The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids.

In order to solve a certain type of Electro-Hydrostatic Actuators (EHA) hydraulic cylinder small cavity buffer end impact problem, based on AMESim to establish a hydraulic cylinder small cavity buffer machine–hydraulic joint simulation model. First, four important structural parameters, namely, the fitting clearance G of the buffer sleeve and buffer hole, the fixed orifice D, the wedge face angle $\theta$, and the wedge face length L₁ were selected to analyze their influence on the pressure of the buffer chamber and the end speed of the piston. Second, enhanced Social Behavior Optimization (SBO) was used to optimize the back-propagation neural network (BP) model to construct a prediction model for the buffer time T of the small chamber of the hydraulic cylinder, the end-piston speed V_e, the rate of change of the end-piston speed V_r, and the return speed of the hydraulic oil V_h. The SBO–BP model performed well in several key performance evaluation metrics, showing better prediction accuracy and generalization performance. Finally, the multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to optimize the hydraulic cylinder small-cavity buffer structure using the multi-objective NSGA-II with the objectives of the shortest buffer time, the minimum end-piston speed, the minimum change rate of the end-piston speed, and the minimum hydraulic oil reflux speed. The optimized design reduced the piston end speed from 0.060 m/s to 0.032 m/s, corresponding to a 46.7% improvement. The findings demonstrate that the proposed hybrid optimization approach effectively alleviates the end-impact problem of EHA small-cavity buffers and provides a novel methodology for achieving high-performance and reliable actuator designs.

At present, the development of the global energy internet is occurring in depth and the construction of a distributed power supply is rapid, and the energy router (ER), as a key device for integrating energy flow and information flow, has important application value in microgrids. In this paper, a multi-port energy router based on a 710 V DC bus is designed and developed with a modular structure design, including core components such as a total controller, a power converter, a hybrid energy storage system, and an auxiliary power supply. Flexible access and the management of multiple-voltage-level ports (690 V AC, 380 V AC, 220 V DC, and 24 V DC) are realized through rational topology design. The test results of the device show that the system performance indexes meet the design requirements. The operation is stable and reliable, displaying strong practical engineering value, and at the same time provides a technical solution that can be borrowed for other special scenarios such as the microgrid system.

This paper proposes an Enhanced Dynamic Sand Cat Search Optimization algorithm (EDSCSO) designed to address the high-order nonlinearities and strong coupling issues in the parameter tuning of the position sliding mode controller for electro-hydrostatic actuators (EHAs). Traditional swarm intelligence optimization algorithms often struggle with the transition from global to local search, which leads to being trapped in local optima and results in lower computational efficiency. To overcome these challenges, the EDSCSO algorithm introduces an escape mechanism, a stochastic elite cooperative bootstrap strategy, and a multi-path differential perturbation strategy. These enhancements significantly increase the diversity of the population, facilitate a smooth transition from global to local search, avoid local optimum traps, and better balance the exploration and exploitation capabilities of the algorithm. Based on this algorithm, the sliding mode surface and convergence rate parameters within the sliding mode controller are optimized. Simulation validations conducted on the combined platform of MATLAB/Simulink and AMESim demonstrate that the sliding mode PID controller optimized by the EDSCSO algorithm achieves smaller steady-state and tracking errors, exhibits greater robustness, and offers enhanced computational efficiency compared to other swarm intelligence optimization algorithms. This study provides an effective optimization strategy to improve the control performance of the EHA position sliding mode controller.

To enhance the operational efficiency of energy storage converters in grid-connected systems with high renewable penetration, this study systematically investigates key technologies of active neutral-point clamped (ANPC) topology under “electrical–thermal–mechanical” multi-physical field coupling. The study reviews recent progress in structural design, modulation strategies, and fault-tolerant control, highlighting their impact on efficiency, reliability, and power density. At the structural stage, a hybrid SiC/IGBT device configuration combined with a three-dimensional stacked bus reduces conduction loss and achieves parasitic inductance. In the modulation stage, improved finite-set model predictive control and adaptive space vector modulation shorten computation time to 20 µs and keep total harmonic distortion (THD) within 2.8%. System-level evaluations demonstrate that a 250 kW ANPC converter attains a peak efficiency of 99.1%, a power density of 4.5 kW/kg, and a mean time between failure exceeding 150,000 h. These findings reveal a clear transition from single-objective performance improvement toward integrated multi-physics co-design. By unifying advanced modulation, intelligent fault-tolerant control, and multi-field coupling optimization, ANPC-based converters advance converters to a new stage of higher efficiency, reliability, and stability. The results provide essential technical support for next-generation power conversion systems in renewable-rich grids.

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer.