Research on Computer-Aided Multi-Type Resource Regulation Strategy for Key Sections of Power Grid

The collaborative regulation of multiple types of resources in key sections of smart grids faces challenges such as insufficient data dynamic perception, multi-objective optimization conflicts, and weak uncertainty robustness. In view of this, this research proposes a computer-assisted coordinated regulation strategy for multiple types of resources, builds an integrated framework of “data perception - intelligent decisionmaking - execution optimization”, collects real-time cross section power flow, new energy output and other multi-source data through edge computing nodes, combines Kalman filtering and feature engineering to achieve data fusion, and uses a hybrid algorithm of reinforcement learning and three-layer robust optimization model to achieve power balance and efficient utilization of resources in key cross sections. Verification in the improved IEEE 33 node system shows that the proposed strategy can control the critical section power deviation within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 2 \%$</tex>, increase renewable energy consumption rate by 18%, reduce regulation delay to 150 ms, and reduce load loss by 35% compared to traditional methods in extreme fluctuation scenarios. The research results have constructed a layered control architecture for edge cloud collaboration, breaking through the technical bottlenecks of traditional methods in real-time and robustness, providing theoretical and technical support for multi resource collaborative control of key sections in new power systems, and having important engineering significance for improving power grid stability and resource utilization.