Against the backdrop of global climate change, extreme weather events are increasingly challenging the safe and stable operation of power distribution networks. These events can cause sudden load fluctuations, equipment failures, and disruptions in power transfer. To address these, this paper proposes an optimal control strategy for distribution network power transfer, integrating Long Short-Term Memory (LSTM) networks and dynamic optimization models. By fusing meteorological data with grid characteristics, the LSTM model predicts load demand and fault probability, capturing complex system behaviors under extreme conditions. Combined with Mixed-Integer Linear Programming (MILP), a decision-making model is developed, and a deep-reinforcement-learning-based algorithm handles uncertainties in weather, load, and equipment faults, enabling accurate control. Validation on a 33-bus system shows the method enhances reliability under extreme weather, providing practical value. Furthermore, typhoons, as extreme weather events, can severely damage infrastructure, disrupt power lines, and affect grid stability. In the 33-bus system, typhoons can cause tower collapses and line failures, impacting power transfer. This paper explores the impact of typhoons on a bus model integrated with renewable energy, proposing optimal control strategies to ensure power supply to critical loads while minimizing equipment damage.
Su et al. (Mon,) studied this question.