پدافند غیرعامل

پدافند غیرعامل

راهبردهای بهره­برداری تاب­آور- محور شبکه­ های توزیع انرژی الکتریکی در شرایط بحرانی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دکتری مهندسی برق، دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران
2 دکتری مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران.
چکیده .
ساختار و کیفیت ذاتی سیستم‌های قدرت معاصر به دلیل افزایش تصاعدی نفوذ منابع تجدیدپذیر در سیستم‌های قدرت، تغییرات قابل‌توجهی را تجربه کرده است. سیستم‌های قدرت امروزی به دلیل استفاده از منابع تولید پراکنده بر اساس الکترونیک قدرت و مشکلات در تضمین عملکرد ایمن آنها با موانع فنی جدیدی روبرو هستند. علاوه بر افزایش غیرقابل‌پیش‌بینی و تغییرپذیری، افزایش نسبت منابع تجدیدپذیر در تولید برق باعث کاهش اینرسی چرخشی سیستم‌های قدرت نیز شده است. امروزه حوادث آب‌وهوایی باعث تحمیل هزینه­های اقتصادی و اجتماعی سرسام­آوری به جوامع بشری شده است. باوجود احتمال پایین وقوع این نوع رخدادها، شدت و تأثیر قابل‌توجه آن­ها بر روی عملکرد ایمن سیستم­های قدرت باعث شده تا توجهات زیادی بر روی افزایش تاب­آوری شبکه معطوف گردد. سیستم‌های ذخیره‌ساز قابل‌حمل می‌توانند در برابر حوادثی مانند قطعی برق، نوسانات بار، خرابی تجهیزات و سایر حوادث پیش‌بینی‌نشده در شبکه قدرت، تاب‌آوری و امنیت شبکه را افزایش دهند. این سیستم‌ها باقابلیت حمل‌ونقل، به‌سرعت می‌توانند در محل مورد نیاز نصب شده و به تأمین برق در زمان‌های بحرانی کمک کنند. این مقاله اقدامات مختلفی را برای افزایش تاب­آوری سیستم قدرت مورد بحث قرار می‌دهد و چالش‌های بالقوه را برای تحقیقات آینده بیان می‌کند.
کلیدواژه‌ها

عنوان مقاله English

Resilient-oriented Operation Strategies of Electrical Energy Distribution Networks in Critical Situations

نویسندگان English

Sina Samadi Gharehveran 1
Kimia Shirini 2
1 PhD in Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
2 PhD in Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.,
چکیده . English

The structure and inherent quality of contemporary power systems have undergone significant changes due to the exponential increase in the penetration of renewable resources. Today's power systems face new technical barriers due to the utilization of power electronics-based distributed generation resources and the challenges in ensuring their secure operation. In addition to increased unpredictability and variability, the rising share of renewable resources in power generation has also led to a reduction in the rotational inertia of power systems. Nowadays, extreme weather events impose staggering economic and social costs on human societies. Despite the low probability of occurrence of such events, their considerable intensity and impact on the secure operation of power systems have drawn widespread attention toward enhancing network resilience. Mobile energy storage systems can improve grid resilience and security against events such as power outages, load fluctuations, equipment failures, and other unforeseen incidents in the power network. With their mobility capability, these systems can be rapidly deployed at required locations to help supply electricity during critical times. This paper discusses various measures to enhance power system resilience and outlines potential challenges for future research.

کلیدواژه‌ها English

Distribution Networks
Mobile Energy Storage
Renewable Resources
Resilience
Weather Events
[1]   M. Abedini and H. Moazami, "Reconfiguration of electricity distribution networks to increase resilience and reliability using fuzzy objective functions and game theory," Passive Defense, vol. 15, no. 3, pp. 27-38, 2024(in persian). DOR: 20.1001.1.20086849.1403.15.1.1.8
[2]   L. Che, M. Khodayar, M. Shahidehpour, “Only connect: Microgrids for distribution system restoration,” IEEE Power Energy Magazine, vol. 12, no. 1, pp. 70-81, 2014. doi: 10.1109/MPE.2013.2286317
[3]   Y. Lin, Z. Bie. “Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding.” Applied Energy, vol. 210, pp. 1266-1279, 2018. DOI: 10.1016/j.apenergy.2017.06.059
[4]   A. Arab, et al. “System hardening and condition-based maintenance for electric power infrastructure under hurricane effects.” IEEE Transactions on Reliability, vol. 65, no. 3, pp. 1457-1470, 2016. DOI: 10.1109/TR.2016.2575445
[5]   S. Samadi Gharehveran, G. Zadeh, and M. Nasiri, "Optimum operation of resilient multi-energy microgrids under low probability and high impact events," Journal of Advanced Defense Science & Technology, vol. 16, no. 1, pp. 51-60, 2025 (in persian).
[6]   M. Mirsadeghi and R. Ghaffarpour, "Improving the Resilience of Power Grids in the Face of Focused Attacks Using the Contingency Analysis," Passive Defense, vol. 13, no. 3, pp. 1-10, 2022(in persian). 20.1001.1.20086849.1401.13.3.1.8
[7]   R. Ghaffarpour, S. Sadi, S. Zamanian, and M. Mahmoudian, "Improving Electrical Distribution Systems Resilience by their Optimal and Adaptable Operation in the Presence of Mobile Power Sources," Scientific Journal of Passive Defence, vol. 14, no. 3, 2023(in persian). 20.1001.1.20086849.1402.14.3.9.3
[8]   S. S. Gharehveran, K. Shirini, S. C. Khavar, and A. Abdollahi, "Optimizing day-ahead power scheduling: a novel MIQCP approach for enhanced SCUC with renewable integration," e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 12, p. 101022, 2025, doi: https://doi.org/10.1016/j.prime.2025.101022.
[9]   Lu, Xinhui, and Kaile Zhou. "A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs." Journal of Energy Storage 78: 110085, 2024. https://doi.org/10.1016/j.est.2023.110085
[10] A. Hussain, V. Bui, H. Kim. “A resilient and privacy-preserving energy management strategy for networked microgrids.” IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 2127-2139, 2018. doi: 10.1109/TSG.2016.2607422.
[11] S. Samadi Gharehveran and M. Nasiri, "Resilient planning against disturbances and optimal location determination for mobile energy storage systems in smart microgrids," Passive Defense, vol. 16, no. 2, pp. 69-80, 2025 (in persian). DOR: 20.1001.1.20086849.1404.16.2.6.2
[12] S. Samadi Gharehveran, "A review of energy management of multi-microgrid power systems in the presence of uncertainty of distributed generation resources," Power, Control, and Data Processing Systems, vol. 2, no. 4, pp. 46-58, 2025, doi: 10.30511/pcdp.2025.2072671.1046.
[13] S. Samadi Gharehveran and K. Shirini, "Resilient-oriented Operation Strategies of Electrical Energy Distribution Networks in Critical Situations," Passive Defense, vol. 16, no. -, p. e210556, 2026 (in persian).
[14] A. Gholami, T. Shekari, S. Grijalva. “Proactive management of microgrids for resiliency enhancement: An adaptive robust approach.” IEEE Transactions on Sustainable Energy, vol. 10, no. 1, pp. 470-480, 2017. doi: 10.1109/TSTE.2017.2740433
[15] G. Huang, et al. “Integration of preventive and emergency responses for power grid resilience enhancement.” IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4451-4463, 2017. DOI: 10.1109/TPWRS.2017.2685640
[16] S. Samadi Gharehveran, "Optimization of the number of batteries and their arrangement in electric vehicles based on multi-objective optimization," Passive Defense, vol. 16, no. 4, pp. 17-33, 2026 (in persian), doi: https://doi.org/10.47176/PD.2026.1490.
[17] A. Hussain, V. Bui, H. Kim. “A proactive and survivability-constrained operation strategy for enhancing resilience of microgrids using energy storage system.” IEEE Access, vol. 6, pp. 75495-75507, 2018. doi: 10.1109/ACCESS.2018.2883418.
[18] A. Hayati, S. S. Gharehveran, and K. Shirini, "Electricity price forecasting with ensemble meta-models and SHAP explainers: a PCA-driven approach," Sci Rep, vol. 16, p. 6466, 2026, https://doi.org/10.1038/s41598-026-35839-1.
[19] S. M. Farshbaf, K. Shirini, S. S. Gharehveran, and A. Abdollahi, "Reducing Phase Lag and Noise in Residential Load Forecasting with a Hybrid Residual-Attention Model," Algorithms, vol. 19, no. 8, p. 661, 2026, doi: 10.3390/a19080661.
[20] Mishra, Dillip Kumar, et al. "A detailed review of power system resilience enhancement pillars." Electric Power Systems Research 230: 110223, 2024. https://doi.org/10.1016/j.epsr.2024.110223
[21] S. Samadi Gharehveran, K. Shirini, M. Sarhangzadeh, and S. Gholinavaz, "A Review of Game Theory-based Approaches for Demand Side Management in Smart Energy Grids," Power, Control, and Data Processing Systems, vol. 3, no. 1, pp. 28-37, 2026, doi: 10.30511/pcdp.2025.2072667.1045.
[22] T. Ding, et al. “A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration.” Applied energy, vol. 199, pp. 205-216, 2017. DOI: 10.1016/j.apenergy.2017.05.012
[23] L. Che, M. Shahidehpour. “Adaptive formation of microgrids with mobile emergency resources for critical service restoration in extreme conditions.” IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 742-753, 2019. DOI: 10.1109/TPWRS.2018.2866099
[24] N. S. S. Singh et al., "Artificial Neural Network-Based Adaptive Voltage Control of a Hybrid Zeta DC/DC Converter Using Multilayer Perceptron and Radial Basis Function Architectures With Back-Propagation Learning," in IEEE Access, vol. 14, pp. 105596-105622, 2026, doi: 10.1109/ACCESS.2026.3703319.
[25] Leandro, J., Cunneff, S., & Viernstein, L. "Resilience modeling of flood induced electrical distribution network failures: Munich, Germany." Frontiers in Earth Science, 9, 572925, 2021. https://doi.org/10.3389/feart.2021.572925
[26] A. G. Oskouei, N. Abdolmaleki, A. Bouyer, B. Arasteh, and K. Shirini, “Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering,” Biomedical Signal Processing and Control, vol. 100, p. 107063, 2025. https://doi.org/10.1016/j.bspc.2024.107063
[27] K. Shirini, H. S. Aghdasi, and S. Saeedvand, “Modified imperialist competitive algorithm for aircraft landing scheduling problem,” The Journal of Supercomputing, vol. 80, no. 10, pp. 13782–13812, 2024. https://doi.org/10.1007/s11227-024-05999-w
[28] K. Shirini, H. S. Aghdasi, and S. Saeedvand, “A comprehensive survey on multiple-runway aircraft landing optimization problem,” International Journal of Aeronautical and Space Sciences, vol. 25, no. 4, pp. 1574–1602, 2024. https://doi.org/10.1007/s42405-024-00747-z
[29] K. Shirini, H. S. Aghdasi, and S. Saeedvand, “Multi-objective aircraft landing problem: a multi-population solution based on non-dominated sorting genetic algorithm-II,” The Journal of Supercomputing, vol. 80, no. 17, pp. 25283–25314, 2024. https://doi.org/10.1007/s11227-024-06385-2
[30] M. Ahrari, K. Shirini, S. S. Gharehveran, M. G. Ahsaee, S. Haidari, and P. Anvari, “A security-constrained robust optimization for energy management of active distribution networks with presence of energy storage and demand flexibility,” Journal of Energy Storage, vol. 84, p. 111024, 2024. https://doi.org/10.1016/j.est.2024.111024
[31] S. S. Gharehveran, S. Ghassemzadeh, and N. Rostami, “Two-stage resilience-constrained planning of coupled multi-energy microgrids in the presence of battery energy storages,” Sustainable Cities and Society, vol. 83, p. 103952, 2022. https://doi.org/10.1016/j.scs.2022.103952
[32] S. S. Gharehveran, S. G. Zadeh, and N. Rostami, “Resilience-oriented planning and pre-positioning of vehicle-mounted energy storage facilities in community microgrids,” Journal of Energy Storage, vol. 72, p. 108263, 2023. https://doi.org/10.1016/j.est.2023.108263
[33] A. Taherihajivand, K. Shirini, and S. Samadi Gharehveran, “Weed detection in fields using convolutional neural network based on deep learning,” Agricultural Engineering, vol. 47, no. 1, pp. 129–142, 2024(in persian). https://doi.org/10.22055/agen.2024.45327.1688
دوره 17، شماره 2 - شماره پیاپی 66
شماره پیا پی 66 تابستان 1405
تابستان 1405
صفحه 1-17

  • تاریخ دریافت 07 آذر 1403
  • تاریخ بازنگری 06 اردیبهشت 1404
  • تاریخ پذیرش 29 شهریور 1404
  • تاریخ انتشار 01 مرداد 1405