Resilient power systems operation with offshore wind farms and cloud data centers

• Main Authors: Liu, Shengwei, Li, Yuanzheng, Liu, Xuan, Zhao, Tianyang, Wang, Peng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Cloud computing data center; Decomposition
• Online Access: https://hdl.handle.net/10356/173907
• Institution: Nanyang Technological University

To enhance the resilience of power systems with offshore wind farms (OWFs), a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers (CDCs) responding to uncertain spatial and temporal impacts induced by hurricanes. The total life simulation (TLS) is adopted to project the local weather conditions at transmission lines and OWFs, before, during, and after the hurricane. The static power curve of wind turbines (WTs) is used to capture the output of OWFs, and the fragility analysis of transmission-line components is used to formulate the time-varying failure rates of transmission lines. A novel distributionally robust ambiguity set is constructed with a discrete support set, where the impacts of hurricanes are depicted by these supports. To minimize load sheddings and dropping workloads, the spatial and temporal demand response capabilities of CDCs according to task migration and delay tolerance are incorporated into resilient management. The flexibilities of CDC's power consumption are integrated into a two-stage distributionally robust optimization problem with conditional value at risk (CVaR). Based on Lagrange duality, this problem is reformulated into its deterministic counterpart and solved by a novel decomposition method with hybrid cuts, admitting fewer iterations and a faster convergence rate. The effectiveness of the proposed resilient management strategy is verified through case studies conducted on the modified IEEE-RTS 24 system, which includes 4 data centers and 5 offshore wind farms.