with proactive islanding and rcs-based fast fault isolation and service restoration,” IEEE Trans. Smart Grid, vol. 11, no. 3, pp. 2381–2395, 2019.
  21. D. Dwivedi, P. K. Yemula, and M. Pal, “Evaluating the planning and operational resilience of electrical distribution systems with distributed energy resources using complex network theory,” Renewable Energy Focus, 2023.
  22. A. Rahiminejad, M. Ghafouri, R. Atallah, W. Lucia, M. Debbabi, and A. Mohammadi, “Resilience enhancement of islanded microgrid by diversification, reconfiguration, and der placement/sizing,” Int. J. Electr. Power Energy Syst., vol. 147, p. 108817, 2023.
  23. [23]    A. Serrano-Fontova, Z. Liao, H. Li, and C. Booth, “A novel resilience assessment for active distribution networks including a der voltage regulation scheme considering windstorms,” Int. J. Electr. Power Energy Syst., vol. 153, p. 109310, 2023.
  24. E. Kianmehr, S. Nikkhah, V. Vahidinasab, D. Giaouris, and P. C. Taylor, “A resilience-based architecture for joint distributed energy resources allocation and hourly network reconfiguration,” IEEE Trans. Ind. Inf., vol. 15, no. 10, pp. 5444–5455, 2019.
  25. M. Sedighizadeh, G. Shaghaghi-shahr, M. R. Aghamohammadi, and M. Esmaili, “A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously,” Int. Trans. Electr. Energy Syst., vol. 30, no. 4, p. e12302, 2020.
  26. G. Shaghaghi-shahr, M. Sedighizadeh, M. Aghamohammadi, and M. Esmaili, “Optimal generation scheduling in microgrids using mixed-integer second-order cone programming,” Eng. Optim., vol. 52, no. 12, pp. 2164–2192, 2020.
  27. Q. Shi, F. Li, M. Olama, J. Dong, Y. Xue, M. Starke, C. Winstead, and T. Kuruganti, “Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience,” Int. J. Electr. Power Energy Syst., vol. 124, p. 106355, 2021.
  28. M. Sedighizadeh, S. S. Fazlhashemi, H. Javadi, and M. Taghvaei, “Multi-objective day-ahead energy management of a microgrid considering responsive loads and uncertainty of the electric vehicles,” J. Cleaner Prod., vol. 267, p. 121562, 2020.
  29. S. S. Fazlhashemi, M. Sedighizadeh, and M. E. Khodayar, “Day-ahead energy management and feeder reconfiguration for microgrids with cchp and energy storage systems,” J. Energy Storage, vol. 29, p. 101301, 2020.
  30. P. Harsh and D. Das, “Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid,” Renewable Sustainable Energy Rev., vol. 160, p. 112251, 2022.
  31. F. H. Aghdam, N. T. Kalantari, and B. Mohammadi-Ivatloo, “A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements,” J. Energy Storage, vol. 29, p. 101416, 2020.
  32. F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation,” in 2008 IEEE Power Energy Soc. Gener. Meet.-Convers. Delivery Electr. Energy 21st Century, pp. 1–11, IEEE, 2008.
  33. Z. Tang, Y. Liu, L. Wu, J. Liu, and H. Gao, “Reserve model of energy storage in day-ahead joint energy and reserve markets: A stochastic uc solution,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 372–382, 2020.
  34. R. Torquato, Q. Shi, W. Xu, and W. Freitas, “A monte carlo simulation platform for studying low voltage residential networks,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2766– 2776, 2014.
  35. W. Gil-González, O. D. Montoya, E. Holguín, A. Garces, and L. F. Grisales-Noreña, “Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model,” J. Energy Storage, vol. 21, pp. 1–8, 2019.
  36. H. Yuan, F. Li, Y. Wei, and J. Zhu, “Novel linearized power flow and linearized opf models for active distribution networks with application in distribution lmp,” IEEE Trans. Smart Grid, vol. 9, no. 1, pp. 438–448, 2016.
  37. S. Dunn, S. Wilkinson, D. Alderson, H. Fowler, and C. Galasso, “Fragility curves for assessing the resilience of electricity networks constructed from an extensive fault database,” Nat. Hazard. Rev., vol. 19, no. 1, p. 04017019, 2018.
  38. Z. K. Pecenak, M. Stadler, P. Mathiesen, K. Fahy, and J. Kleissl, “Robust design of microgrids using a hybrid minimum investment optimization,” Appl. Energy, vol. 276, p. 115400, 2020.
  39. N. Rezaei, A. Khazali, M. Mazidi, and A. Ahmadi, “Economic energy and reserve management of renewablebased microgrids in the presence of electric vehicle aggregators: A robust optimization approach,” Energy, vol. 201, p. 117629, 2020.
  40. N. Nikmehr, “Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs,” Energy, vol. 199, p. 117440, 2020.
  41. M. Kafaei, D. Sedighizadeh, M. Sedighizadeh, and A. S. Fini, “A two-stage igdt/tpem model for optimal operation of a smart building: A case study of gheshm island, iran,” Therm. Sci. Eng. Prog., vol. 24, p. 100955, 2021.
  42. S. M. Mousavi-Taghiabadi, M. Sedighizadeh, M. Zangiabadi, and A. S. Fini, “Integration of wind generation uncertainties into frequency dynamic constrained unit commitment considering reserve and plug in electric vehicles,” J. Cleaner Prod., vol. 276, p. 124272, 2020.
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