Global climate deterioration caused by excessive carbon emissions poses a severe threat to the stable operation of power systems. In extreme snow and ice weather, complex faults, such as ice-covered line breakage and insulator flashover, may be triggered. Hybrid simulations to reveal the influence mechanism and dynamic interaction process between extreme weather and power systems have become a research focus. Relationship models between meteorological conditions and grid failure were constructed in ice accretion and insulator flashover scenarios. Based on the above two models, a multi-variable and multi-time scale hybrid simulation of the meteorological process and power system, considering line icing and insulator flashover, was realized. An example was provided to verify the feasibility of the proposed simulation scheme.

With the increasing occurrence of extreme weather events, the short circuit and line-breaking faults in transmission lines caused by line galloping have been threatening the security operation of power systems. These faults are also hard to be simulated with current simulation tools. A numerical simulation approach of power systems is presented to simulate the clustered, cascading faults of long-timescale caused by line-galloping events. A simulation framework is constructed in which large numbers of fault scenarios are simulated to reflect the randomness of line galloping. The interaction mechanism between power system operation states and line galloping processes is revealed and simulated by the solution of differences of timescales and parameters. Based on Power System Simulator/Engineering (PSS/E), an extended software package for line galloping simulation is developed with Python, which extends the functionalities of the PSS/E in power system simulation. An example is given to demonstrate the feasibility of the proposed simulation method.

As the proportion of building energy consumption in total energy consumption continues to rise, traditional energy scheduling strategies and building load regulation methods are improved to reduce energy consumption and enhance the flexibility of building scheduling. In this study, a two‐stage optimization strategy for energy‐efficient buildings incorporating electric vehicles (EVs) based on user satisfaction is proposed. First, a source–load–storage coordinated energy optimization system for buildings, including photovoltaic (PV) generation, energy storage systems (ESSs), EVs, light‐emitting diode (LED) lights, and heating, ventilation, and air conditioning (HVAC), is established. Second, the satisfaction levels of users with multiple flexible loads are used as indicators of comfort to dynamically adjust energy consumption in buildings. Then, a multiobjective energy optimization model is formulated to minimize daily operational costs while simultaneously maximizing user satisfaction, with an emphasis on balancing comfort and economic efficiency. Third, a two‐stage energy optimization model of day‐ahead and intraday is constructed to reduce the impact of source–load forecasting errors on the operation of building energy systems, and an incentive demand response strategy is introduced to guide users to participate in scheduling in the intraday stage. Finally, different cases are created to test the effectiveness of the proposed strategy. The overall simulation results validate the proposed approach with operational cost reduction of 12.9% while maintaining a user satisfaction level above 0.95 and grid volatility reduction of 7.56% as compared to the traditional energy optimization strategy.