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Resilience Enhancement of Multi-microgrid System of Systems Based on Distributed Energy Scheduling and Network Reconfiguration

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Abstract

With the continuous development of MMG (Multi-Microgrid) technology, the coordinated operation among microgrids is of a positive significance to improve the power system resilience. SoS (System of Systems) is considered as an effective approach to study the resource scheduling problem of MMG systems with complex interaction behaviors. In this context, this paper establishes a hierarchical SoS architecture suitable for MMG systems and proposes a RO-OMS (Robust Optimization Outage Management Strategy). In the pre-disturbance prevention phase, a robust optimization method is used to model the microgrid, and distributed energy resources are rationally dispatched and corrected to prepare for the next phase. In the post-disturbance recovery phase, microgrids are dynamically dispatched through grid reconfiguration to ensure power to critical loads while minimizing load shedding. Based on this, resilience metrics are defined to quantitatively analyze the resilience of MMG systems. Finally, the proposed model is tested under different scenarios and damage levels in a modified IEEE 33-node test system to verify the effectiveness of the proposed model.

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References

  1. Presekal A, Ştefanov A, Rajkumar VS, Palensky P (2023) Attack graph model for cyber-physical power systems using hybrid deep learning. IEEE Trans Smart Grid 14(5):4007–4020. https://doi.org/10.1109/TSG.2023.3237011

    Article  Google Scholar 

  2. Panteli M, Mancarella P, Trakas DN, Kyriakides E, Hatziargyriou ND (2017) Metrics and quantification of operational and infrastructure resilience in power systems. IEEE Trans Power Syst 32(6):4732–4742. https://doi.org/10.1109/tpwrs.2017.2664141

    Article  Google Scholar 

  3. Ussain A, Bui VH, Kim HM (2019) Microgridsas a resilience resource and strategies used by microgrids for enhancing resilience. Appl Energy 240(12):56–72. https://doi.org/10.1016/j.apenergy.2019.02.055

    Article  Google Scholar 

  4. Yao YY, Liu WJ, Jain R, Chowdhury B (2022) Quantitative metrics for grid resilience evaluation and optimization. IEEE Trans Sustain Energy 14(2):1244–1258. https://doi.org/10.1109/TSTE.2022.3230019

    Article  Google Scholar 

  5. Raoufi H, Vahidinasab V, Mehran K (2020) Power systems resilience metrics: a comprehensive review of challenges and outlook. Sustainability 12(22):9698–9705. https://doi.org/10.3390/su12229698

    Article  Google Scholar 

  6. Puspendu G, Mala D (2022) Probabilistic quantification of distribution system resilience for an extreme event. Int Trans Electr Energy Syst. https://doi.org/10.1155/2022/3838695

    Article  Google Scholar 

  7. Arian K, Amirali S (2023) Three-stage resilience-oriented active distribution systems operation after naturaldisasters. Energy. https://doi.org/10.1016/j.energy.2023.128360

    Article  Google Scholar 

  8. Mirioun MH, Aminifar F, Lesani H (2021) Resilience-oriented proactive management of microgrids against windstorms. IEEE Trans Power Syst 33(4):4275–4284. https://doi.org/10.1109/TPWRS.2017.2765600

    Article  Google Scholar 

  9. Shi Q, Li F, Kuruganti T, Olama MM, Dong J, Wang X, Winstead C (2021) Resilience-oriented DG siting and sizing considering stochastic scenario reduction. IEEE Trans Power Syst 36(4):3715–3727. https://doi.org/10.1109/TPWRS.2020.3043874

    Article  Google Scholar 

  10. Bullich-Massague E, Diaz-Gonzalez F, Aragues-Penalba M (2018) Microgrid clustering architectures. Appl Energy 212(5):340–361. https://doi.org/10.1016/j.apenergy.2017.12.048

    Article  Google Scholar 

  11. Zhao B, Qin RW (2020) SoS architecting and operational coordination of microgrid cluster for optimal energy management. Proc CSEE 40(6):1886–1896

    Google Scholar 

  12. Kargarian A, Fu Y (2014) System of systems based security-constrained unit commitment incorporating active distribution grids. IEEE Trans Power Syst 29(5):2489–2498. https://doi.org/10.1109/TPWRS.2014.2307863

    Article  Google Scholar 

  13. Marvasti AK, Fu Y, DorMohammadi S, Rais-Rohani M (2014) Optimal operation of active distribution grids: a system of systems framework. IEEE Trans Smart Grid 5(3):1228–1237. https://doi.org/10.1109/TSG.2013.2282867

    Article  Google Scholar 

  14. Padhi S, Panigrahi BP, Dash D (2020) Solving dynamic economic emission dispatch problem with uncertainty of wind and load using whale optimization algorithm. J Instit Eng India Ser B 101:65–78. https://doi.org/10.1007/s40031-020-00435-y

    Article  Google Scholar 

  15. Muruganantham B, Gnanadass R (2021) Solar integrated time series load flow analysis for practical distribution system. J Instit Eng India Ser B 102:829–841. https://doi.org/10.1007/s40031-021-00561-1

    Article  Google Scholar 

  16. Singh U, Rizwan M (2023) Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system. J Ambient Intell Humaniz Comput 14:8035–8044. https://doi.org/10.1007/s12652-022-03878-x

    Article  Google Scholar 

  17. Sengar S, Liu X (2020) Ensemble approach for short term load forecasting in wind energy system using hybrid algorithm. J Ambient Intell Humaniz Comput 11:5297–5314. https://doi.org/10.1007/s12652-020-01866-7

    Article  Google Scholar 

  18. Dong X, Deng S, Wang D (2022) A short-term power load forecasting method based on k-means and SVM. J Ambient Intell Humaniz Comput 13:5253–5267. https://doi.org/10.1007/s12652-021-03444-x

    Article  Google Scholar 

  19. Peng C, Hou Y, Yu N, Wang W (2017) Risk-limiting unit commitment in smart grid with intelligent periphery. IEEE Trans Power Syst 32(6):4696–4707. https://doi.org/10.1109/TPWRS.2017.2672939

    Article  Google Scholar 

  20. Peng C, Hou Y, Yu N, Yan J, Lei S, Wang W (2017) Multi-period risklimiting dispatch in power systems with renewables integration. IEEE Trans Industr Inf 13(4):1843–1854. https://doi.org/10.1109/TII.2017.2651146

    Article  Google Scholar 

  21. Wu C, Hug G, Kar S (2017) Risk-limiting economic dispatch for electricity markets with flexible ramping products. IEEE Trans Power Syst 31(3):1990–2003. https://doi.org/10.1109/PESGM.2017.8273771

    Article  Google Scholar 

  22. Shao ZT, Cao XY, Zhai QZ, Guan XH (2023) Risk-constrained planning of rural-area hydrogen-based microgrid considering multiscale and multi-energy storage systems. Appl Energy. https://doi.org/10.1016/j.apenergy.2023.120682

    Article  Google Scholar 

  23. Shen Y, Wang G, Zhu J (2023) Resilience improvement model of distribution network based on two-stage robust optimization. Electric Power Syst Res. https://doi.org/10.1016/j.epsr.2023.109559

    Article  Google Scholar 

  24. Wang C, Pang K, Shahidehpour M, Wen F, Duan S (2023) Two-stage robust design of resilient active distribution networks considering random tie line outages and outage propagation. IEEE Trans Smart Grid 14(4):1949–3053. https://doi.org/10.1109/TSG.2022.3224605

    Article  Google Scholar 

  25. Mahsa E, Maryam M, Mohammad H, Seyed H (2023) Microgrids and distribution system resilience assessment:amulti-objective robust-stochastic optimization approach considering social behavior using a deep learning modeling. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2023.104686

    Article  Google Scholar 

  26. Rosales-Asensio E, Icaza D, Gonzalez-Cobos N (2023) Peak load reduction and resilience benefits through optimized dispatch, heating and cooling strategies in buildings with critical microgrids. J Build Eng 68(2):345–350. https://doi.org/10.1016/j.jobe.2023.106096

    Article  Google Scholar 

  27. Cai S, Zhang ML, Xie YY, Wu QW, Jin XL, Xiang ZR (2022) Hybrid stochastic-robust service restoration for wind power penetrated distribution systems considering subsequent random contingencies. IEEE Trans Smart Grid 13(4):2859–2872. https://doi.org/10.1109/TSG.2022.3161801

    Article  Google Scholar 

  28. Saeed NJ, Ehsan HJ, Mehdi E (2022) Optimal resilience-oriented microgrid formation considering failure probability of distribution feeders. Electric Power Sys Res 52(3):1745–1755. https://doi.org/10.1016/j.epsr.2022.108012

    Article  Google Scholar 

  29. Azizi AA, Shamim AG, Mosayebian ME (2023) Robust island-mode operation of power distribution network using game theory for resilience enhancement. Sustain Energy Grids Netw. https://doi.org/10.1016/j.segan.2022.100978

    Article  Google Scholar 

  30. Li ZP, Tang WH, Lian XL (2022) Resilience-oriented two-stage recovery method for power distribution system considering transportation network. Int J Electr Power Energy Syst 23(11):1031–1042. https://doi.org/10.1016/j.ijepes.2022.107497

    Article  Google Scholar 

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  1. College of Electrical Engineering, Shanghai Dianji University, Shanghai, China

    Haihong Qin & Tianyu Liu

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Qin, H., Liu, T. Resilience Enhancement of Multi-microgrid System of Systems Based on Distributed Energy Scheduling and Network Reconfiguration. J. Electr. Eng. Technol. 19, 2135–2147 (2024). https://doi.org/10.1007/s42835-023-01724-4

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