Controllable Load Regulation Model of Distribution Network Source Load Energy Storage Based on Machine Learning and Flexible Interconnection Optimization Framework

With the integration of high-penetration renewable energy sources, distribution networks are confronted with increasing challenges such as source-load uncertainty and delayed regulation response. Conventional approaches like PID control struggle to accommodate millisecond-level fluctuations, while existing Model Predictive Control (MPC) approaches suffer from high computational complexity and difficulties in online updates. Relating to these problems, this study proposes a Machine Learning based Flexible Interconnection Optimization (ML-FIO) framework, which achieves breakthroughs through three technological innovations: First, a predictive-control closed-loop coupling integrating Long Short-Term Memory (LSTM) and Hierarchical Deep Q-Network (HDQN) reduces the dynamic response time of Nondominated Sorting Genetic Algorithm II (NSGA-II) to 1.89 seconds (a 60.2% improvement compared to PID control). Second, a flexible interconnection strategy based on dynamic impedance matching reduces the line loss rate to 3.01% during midday peak hours (a 47.6% reduction compared to droop control). Finally, a multi-timescale weight allocation mechanism maintains the root mean square (RMS) voltage deviation at critical nodes within 0.015 pu. Simulation results indicate that the proposed framework maintains robustness in over 80% of mutation scenarios and exhibits convergence in non-convex problems, providing an engineering-ready solution for Source-Load-Storage Coordination (SLSC) in modern power systems and establishing a rigorous mathematical foundation for the application of intelligent algorithms in power systems.