Resilient Planning Against Disturbances and Optimal Location Determination for Mobile Energy Storage Systems in Smart Microgrids

Document Type : Original Article

Authors

1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 East Azerbaijan Electric Power Distribution Company

Abstract

Batteries, as one of the types of electrical energy storage devices, play an important role in increasing the resilience of distribution networks against extreme events such as floods. However, the use of these devices solely for increasing resilience is not economically justified. In this paper, an innovative approach is presented for the use of vehicle-mounted batteries that, with the ability to be moved through public transportation routes, enable the creation of dynamic microgrids and reduce outages in critical areas. The main innovation of this study is the development of a two-stage optimization framework that, in the first stage, maximizes the optimal location and investment in battery storage modules and, in the second stage, enables the rerouting of these modules to cope with extreme floods and ensure the continuity of energy supply. The proposed approach has been tested on radial distribution networks with 15, 33, and 85 buses. The results show that the use of mobile batteries significantly reduces load shedding compared to fixed batteries, with the average load cap being 11% lower. Also, among the three scenarios studied, the scenario using mobile batteries provides the lowest objective function value and the most optimal performance. This method has been able to reduce outages by 35% and optimize overall operating and investment costs by 22%.

Keywords


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  1. S. Paul, A. Poudyal, S. Poudel, A. Dubey, Z. Wang, "Resilience Assessment and Planning In Power distribution systems: Past and future considerations," Renewable and Sustainable Energy Reviews, vol. 189, pp. 113991, 2024. https://doi.org/10.1016/j.rser.2023.113991
  2. M. Panteli, P. Mancarella, "The grid: Stronger, Bigger, Smarter? Presenting a Conceptual Framework of Power System Resilience," IEEE Power and Energy Magazine, vol. 13, no. 3, pp. 58-66, May/June 2015. doi: 10.1109/MPE.2015.2397334
  3. J. J. Plotnek, J. Slay, "Power Systems Resilience: Definition And Taxonomy With A View Towards Metrics," International Journal of Critical Infrastructure Protection, vol. 33, pp. 100411, 2021. https://doi.org/10.1016/j.ijcip.2021.100411
  4. K. Jin, H. Banizaman, S. S. Gharehveran, et al., "Robust power management capabilities of integrated energy systems in the smart distribution network including linear and non-linear loads," Scientific Reports, vol. 15, p. 6615, 2025. doi: 10.1038/s41598-025-89817-0

5.H. H. Abdeltawab, Y. A. Mohamed, "Mobile Energy Storage Scheduling And Operation in Active Distribution Systems," IEEE Transactions on Industrial Electronics, vol. 64, no. 9, pp. 6828-6840, 2017. doi: 10.1109/TIE.2017.2682779

  1. F. Mujjuni, T. R. Betts, R. E. Blanchard, "Evaluation of power systems resilience to extreme weather events: A review of methods and assumptions," IEEE Access, vol. 11, pp. 87279-87296, 2023. doi: 10.1109/ACCESS.2023.3304643
  2. H. Zhang, P. Wang, S. Yao, X. Liu, T. Zhao, "Resilience Assessment of Interdependent Energy Systems Under Hurricanes," IEEE Transactions on Power Systems, vol. 35, no. 5, pp. 3682-3694, 2020. https://doi.org/10.1016/j.ijepes.2022.108616
  3. M. A. Mohamed, T. Chen, W. Su, 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
  4. T. Khalili, M. T. Hagh, S. G. Zadeh, S. Maleki, "Optimal Reliable and Resilient Construction of Dynamic Self‐adequate Multi‐microgrids Under Large‐scale Events," IET Renewable Power Generation, vol. 13, no. 10, pp. 1750-1760, 2019. https://doi.org/10.1049/iet-rpg.2018.6222
  5. S. S. Gharehveran, S. Ghassem Zadeh, N. Rostami, "Resilience-oriented planning and pre-positioning of vehicle-mounted energy storage facilities in community microgrids," Journal of Energy Storage, vol. 72, pp. 108263, 2023. https://doi.org/10.1016/j.est.2023.108263
  6. F. Ni, et al., "Enhancing Resilience of DC Microgrids With Model Predictive Control Based Hybrid Energy Storage System," International Journal of Electrical Power & Energy Systems, vol. 128, pp. 106738, 2021. https://doi.org/10.1016/j.ijepes.2020.106738
  7. J. P. Moreno, C. Rahmann, R. Moreno, L. Morán "Co-optimizing transmission and BESS expansions with system strength constraints," Electric Power Systems Research, vol. 235, pp. 110696, 2024. https://doi.org/10.1016/j.epsr.2024.110696
  8. X. Jiang, J. Chen, W. Zhang, Q. Wu, Y. Zhang, J. Liu, "Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks," Journal of Modern Power Systems and Clean Energy, vol. 9, no. 4, pp. 788-799, 2021. doi: 10.35833/MPCE.2020.000910
  9. A. Karimi Saiedabadi, B. Mozafari, S. Soleymani, and H. Mohammadnezhad Shourkaei, "Optimal Operation of the Water-Energy Supply System in an Islanded Microgrid with Several Energy Carriers to Improve Resilience Against Cyber Attacks," Passive Defense, vol. 13, no. 4, pp. 1-10, 2023.( in persian). 20.1001.1.20086849.1401.13.4.1.0
  10. 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). 20.1001.1.20086849.1403.15.3.3.4
  11. D. Baghbanzadeh, et al., "Resilience Improvement of Multi-microgrid Distribution Networks Using Distributed Generation," Sustainable Energy, Grids and Networks, vol. 27, pp. 100503, 2021. https://doi.org/10.1016/j.segan.2021.100503
  12. N. Saeedi, D. Baharvand, K. Shirini, et al., "Prediction of electrical energy consumption using principal component analysis and independent components analysis," Journal of Supercomputing, vol. 81, p. 1072, 2025. doi: 10.1007/s11227-025-07505-2
  13. 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, Art. no. 101022, 2025. https://doi.org/10.1016/j.prime.2025.101022
  14. J. Tan, R. M. Radhi, K. Shirini, S. S. Gharehveran, Z. Parisooz, M. Khosravi, and H. Azarinfar, "Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning," Scientific Reports, vol. 15, no. 1, p. 15669, 2025. https://doi.org/10.1038/s41598-025-98235-1
  15. S. S. Gharehveran, S. Ghassemzadeh, 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, pp. 103952, 2022. https://doi.org/10.1016/j.scs.2022.103952
  16. H. Zaki Dizaji, K. Shirini, A. Taheri Hajivand, and N. Monjezi, "Modelling variables affecting the yield of sugarcane fields using deep recurrent neural network," Iranian Journal of Biosystems Engineering, vol. 55, no. 2, pp. 93–108, 2024. doi: 10.22059/ijbse.2025.378958.665557
  17. F. Gazijahani, J. Salehi, M. Shafie-khah, "Benefiting From Energy-Hub Flexibilities to Reinforce Distribution System Resilience: A Pre-and Post-Disaster Management Model," IEEE Systems Journal, vol. 16, no. 2, pp. 3381-3390, 2022. DOI: 10.1109/jsyst.2022.3147075
  18. S. Yao, P. Wang, X. Liu, H. Zhang, T. Zhao, "Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration," IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1030-1043, 2019. doi: 10.1109/TSG.2019.2930012.
  19. N. N. Ibrahim, J. J. Jamian, M. M. Rasid, "Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns," Energy, vol. 304, pp. 131921, 2024. https://doi.org/10.1016/j.energy.2024.131921
  20. M. T. Sattari, K. Shirini, and S. Javidan, "Evaluating the efficiency of dimensionality reduction methods in improving the accuracy of water quality index modeling in Qizil-Uzen River using machine learning algorithms," Water and Soil Management and Modelling, vol. 4, no. 2, pp. 89–104, 2024. doi: 10.22098/mmws.2023.12434.1241
  21. M. T. Sattari, R. Bagheri, K. Shirini, and P. Allahverdipour, "Modeling Daily and Monthly Rainfall in Tabriz using Ensemble Learning Models and Decision Tree Regression," Journal of the Climate Change Research, vol. 5, no. 18, pp. 31–48, 2024. Doi:  10.30488/ccr.2024.433394.1192
  22. M. Ahrari, K. Shirini, S. S. Gharehveran, M. G. Ahsaee, S. Haidari, 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, pp. 111024, 2024. https://doi.org/10.1016/j.est.2024.111024
  23. S. S. Gharehveran, K. Shirini, S. Cheshmeh Khavar, S. H. Mousavi, A. Abdolahi, "Deep learning-based demand response for short-term operation of renewable-based microgrids," Journal of Supercomputing, vol. 80, pp. 26002–26035, 2024. https://doi.org/10.1007/s11227-024-06407-z

29.Shirini, K., Aghdasi, H.S. & Saeedvand, S. A Comprehensive Survey on Multiple-Runway Aircraft Landing Optimization Problem. Int. J. Aeronaut. Space Sci. 25, 1574–1602 (2024). https://doi.org/10.1007/s42405-024-00747-z

  1. Shirini, K., Aghdasi, H.S. & Saeedvand, S. Multi-objective aircraft landing problem: a multi-population solution based on non-dominated sorting genetic algorithm-II. J Supercomput 80, 25283–25314 (2024). https://doi.org/10.1007/s11227-024-06385-2
  2. Taherihajivand, A. , shirini, K. , & samadi Gharehveran, S. (2024). Weed detection in fields using convolutional neural network based on deep learning. Agricultural Engineering, 47(1), 129-142. (In Persian( doi: 10.22055/agen.2024.45327.1688
  3. Taherihajivand, A. , shirini, K. , & samadi gharehveran, S. (2024). An Overview of Product Performance Prediction Using Artificial Algorithms. Agricultural Mechanization, 9(3), 1-14. (In Persian) doi: 10.22034/jam.2024.61899.1276
  4. Shirini, K., Kordan, M.B. & Gharehveran, S.S. Impact of learning rate and epochs on lstm model performance: a study of chlorophyll-a concentrations in the Marmara Sea. J Supercomput 81, 265 (2025). https://doi.org/10.1007/s11227-024-06806-2
  5. zahiri, M., shirini, K., & samadi gharehveran, S. (2024). Network traffic analysis with machine learning for faster detection of distributed denial of service attack. Journal of Advanced Defense Science & Technology, 4, 67-76, (2024). (In Persian) https://dorl.net/dor/20.1001.1.26762935.1402.14.3.1.5

35.Taheri hajivand, A. , Shirini, K. , & Samadi Gharehveran, S. (2024). Balancing Time and Cost in Resource-Constrained Project Scheduling Using Meta-Heuristic Approach. Journal of Agricultural Machinery, 14(2), 215-234. (In Persian)  doi: 10.22067/jam.2023.81735.1157

  1. shirini, K. , Taherihajivand, A. , & samadi Gharehveran, S. (2023). A review of algorithms for solving the project scheduling problem with resource-constrained considering agricultural problems. Agricultural Mechanization, 8(1), 1-14. (In Persian) doi: 10.22034/jam.2023.55751.1227
  2. Zamanian, S., & sadeghzadeh, S. M. (2022). Presentation of an Economic-Environmental Planning Model for the Interconnected Water and Electricity Network Microgrids. Passive Defense, 13(3), 67-75. (In Persian). Dor: 20.1001.1.20086849.1401.13.3.7.4

38.abedini, M., & Moazami, H. (2024). Reconfiguration of electricity distribution networks to increase resilience and reliability using fuzzy objective functions and game theory. Passive Defense, 15(3), 27-38. (In Persian) 20.1001.1.20086849.1403.15.3.3.4

  1. Taghi Tahooneh, M., Dashti, R., Ghaffarpour, R., & Jalali Farahani, G. (2021). New Critical Infrastructure Protection Strategies. Passive Defense, 11(4), 1-6. (In Persian) dor: 20.1001.1.20086849.1399.11.4.1.6