Intelligent Decision-making for Proactive Dispatch of Resilient Distribution Networks Under High-temperature Weather

In natural disasters, power infrastructure often suffers damage to varying degrees, making it difficult to ensure the system’s continuous and stable operation. Since the pre-disaster resource reserves directly affect the fault recovery process, mobile resources are utilized to participate in the proactive scheduling and intelligent decision-making of resilient distribution networks, aiming to address the challenge of replenishing emergency resources after disasters. Firstly, the impact of natural disasters on resilient distribution networks is assessed based on meteorological forecast data. Secondly, a two-stage optimization model—involving mobile resource participation in pre-scheduling and real-time scheduling—is established and solved using a stepwise hedging-based intelligent algorithm. Finally, an IEEE 33-bus test system is employed to validate the model. The case study demonstrates that the progressive hedging intelligent algorithm exhibits excellent computational performance within the two-stage optimization framework, enabling rapid solution of the problem. Moreover, the pre-deployed mobile resources demonstrate significant power support effectiveness after disasters, which is conducive to the continuous power supply to critical loads.