TY  - EJOU
AU  - Zhang, Hong 
AU  - Xu, Bin 
AU  - Li, Jinzhong 
AU  - Meng, Xiaoxiao 
AU  - Qian, Cheng 
AU  - Ma, Wei 
AU  - Xie, Yuguang 

TI  - Distributed Iterative Learning Control for Load Balancing in Flexible AC/DC Hybrid Distribution Systems
T2  - Energy Engineering

PY  - 2026
VL  - 123
IS  - 5
SN  - 1546-0118

AB  - The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network. The traditional load balancing methods are mainly based on network reconfiguration, which have problems such as a long time scale and poor adaptability. In response to these issues, this paper proposes a distributed iterative learning control (ILC) strategy for load balancing in flexible AC/DC hybrid distribution systems. This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model. It is only necessary to measure the load rate of adjacent units without observing the overall system status, which greatly reduces complexity and enhances robustness. In this paper, a new energy photovoltaic and energy storage integrated system was built through MATLAB/Simulink simulation, and the effectiveness of the proposed strategy under normal working conditions and port faults was verified through this system. Through comparative studies with event-triggered control and traditional consensus algorithms, as well as real-time simulations on the RT-LAB simulation platform, it has been confirmed that this method has superior performance in terms of convergence speed, steady-state accuracy, and dynamic response, and has the potential to be applied in practical models. It is suitable for application in medium and low voltage distribution systems with new energy access.
KW  - AC/DC hybrid distribution systems; iterative learning algorithm; load rate

DO  - 10.32604/ee.2025.073542