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.

Microgrids (MGs) are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, as well as a more resilient and economical on/off-grid control, operation, and energy management. However, MGs, as newcomers to the utility grid, are also facing challenges due to economic deregulation of energy systems, restructuring of generation, and market-based operation. This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques. First, MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management. Second, energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management. Mathematical programming, adaptive dynamic programming, and deep reinforcement learning-based solution methods are investigated accordingly, together with their implementation schemes. Finally, problems for future energy management systems with dynamics-captured critical component models, stability constraints, resilience awareness, market operation, and emerging computational techniques are discussed.