This comprehensive review of uncertainties in power systems covers modeling, impact, and mitigation strategies. It highlights the significant challenges posed by various sources of uncertainty, such as extreme weather events, cyber-attacks, and asset management, and their implications for grid reliability, stability, and economic efficiency. The review emphasizes the unique vulnerabilities of Australia's power network due to its susceptibility to wildfires and heavy rainfall, as well as the economic challenges of supplying power to remote areas. The evolution of modern power grids, driven by advanced metering infrastructure (AMI), has introduced new challenges, including the risk of cyber-attacks via communication links. The paper explores conventional robust control methods and modern probabilistic and data-driven approaches for modeling and correlating uncertainty events with grid state to optimize decision-making. It also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary services (D-FCAS) to ensure uncertainty-tolerant power systems. The trade-offs between reliability, computational speed, and economic efficiency are discussed, along with the influence of these strategies on future power grid planning and operation. The review categorizes uncertainties into three main categories: those affecting power generation, network assets, and communication links. It provides a detailed analysis of the impacts of renewable energy integration, weather-related events, and cyber-attacks on power systems. For each category, the paper discusses existing modeling approaches and mitigation strategies, including the use of advanced forecasting methods, machine learning models, and control techniques. Finally, the paper outlines common mitigation approaches for each type of uncertainty, such as emergency power imports, ESS management, real-time network reconfiguration, and preventive maintenance plans. It emphasizes the importance of integrating uncertainty into public energy research and development decisions and highlights the potential of digitalization and AI in improving forecasting, grid management, and demand-side response.This comprehensive review of uncertainties in power systems covers modeling, impact, and mitigation strategies. It highlights the significant challenges posed by various sources of uncertainty, such as extreme weather events, cyber-attacks, and asset management, and their implications for grid reliability, stability, and economic efficiency. The review emphasizes the unique vulnerabilities of Australia's power network due to its susceptibility to wildfires and heavy rainfall, as well as the economic challenges of supplying power to remote areas. The evolution of modern power grids, driven by advanced metering infrastructure (AMI), has introduced new challenges, including the risk of cyber-attacks via communication links. The paper explores conventional robust control methods and modern probabilistic and data-driven approaches for modeling and correlating uncertainty events with grid state to optimize decision-making. It also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary services (D-FCAS) to ensure uncertainty-tolerant power systems. The trade-offs between reliability, computational speed, and economic efficiency are discussed, along with the influence of these strategies on future power grid planning and operation. The review categorizes uncertainties into three main categories: those affecting power generation, network assets, and communication links. It provides a detailed analysis of the impacts of renewable energy integration, weather-related events, and cyber-attacks on power systems. For each category, the paper discusses existing modeling approaches and mitigation strategies, including the use of advanced forecasting methods, machine learning models, and control techniques. Finally, the paper outlines common mitigation approaches for each type of uncertainty, such as emergency power imports, ESS management, real-time network reconfiguration, and preventive maintenance plans. It emphasizes the importance of integrating uncertainty into public energy research and development decisions and highlights the potential of digitalization and AI in improving forecasting, grid management, and demand-side response.