Distribution system resilience enhancement by microgrid formation considering distributed energy resources

• Published in: Energy (Oxford) 2020-01, Vol.191, p.116442, Article 116442
• Main Authors: Gilani, Mohammad Amin, Kazemi, Ahad, Ghasemi, Mostafa
• Format: Article
• Language: English
• Subjects: Constraint modelling; Distributed energy resources; Distributed generation; Distribution system resilience; Electric power distribution; Electric power grids; Emergency operation budgets; Emergency procedures; Energy management; Energy resources; Energy sources; Energy storage; Microgrids; Mixed-integer linear program; Natural disasters; Networks; Renewable energy; Resilience; Smart grid; Smart grid technology; Storage units; Topology; Turbines; Wind power; Wind turbines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.116442

Due to increasing in natural disasters in the recent years, the issue of distribution network resilience has become highly important. Microgrids with different types of distributed energy resources have the capabilities to improve distribution network resilience under extreme events. In this paper, we present a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints. In the presented model, we study dynamic microgrid formation and optimal management of various smart grid technologies such as distributed generations, demand response programs, wind turbines and energy storage units. We also investigate the significant impact of renewable energy resources, loads and their uncertainty on distribution system resilience. In addition, we determine the required emergency budgets of operation to restore a distribution system from extreme events. We also intend to examine the effects of demand response programs on improving the distribution system performance in the recovery period. Finally, we verify the effectiveness of the presented model on a modified IEEE 33-node test system and a real distribution system. •A linear approach is presented to enhance the resilience of distribution system.•A resilient model is proposed to form dynamic microgrids.•A stochastic programming is used to model the uncertainty of renewable energy.