Improving Resilience Based on Proactive Scheduling Management in Multi-‎energy Carrier ‎Distribution Network Using Microgrids

Document Type : Original Article

Authors

Department of Electrical Engineering, Lahijan branch, Islamic Azad University, Lahijan, Iran

Abstract

In recent years, due to the interconnectedness and stress on power distribution and natural gas networks, enhancing the level of resilience against severe natural events such as storms has become crucial and vital. The presence of energy storage systems in microgrids has transformed them into reliable resilience sources in electric energy distribution systems. In this regard, studying the improvement of resilience in distribution networks in the presence of microgrids holds special importance. The objective of this article is to achieve the maximum utilization of available network storage to supply critical and non-critical electrical loads while minimizing the loss of load prior to the occurrence of severe events. To this end, a multi-objective optimization algorithm, namely Ant Colony Optimization, has been employed for proactive scheduling and achieving optimal decisions within consecutive time periods. Simulation results demonstrate that increasing the number of microgrids and expanding energy storage systems in the network not only improves network loadability but also reduces the amount of lost load by 15.27%, thereby increasing the level of resilience.

Keywords

Main Subjects


[1] M. Panteli, D.N. Trakas, P. Mancarella and N.D. Hatziargyriou, "Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies", Proceedings of the IEEE, Vol. 105, No. 7, 2017, pp. 1202-1213‎, http://doi: 10.1109/JPROC.2017.2691357.‎
 [2] R. Nateghi, "Multi-Dimensional Infrastructure Resilience Modeling: An Application to Hurricane-Prone Electric Power Distribution Systems", vol. 9, no. 4, pp. 2918 – 2929, July 2018‎, http://doi: 10.1109/ACCESS.2018.2792680.‎
[3] P. Bajpai, S. Chanda, K. Srivastava, "A Novel Metric to Quantify and Enable Resilient Distribution System using Graph Theory and Choquet Integral", IEEE Trans. Smart Grid, Vol. 9, no. 4, pp. 2918 – 2929, July 2018‎, http://doi: 10.1109/TSG.2016.2623818.‎
 [4] Z. Li, M. Shahidehpour, F. Aminifar, A. Alabdulwahab and Y. Al-Turki, "Networked Microgrids for Enhancing the Power System Resilience", Proceedings of the IEEE, Vol. 105, No. 7, 2017, pp. 1289-1310http://doi: 10.1109/JPROC.2017.2685558.‎
 [5] Ye ,Z., 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, 2021. 17: 1386-96‎, http://doi: 10.1109/TII.2020.2976831.‎
 [6] Borghei, Moein, and Mona Ghassemi. 'Optimal planning of microgrids for resilient distribution networks', International Journal of Electrical Power & Energy Systems, 128: 106682.2021. https://doi.org/10.1016/j.ijepes.2020.106682.‎
 [7] Wang, Hongbin, Yuquan Liu, Jian Fang, Jiaxing He, Yan Tian, and Hang Zhang. 'Emergency sources prepositioning for resilient restoration of distribution network', Energy Reports, 6: 1283-90‎,2020. https://doi.org/10.1016/j.egyr.2020.11.042.‎
[8] Zhu, Junpeng, Yue Yuan, and Weisheng Wang.'An exact microgrid formation model for load restoration in resilient distribution system', International Journal of Electrical Power & Energy Systems, 116: 105568‎,2020. https://doi.org/10.1016/j.ijepes.2019.105568.‎
[9] Bie, Z., Lin, Y., Li, G ,.and Li, F "Battling the Extreme: A Study on the Power System Resilience", Proceedings of the IEEE, 105 (7), pp. 1253-1266.2017. http://doi: 10.1109/JPROC.2017.2679040.‎
[10] M. Panteli, D. N. Trakas, P. Mancarella, and N. D. Hatziargyriou, "Power systems resilience assessment: Hardening and smart operational enhancement strategies", Proceedings of the IEEE, vol. 105, pp. 1202-1213, 2017‎, http://doi: 10.1109/JPROC.2017.2691357.‎
[11] Saberi, Reza, Hamid Falaghi, and Mostafa Esmaeeli. 'Resilience-Based Framework for Distributed Generation Planning in Distribution Networks', ieijqp, 9: 35-49. 2020.
[12] Yang, W., F. Shanshan, W. Bing, H. Jinhui, and W. Xiaoyang. 2018. 'Towards optimal recovery scheduling for dynamic resilience of networked infrastructure', Journal of Systems Engineering and Electronics, 29: 995-1008‎, http://doi:10.21629/JSEE.2018.05.11. ‎
[13] Mancarella, P. “MES (Multi-Energy Systems): An Overview of Concepts and Evaluation Models”; Energy 2014, 65, 1-17.
 [14] Clegg, S.; Mancarella, P. “Integrated Electrical and Gas Network Flexibility Assessment in Low-Carbon Multi-Energy Systems”; IEEE. Trans. Sustain. Energy 2016, 7, 718-731‎,http://doi:10.1109/TSTE.2015.2497329.‎
[15] Li, G.; Zhang, R.; Jiang, T.; Chen, H.; Bai, L.; Li, X. “Security-Constrained Bi-Level Economic Dispatch Model for Integrated Natural Gas and Electricity Systems Considering Wind Power and Power-To-Gas Process”; Appl. Energy 2017, 194, 696-704.
[16] M. Monemi, S. Hassanpour Darban, Unit Commitment Problem with the Aim of Increasing System Resilience, Tabriz Journal of Electrical Engineering, vol. 48, no. 4, 2018.
[17] J. Jannati, D. Nazarpour, Energy Management of Intelligent Parking lot in a Microgrid Considering the Effects of Demand Response Program, Tabriz Journal of Electrical Engineering, vol. 47, no. 2, 2017.
[18] N. Parhizi, M. Marzband, S. M. Mirhosseini Moghaddam, B. Mohammadi Ivatloo and F. Azarinejadian, “The experimental implementation of an energy management system for a grid connected microgrid by using a multiperiod imperialist competition algorithm,” Tabriz Journal of Electrical Eng., vol. 46, no. 1, 2016.
[19] Khodaei, A.. Resiliency-oriented microgrid optimal scheduling. IEEE Transactions on Smart Grid, (2014) 5(4),1584–1591‎, http://doi: 10.1109/TSG.2014.2311465.‎
[20] Gholami, A., Shekari, T., Aminifar, F., & Shahidehpour, M. Microgrid scheduling with uncertainty: The quest forresilience. IEEE Transactions on Smart Grid, 7(6), 2849–2858‎,2016.http://doi:10.1109/TSG.2016.2598802.‎
[21] Pham, T. T. H., Y. Besanger, and N. Hadjsaid. 2009. 'New Challenges in Power System Restoration With Large Scale of Dispersed Generation Insertion', IEEE Transactions on Power Systems, 24: 398-406‎, http://doi: 10.1109/TPWRS.2008.2009477. ‎
[22] Strbac, G., 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, 13: 35-43‎, 2015.http://doi: 10.1109/MPE.2015.2397336.‎
[23] Xu, Y., 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, 9: 426-37‎, 2018.http://doi: 10.1109/TSG.2016.2591531.‎
[24] Wang, Z., 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 .22-2507 :10‎,2019. http://doi: 10.1109/TSG.2018.2803141.‎
[25] Zangeneh, Ali, Shahram Jadid, and Ashkan Rahimi-Kian. 'A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran', Energy Policy, 37: 5752-63. 2009.
[26] Goroohi Sardou, I., Mallahi, A., and Goroohi, A. "Optimal operation of the microgrid in presence of electric vehicles considering demand response", Iranian Electric Industry Journal of Quality and Productivity, 8(1), pp. 13-2‎,2019; http://doi:10.1109/TSG.2016.2594814.‎
[27] J. Zhao, Z. Wang, and J. Wang, “Robust time-varying load modeling for conservation voltage reduction assessment,” IEEE Trans. Smart Grid, to be published.
[28] A. Gholami, T. Shekari, F. Aminifar, and M. Shahidehpour, “Microgrid scheduling with uncertainty: the quest for resilience,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2849 - 2858, Nov. 2016‎, http://doi: 10.1109/TSG.2016.2598802.‎
[29] M. M. A. Abdelaziz, H. E. Farag, and E. F. El-Saadany, “Optimum droop parameter settings of islanded microgrids with renewable energy resources,” IEEE Trans. Sustain. Energy, vol. 5, no. 2, pp. 434–445, Apr. 2014‎, http://doi: 10.1109/TSTE.2013.2293201.‎
[30] M. Panteli, D. N. Trakas, P. Mancarella, and N. D. Hatziargyriou, “Boosting the power grid resilience to extreme weather events using defensive islanding.” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2913 - 2922, Nov. 2016.
[31] A. Shafieezadeh, U. P. Onyewuchi, M. M. Begovic, and R. DesRoches, “Age-dependent fragility models of utility wood poles in power distribution networks against extreme wind hazards,” IEEE Trans. Power Deliv., vol. 29, no. 1, pp. 131–139, Feb. 2014.