Real-Time Power System Optimization Under Typhoon Weather Using the Smart “Predict, Then Optimize” Framework

With the increase of extreme weather events caused by global climate change, the issue of power system resilience has become more and more important. Traditional power system management methods cannot cope with dynamic real-time changes, and it is difficult to effectively predict and respond to potential failures caused by extreme weather. To this end, this paper proposes a real-time power system optimization method based on the Smart “Predict, then Optimize” (SPO) framework. The SPO method first uses the Transformer model to predict, in real time, the future line damage states and then dynamically adjusts the optimization strategy based on the prediction results. This method can efficaciously enhance the prediction accuracy of faulty lines under extreme weather conditions and optimize generation scheduling, load management, as well as EV battery scheduling to minimize the system cost. This study proposes a solution based on the SPO loss function, artificial intelligence prediction model, and bi-level optimization model to address the dynamic optimization of power systems under extreme conditions, significantly enhancing the system’s response to extreme weather events. The experimental results demonstrate that the SPO method can optimize system operation in real time, significantly reducing load shedding and total system cost during typhoon weather, which not only improves the system’s economic efficiency but also effectively enhances power system resilience.