An unsolvable power flow adjustment method for weak power grid based on transmission channel positioning and deep reinforcement learning

To solve the problem of human resources and time consumption caused by unsolvable power flow of weak power grid, an adjustment method for unsolvable power flow based on weak transmission channels and deep reinforcement learning (DRL) is proposed. Weak horizontal and vertical channels are defined, and their relationship with unsolvable power flow is demonstrated. Then, based on the idea of reducing the load level first and then gradually recovering it, actionable device and action margin are determined based on transmission channel positioning, adjustment sensitivity and solvability degree, so as to form a power flow adjustment strategy. Next, the Markov decision-making process of power flow adjustment is constructed, the prior probability of action is given by adjustment sensitivity of the actionable device, and the action is mapped to the adjustment amounts of generators and capacitors/reactors. Based on soft actor-critic, a DRL model suitable for power flow adjustment is established. Finally, the simulation results show that the proposed method performs well both in the New England 39-bus standard system and an actual power grid of China. Compared with the results obtained by other methods, the superiority of the proposed method is verified.