The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

A hydrogen-based microgrid (MG) is an energy system that uses hydrogen as a primary energy carrier within a localized grid. Numerous alternative approaches and concepts are found concerning the management of renewable energy systems. This study proposes a novel approach to assess the energy management system (EMS) and optimal hydrogen-based Energy Storage Systems (HBESS) at minimal total cost, employing particle swarm optimization (PSO) and fuzzy control in stand-alone microgrids. Together, these methods effectively address control and management challenges within hybrid microgrids (HMGs). This has been proposed to enhance energy management and to improve power quality. The findings reveal that PSO is the most advantageous and efficient approach. Its utilization proves instrumental in reducing costs, boosting reliability, and optimizing operational schedules within HMGs. Furthermore, the power profile holds considerable importance in this study, significantly enhancing system reliability and stability. This study has achieved an impressive 6.147% improvement in cost-effectiveness compared to traditional methods. This has been put into practice and validated through implementation within a MATLAB (9.13.0 (R2022b))/Simulink framework.

Compared to electric vehicle battery swapping stations (BSS) based on DC voltage bus, the series-connected BSS based on DC current bus (DCB) can achieve independent control of each battery at a lower cost, presenting foreseeable application prospects. However, this system necessitates a hybrid dual active bridge (HDAB) converter capable of connecting both current and voltage sources. The current-source-side (CS-side) devices are prone to voltage spikes and need clamp circuits. Moreover, a dedicated isolation conversion stage (ICS) is required to provide energy to the CS-side auxiliary power supply (APS). This paper proposes a solution combining clamp circuits with APS. By improving the topology and control, the proposed solution not only reduces the voltage and current stress of the clamp circuit itself but also ensures good clamping effects. Moreover, the CS-side APS has two fault-tolerant power inputs, enhancing reliability. As a result, the dedicated ICS has been eliminated, reducing the cost and size of additional circuits. Experimental results validate the effectiveness and advantages of the method proposed in this paper.