Resiliency/Cost-Based Optimal Design of Distribution Network to Maintain Power System Stability Against Physical Attacks: A Practical Study Case

• Main Authors: Mohammad Ghiasi, Moslem Dehghani, Taher Niknam, Hamid Reza Baghaee, Sanjeevikumar Padmanaban, Gevork B. Gharehpetian, Hamdulah Aliev
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Power distribution network; grey wolf optimization; high-impact low-probability event; optimal design; resilience
• Online Access: https://ieeexplore.ieee.org/document/9380135/

Unexpected natural disasters or physical attacks can have various consequences, including extensive and prolonged blackouts on power systems. Energy systems should be resistant to unwanted events, and their performance is not easily affected by such conditions. The power system should also have sufficient flexibility to adapt to severe disturbances without losing its full version; it should restore itself immediately after resolving the disturbance. This critical feature of the behavior of infrastructure systems in power grids is called resilience. In this paper, the concepts related to resilience in the power system against severe disturbance are explained. The resilience and evaluation process components are introduced; then, an optimal design of resilient substations in the Noorabad city distribution grid against physical attack is presented. This research proposes an optimal solution for simultaneously allocating the feeder routing issue and substation facilities and finding the models of installed conductors and economic hardening of power lines due to unexpected physical attacks on vital urban operational infrastructure. The values of distribution networks are calculated using the grey wolf optimization (GWO) algorithm to solve the problem of designing an optimal distribution network scheme (ODNS) and optimal resilient distribution network scheme (ORDNS). Obtained results confirm the effectiveness of the proposed resiliency-cost-based optimization approach.