Open Access

ARTICLE

Distributed Iterative Learning Control for Load Balancing in Flexible AC/DC Hybrid Distribution Systems

Hong Zhang¹, Bin Xu¹, Jinzhong Li¹, Xiaoxiao Meng^2,*, Cheng Qian², Wei Ma¹, Yuguang Xie¹
1 State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei, 230601, China
2 Anhui Provincial Key Laboratory of Renewable Energy Utilization and Energy Saving, Hefei University of Technology, Hefei, 230009, China
* Corresponding Author: Xiaoxiao Meng. Email:
(This article belongs to the Special Issue: Operation and Control of Grid-connected New Energy and Emerging Loads)

Energy Engineering https://doi.org/10.32604/ee.2025.073542

Received 20 September 2025; Accepted 03 December 2025; Published online 25 December 2025

Abstract

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.

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Keywords

AC/DC hybrid distribution systems; iterative learning algorithm; load rate

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