論文ID: 2026EAP1109
With the increasing complexity of modern power systems and the growing variability of electricity demand, real-time load regulation has become a critical challenge. In particular, regional power networks with heterogeneous load characteristics require efficient and adaptive control strategies. Although existing studies have explored load grouping based on consumption patterns to improve grid management, most approaches rely on static grouping schemes and lack the capability for real-time hierarchical regulation. To address these limitations, this paper proposes a Greedy-Based Hierarchical Hopfield Neural Network (GBH-HNN) for adaptive load grouping and real-time regulation. In the proposed framework, controllable loads are first organized into priority-based groups, while a hierarchical control structure is introduced to reflect different operational levels of the power system. The GBH-HNN then employs a greedy neuron update mechanism, which selectively adjusts the most critical load groups at each iteration, thereby achieving efficient and lightweight real-time regulation. The effectiveness of the proposed method is evaluated using publicly available household electricity consumption data under Hubei-oriented operational scenarios. Experimental results demonstrate that the proposed approach reduces the maximum load imbalance from 28.70 to 15.89 and the steady-state error from 4.64 to 2.46. In addition, it achieves faster convergence and a regulation accuracy of 84.48%, indicating its superiority in both efficiency and accuracy for real-time load regulation.
