## 1 Introduction  
With the advancement of DC microgrid technology, hybrid energy storage systems (HESSs) have also seen significant development in recent years. However, due to the diverse types of HESSs, extensive research has been conducted to achieve rapid energy conversion (storage and release) within microgrids (such as ship microgrids and wind power microgrids) under load disturbances and system uncertainties [1]. The control issues of microgrids based on HESSs include the distribution of unbalanced power within the system, the stability of the DC bus voltage and the coordinated control strategy of DC converters. Among these, the distribution of unbalanced power is the core of the control system [2]. Only by distributing unbalanced power in a timely manner can energy be released promptly when the system requires it, ensuring stable operation of the microgrid. However, as the role of renewable energy in microgrids becomes increasingly prominent, the volatility of energy output and the uncertainty of load demand changes cause unbalanced power fluctuations, posing new challenges to bus voltage tracking. A decline in bus voltage stability will directly increase the risk of equipment damage, operating costs and line losses, imposing higher demands on system scheduling and operational management [3; 4; 5; 6; 7; 8]. To achieve stable operation of the bus voltage, an important step is to address the issue of bus voltage resistance to load disturbances [9; 10; 11; 12]. Therefore, this paper discusses and proposes solutions to bus voltage resistance to load disturbances. Extensive research has been conducted worldwide to address the challenge of bus voltage anti-load disturbance caused by unbalanced power fluctuations. The primary control methods can be categorized into two main types: model-based methods and model-free methods. Among the model-based methods, Model Predictive Control (MPC) is a prominent representative [13; 14; 15]. The advantage of this method is that it can predict the bus voltage input of the hybrid energy storage system based on the operating state of the microgrid under disturbances, thereby achieving timely tracking. However, precise modeling is challenging due to pulse interference in the microgrid and the dynamic characteristics of batteries and supercapacitors in the hybrid energy storage system. Consequently, model-free control methods have garnered more attention. Robust control is a typical method that maintains system stability under uncertainties such as system parameter variations, external disturbances and modeling errors [16]. It can achieve bus voltage anti-load disturbance characteristics and adjust control parameters in response to load pulses. However, this control method requires a deep understanding of the dynamic characteristics and uncertainties of the hybrid energy storage system, involves high design complexity and is mostly applicable to relatively small disturbances. In addition to not relying on the precise model of the controlled object, intelligent control also has the advantages of strong adaptability, strong ability to handle complex systems, good real-time performance, strong anti-interference ability, not being affected by system dynamic changes and easy implementation of autonomous control. For example, fuzzy control has also achieved research results in bus voltage control. However, it still has issues such as performance being highly dependent on rule design, difficulty in knowledge acquisition and adjustment, high computational complexity and poor interpretability [17]. PID is one of the industry's most widely used control methods, and it does not rely on the precise model of the controlled object. It can promptly eliminate the error between the control target and the actual behavior. However, it has been proven to have drawbacks, such as the side effects of integral feedback and the \"weighted sum\" strategy not being necessarily optimal [18]. Despite these shortcomings, PID remains a highly regarded method.