Emergency load-shedding optimization control method based on reinforcement learning assistance

At present, on-line pre-decision and real-time matching control forms are the most widely used emergency control forms in the power system. The safety control system predicts the accidents according to the current operation state of the power grid and sets the emergency control strategy for the predicted accidents to ensure the safety and stability of the power grid when the fault occurs. However, the voltage level controlled by the safety control system is relatively high, which leads to a large granularity of load control. Because load nodes are mainly mixed users of various types, the strategy under large-scale centralized control may include important load nodes. It is necessary to replace the important load with other controllable low voltage nodes in the area to ensure that the load-shedding quantity set by the safety control system can be implemented sufficiently. Most emergency control optimization problems regard the load-shedding cost as a definite and constant value, but in fact, the load-shedding cost will change with the environmental factors. Based on the analysis of the influence of environmental factors on the cost of load shedding, this paper uses the reinforcement learning method to learn historical operation data and adapt to the variability of load-shedding cost to decide the best alternatives. The results of an example show that the proposed method can make replacement decisions in any scene based on adaptive environmental variability and can effectively prevent over-cutting through the design of a precise final reward.