Search Results (297)

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

Benefiting from their outstanding porosity, considerable specific surface area, and natural flexibility, cellulose nanofibers (CNFs)/MOF materials have emerged as competitive candidates for advanced flexible energy storage devices. However, conventional CNFs/MOFs aerogels or films often suffer from poor recoverability under compression, bending, and folding, accompanied by severe plastic deformation that compromises the cycling and structural stability of devices. To address this issue, we report a rationally designed flexible PU/CNFs/ZIF-8/PANI composite foam with an interconnected micro-mesoporous structure. Using polyurethane foam as a soft substrate and CNFs/ZIF-8 as building blocks, the composite was fabricated through a combined strategy of impregnation, in situ ZIF-8 growth, hot-pressing, and in situ aniline polymerization with simultaneous etching of the ZIF-8. The incorporation of carboxylated CNFs enhances the hydrophilicity of the PU skeleton. This, in combination with the hot-pressed framework, establishes an interconnected 3D network, thereby effectively preventing the agglomeration of active materials. Meanwhile, the hierarchical pores derived from the sacrificial ZIF-8 template provide abundant electroactive sites, accelerate ion transport, and facilitate high PANI loading. By virtue of this synergistic architectural effect, the resultant electrode achieves a high specific capacitance of 449 F/g at 0.2 A/g, with 97% capacitance retention after 2000 cycles at 5 A/g. Furthermore, the composite foam demonstrates excellent mechanical flexibility, with a tensile strength of 0.87 MPa and an elongation at break of 230%. This work offers a feasible approach for developing high-performance flexible supercapacitors and provides novel perspectives for the rational design of portable energy storage devices. Full article

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters. Full article

The continuous increase in the penetration rate of renewable energy has posed severe challenges to the flexibility of power systems, especially in coastal and island areas where local power supply is insufficient while electricity demand keeps growing. Focusing on the regional water–energy nexus system (WENS), this paper fully taps into the flexibility potential of seawater desalination plants (SWDPs) as adjustable loads, and proposes a bi-level optimal dispatch model. First, the operational characteristics of reverse osmosis (RO) seawater desalination loads are analyzed, and an operational model encompassing water intake equipment, high-pressure pumps, clear water tanks and product water tanks is established. Second, a dispatch framework for the regional WENS incorporating SWDP is designed, on the basis of which a bi-level optimal dispatch model is constructed: the upper-level model takes maximizing wind power accommodation and minimizing wind power output fluctuation as the objectives, so as to determine the wind power output and the charging/discharging strategy of supercapacitors; constrained by the decisions made by the upper-level model, the lower-level model comprehensively takes into account the operation cost of thermal power units (TPUs), the wind curtailment penalty cost of the system, the operation cost of energy storage systems and the operation cost of SWDP, and thus establishes an optimization model with the goal of minimizing the comprehensive operation cost of the system. Finally, a comparative analysis is carried out under different scenarios. The results show that compared with the optimal scheduling scheme in which the seawater desalination load does not participate in regulation, the proposed method can reduce the wind curtailment rate by 43.71%, the energy consumption cost of the seawater desalination load by 50.98%, and the total system operation cost by 22.51%, thus providing a feasible approach for the collaborative optimization of water–energy systems in coastal areas. Full article

Transient modeling of short-circuit faults in the DC power system of hybrid electric aircraft (HEA) serves as a fundamental basis for effective fault identification, localization, and isolation. Before faults are detected and protective measures are taken, distributed sources and loads maintain their normal control strategies. However, previous studies frequently overlook the impact of these control dynamics on the transient behavior of DC power systems, leading to reduced accuracy in fault transient models. Therefore, this paper proposes a fault transient modeling method for the DC power system of HEA considering the control effects of distributed sources and loads. Firstly, the transient characteristics of all components in the system are analyzed, including generators and fan motors, batteries and DC load, and supercapacitors. Subsequently, a comprehensive fault transient model of the HEA DC power system is established. Finally, the validity of the proposed method is verified through comparison with results from a semi-physical test platform. The results demonstrate that the proposed modeling approach enhances the accuracy of transient analysis for the faulty HEA DC power systems. Full article

Stable circumstances and an improved voltage profile need power compensators integrated with energy storage elements in AC power systems. The control of these compensators is of paramount importance for obtaining high accuracy, reliability, and better system dynamics, which involves careful controller design considerations and small-signal analysis. This paper focuses on the use of a static synchronous compensator (STATCOM) and supercapacitor energy storage system (SCESS) for achieving voltage stability, grid support, and better system dynamics. After the primary load is shifted to the grid, real power assistance is promptly injected into the AC grid to enhance the DC-link voltage, as well as the grid voltage, and reduce supply current from the grid using a vector control technique. The SCESS is handled with the help of a bidirectional DC–DC converter, which facilitates charging and discharging during boost and buck operations, respectively. Using small-signal modeling, the stable system is designed to obtain a reliable and stable output, which is confirmed by the systematic simulations and experiments. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. Full article

Microbial fuel cells (MFCs) utilizing livestock wastewater represent a critical path toward sustainable energy and net-zero emissions. To maximize this potential, this study investigates a novel circuit configuration, integrating twin MFCs with dual supercapacitors in a closed-loop system, to enhance charge storage and electricity generation. By utilizing molasses-seawater anolytes, the study establishes a performance benchmark for optimizing energy recovery in future livestock wastewater treatment applications. The self-adjusting potential difference between interconnected MFCs is verified, and supercapacitors significantly improve energy harvesting by reducing load impedance and balancing capacitor plate charges. Voltage gain across supercapacitors exceeds that of single MFC charging, demonstrating the benefits of series integration. Experimental results reveal that catholyte properties—electrical conductivity, salinity, pH, and dissolved oxygen—strongly influence MFC performance. Optimal conditions for a neutralized anolyte (pH 7.12) include dissolved oxygen levels of 5.37–5.68 mg/L and conductivity of 24.3 mS/cm. Under these conditions, supercapacitors charged with sterile diluted seawater catholyte store up to 40% more energy than individual MFCs, attributed to increased output current. While the charge balance mechanism of supercapacitors contributes to storage efficiency, its impact is less pronounced than that of conductivity and oxygen solubility. The interplay between electrochemical activation and charge balancing enhances overall electricity harvesting. These findings provide valuable insights into optimizing MFC-supercapacitor systems for renewable energy applications, particularly in livestock wastewater treatment. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost. Full article

With the rapid advancement of hydrogen-based direct current microgrid (H₂-DCMG) technology, multi-port converters (MPCs) have emerged as the pivotal interface for integrating renewable power generation, energy storage, and diverse DC loads. This paper systematically reviews the current research status and development trends of isolated and non-isolated MPC topologies within hydrogen-based DC microgrids. Firstly, it analyses the interface requirements for typical distributed energy sources (DER) such as photovoltaics (PV), wind turbines (WT), fuel cells (FC), battery energy storage (BESS), proton exchange membrane electrolyzers (PEMEL), and supercapacitors (SC). Secondly, it classifies and evaluates existing MPC topologies, clarifying the structural characteristics, technical advantages, and challenges faced by each type. Results indicate that non-isolated topologies offer advantages such as structural simplicity, high efficiency, and high power density, making them more suitable for residential and small-scale microgrid applications. Isolated topologies, conversely, provide electrical isolation and modular scalability, rendering them appropriate for high-voltage electrolytic hydrogen production and industrial scenarios with stringent safety requirements. Finally, the paper identifies current research gaps and proposes that future efforts should focus on exploring topology optimization, system integration design, and reliability enhancement. Full article

Fuel cell electric vehicles (FCEVs) rely on a battery system to manage transient load demands and to recover braking energy. In recent years, hybrid topologies that also integrate supercapacitors have gained considerable attention, since they can improve system efficiency, driving dynamics, and component lifetime. Supercapacitors, thanks to their much higher power density compared to conventional batteries, are particularly promising for adoption in FCEVs. Most studies in the literature, however, evaluate these architectures under standardized homologation driving cycles. While such cycles provide a common benchmark for comparison, they generally exhibit less energy-intensive profiles and therefore do not fully capture the real operating demands of a vehicle. For this reason, the present work investigates the use of batteries and supercapacitors in FCEVs under an actual urban driving mission, where the route includes an experimentally measured altitude profile. This approach allows for a more realistic assessment of energy requirements. Furthermore, the analysis carried out in this study considers different powertrain configurations: the exclusive use of a battery, the sole use of a supercapacitor, and a hybrid combination of both systems. These scenarios are evaluated both for an FCEV that can only be refueled with hydrogen and for a plug-in hybrid version of the vehicle that can also recharge its battery from an external charging station. Full article

This paper describes the modeling, simulation, and experimental validation of a Hybrid supercapacitor–battery Energy Storage System (HESS) for photovoltaic (PV) modules under partial shading. The system is intended to provide an uninterruptible power supply for a DC primary load. The Hybrid Power System (HPS) architecture includes a DC/DC boost converter with a Maximum Power Point Tracking (MPPT) algorithm that optimizes photovoltaic (PV) energy extraction. Furthermore, two bidirectional DC–DC converters are dedicated to the battery and supercapacitor subsystems to allow the bidirectional power flow within the HPS. The proposed HESS is evaluated through MATLAB/Simulink simulations and experimentally validated on a prototype using real-time hardware based on the dSPACE DS1104. To optimize power flow within the HPS, two energy management strategies are implemented: the Thermostat-Based Method (TBM) and the Filter-Based Method (FBM). The results indicate that the thermostat-based strategy provides better battery protection under shading conditions. Indeed, with this approach, the battery can remain in standby for 300 s under total permanent shading (100%), and for up to 30 min under dynamic partial shading, thereby reducing battery stress and extending its lifetime. Full article