Design of Reinforcement Learning Agent System for Cross-Sectional Control of Power System

The cross-sectional control of the power system is the core link to ensure cross regional power balance and safe and stable operation. The high proportion of new energy access brings about output uncertainty, multi section coupling effects, and real-time limitations of traditional control methods, posing severe challenges to the existing regulation system. This article proposes a cross-section control agent system based on multiagent deep reinforcement learning. Through a layered collaborative architecture design, improved deep deterministic policy gradient (DDPG) algorithm, and integration of source network load storage resources, it achieves precise power control and economic optimization in complex scenarios. Improve the algorithm by introducing a high reward experience pool and stock judgment mechanism, combined with a convex relaxation power flow calculation module, to enhance training efficiency and policy generalization ability; The refined time series simulation model quantifies the dynamic impact of new energy fluctuations and demand side responses, achieving multi section collaborative optimization. IEEE 118 node system verification shows that the system stabilizes the cross-sectional power control error within $\pm 0.8 \%$ per unit value, shortens the adjustment time to 2.1 seconds, reduces the adjustment cost by 31.3 % compared to traditional methods, and increases the new energy consumption rate to 96.5 %. The research results show that the multi-agent collaborative mechanism effectively responds to the dynamic changes and uncertainties of the power grid, providing data-driven intelligent solutions for the safety control of new power system sections. Its hierarchical control framework and resource collaboration strategy have reference value for the expansion of multi energy interconnection systems.