Data‐model driven rescheduling considering both rotor angle stability and transient voltage stability constraints

Abstract

Facing the problem of workforce and time costs caused by the rescheduling of large-scale power grids with renewable energy penetration, and the lack of parallel control research on rotor angle instability and transient voltage instability, a rescheduling method based on trajectory sensitivity and deep reinforcement learning (DRL) is proposed to meet the requirements of rotor angle stability and transient voltage stability at the same time. By introducing the process of rescheduling to meet the transient stability constraint, the Markov decision-making process of rescheduling is first constructed. Then, a simple dominant instability type identification method is proposed according to the occurrence time, location, voltage pattern, and position of the oscillation centre. Moreover, a rescheduling strategy is formulated based on the dominant instability mode identification, trajectory sensitivity calculation, actionable device selection, action amount computation, and action of the device. Next, according to the improved distributed distributive deep deterministic policy gradients (D4PG), a DRL model is established to map the action to actionable generator pairs and capacitors/reactors, so as to realize parallel rescheduling of rotor angle instability and transient voltage instability. Finally, the improved New England 39-bus system and an actual power grid are used to verify the effectiveness and advantages of the method.