Transfer-Reinforcement-Learning-Based rescheduling of differential power grids considering security constraints

The power system rescheduling based on model-free methods has obvious defects in practical application, such as poor scenario transferability, long data training time, and waste of domain knowledge. To overcome the above defects, a transfer-reinforcement-learning-based rescheduling method of differential power grids considering security constraints is proposed. When constructing the Markov decision-making process of security-constrained rescheduling, both the off-limits of line power and node voltage are considered in the reward. The action space of deep reinforcement learning is narrowed by calculating the sensitivities of devices and mapped to control the active and reactive power regulating devices to reschedule active and reactive power simultaneously. According to the change degree of transfer object, the applications of transfer learning are divided into two scenarios. For the security-constrained rescheduling transfer scenario of different structures of the same power grid, a domainadaption transfer learning method is formed, realizing good data adaptability after structure changes with the original model. Moreover, a policy-based transfer learning method is constructed for the security-constrained rescheduling transfer scenario of different power grids, enhancing the training speed and training effect of target power grid. Two standard systems and two actual power grids are utilized to verify the effectiveness of the method. For the actual power grids, the effects of the two scenarios are improved by 5.8% and 3.9% with transfer learning. Compared with other methods, this method not only has obvious advantages in transferability, but also has a shorter learning process and lower control cost.

Automated summary

A transfer-reinforcement-learning-based rescheduling method for differential power grids considering security constraints was proposed to address practical defects, achieve better transfer learning effects, and tackle security issues in different scenarios. It narrowed the action space of deep reinforcement learning, applied domain-adaption transfer learning for different structural changes within the same power grid and policy-based transfer learning for different power grids, showing improved effectiveness and lower control costs compared to other methods.