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A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources

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  • Mousavizadeh, Saeed
  • Haghifam, Mahmoud-Reza
  • Shariatkhah, Mohammad-Hossein

Abstract

Due to the unique structure and special characteristics of electric distribution networks, along with the increase in the number and severity of the natural disasters in recent years, presenting a proper framework and procedure for evaluating the resiliency of these systems is more essential. In addition, it is obligatory to model the different capabilities and features of the smart grids components in order to provide a better knowledge about their expected performance in such circumstances. In response to these challenges, this paper addresses the concept of resiliency and its dimensions in distribution networks. A new model based on mixed-integer linear programming is proposed to properly model and evaluate the resiliency of smart distribution systems. In the proposed model, optimal formation of dynamic microgrids (MGs), their service areas, and the optimal management of different technologies such as energy storage (ES) units, demand side management programs and distributed generations (DGs) units are investigated. In addition, employing a two-stage framework based on stochastic programming, the impact of increasing penetration level of the renewable energy resources and their related uncertainties on system resiliency is examined. The efficiency and applicability of the proposed integrated model is verified by performing multiple simulations on the modified 118-bus test system and a real distribution network.

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  • Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:443-460
    DOI: 10.1016/j.apenergy.2017.11.067
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    11. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
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    13. Liao, Shiwu & Yao, Wei & Han, Xingning & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & He, Haibo, 2019. "An improved two-stage optimization for network and load recovery during power system restoration," Applied Energy, Elsevier, vol. 249(C), pages 265-275.
    14. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    15. Nikoobakht, Ahmad & Aghaei, Jamshid, 2022. "Resilience promotion of active distribution grids under high penetration of renewables using flexible controllers," Energy, Elsevier, vol. 257(C).
    16. Moslehi, Salim & Reddy, T. Agami, 2018. "Sustainability of integrated energy systems: A performance-based resilience assessment methodology," Applied Energy, Elsevier, vol. 228(C), pages 487-498.
    17. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    18. Aldarajee, Ammar H.M. & Hosseinian, Seyed H. & Vahidi, Behrooz, 2020. "A secure tri-level planner-disaster-risk-averse replanner model for enhancing the resilience of energy systems," Energy, Elsevier, vol. 204(C).
    19. Zhou, Yutian & Panteli, Mathaios & Moreno, Rodrigo & Mancarella, Pierluigi, 2018. "System-level assessment of reliability and resilience provision from microgrids," Applied Energy, Elsevier, vol. 230(C), pages 374-392.
    20. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    21. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    22. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
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    24. Gilani, Mohammad Amin & Kazemi, Ahad & Ghasemi, Mostafa, 2020. "Distribution system resilience enhancement by microgrid formation considering distributed energy resources," Energy, Elsevier, vol. 191(C).

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