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Energy management in hybrid fuel cell ship systems faces the dual challenges of optimizing hydrogen consumption and ensuring power quality. This study proposes an Improved Weighted Antlion Optimization (IW-ALO) algorithm for multi-objective problems. The method incorporates a dynamic weight adjustment mechanism and an elite-guided strategy, which significantly enhance global search capability and convergence performance. By integrating IW-ALO with the Equivalent Consumption Minimization Strategy (ECMS), an improved weighted ECMS (IW-ECMS) is developed, enabling real-time optimization of the equivalence factor and ensuring efficient energy sharing between the fuel cell and the lithium-ion battery. To validate the proposed strategy, a system simulation model is established in Matlab/Simulink 2017b. Compared with the rule-based state machine control and optimization-based ECMS methods over a representative 300 s ferry operating cycle, the IW-ECMS achieves a hydrogen consumption reduction of 43.4% and 42.6%, respectively, corresponding to a minimum total usage of 166.6 g under the specified load profile, while maintaining real-time system responsiveness. These reductions reflect the scenario tested, characterized by frequent load variations. Nonetheless, the results highlight the potential of IW-ECMS to enhance the economic performance of ship power systems and offer a novel approach for multi-objective cooperative optimization in complex energy systems. Full article

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids. Full article

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

In this study, the impact of the electric motor size and the hybridization ratio of a Fuel Cell Electric Bus on its vehicle performance (i.e., gradeability and acceleration) and fuel consumption was investigated using the ADVISOR software. The investigation first involved a parametric analysis with different electric motor and fuel cell sizes for the dynamic performance metrics, specifically the 0–60 km/h vehicle acceleration and the maximum gradeability (%) at a constant speed of 20 km/h. The results revealed that the acceleration is most sensitive to fuel cell power. Regarding gradeability, a more complex relationship was observed: when the electric motor power was below 215 kW, gradeability remained consistently low regardless of the fuel cell size. However, for motors exceeding 215 kW, fuel cell power then became a significant influencing factor on the vehicle’s climbing capability. Subsequently, the analysis focused on the effect of the hybridization ratio, which represents the power balance between the fuel cell and the energy storage system, varied between 0 and 0.8. Results showed that increasing the hybridization ratio decreases gradeability and acceleration performance and increases total energy consumption. This trade-off is quantitatively illustrated by the results over the Central Business District (CBD) driving cycle. For instance, the pure battery-electric configuration (a hybridization ratio of 0), featuring a 296 kW battery system, recorded a gradeability of 12.4% and an acceleration time of 16.3 s, while consuming 28,916 kJ. At an intermediate hybridization ratio of 0.4 (composed of a 118.4 kW fuel cell and a 177.6 kW battery), performance remained high with a gradeability of 12.2% and an acceleration of 17.3 s, but the energy consumption increased to 43,128 kJ. Finally, in the fuel-cell-dominant configuration with a hybridization ratio of approximately 0.8 (a 236.8 kW fuel cell and a 59.2 kW battery), gradeability dropped to 8.4%, acceleration time deteriorated to 38.9 s, and total energy consumption increased further to 52,678 kJ over the CBD driving cycle. Full article

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article

This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article

The successful establishment of battery electric vehicles (BEVs) is strongly linked to criteria such as cost and range. In particular, the need for air conditioning strains battery capacities and limits the availability of BEVs. Thermal energy storage systems (TESs) open up alternative paths for heat and cold supply with excellent scalability and cost efficiency. Previous TES concepts have largely focused on heat during cold seasons, but storage-based air conditioning systems for all seasons are still missing. To fill this gap, a concept based on a Brayton cycle allowing heat and cold supply and, simultaneously, an output of electrical energy at times when no air conditioning is needed was investigated. Central thermal components include water-based cold storage and electrically heated, high-temperature, solid-medium storage, both with innovative TPMS structures and flexible operation managements. With transient simulation studies a system was identified with effective storage densities of up to 100 Wh/kg, reaching a constant heat and cold supply of 5 kW and 2.5 kW, respectively, over 41 min. In addition, the underlying cycle allows an electrical output of up to 1.7 kW during times of inactive air conditioning requirements. Compared to a reference system designed only for winter operation, the moderately lower storage densities are compensated by proportionately longer discharging times. By combining a compact and dynamic Brayton cycle with a TES in BEVs, a storage-based air conditioning system with high utilization potential and high operational flexibility was developed. In addition to further optimizations, the knowledge for TES solutions can also be transferred to today’s air conditioning systems, extending the solution space for storage-supported thermomanagement options in BEVs. Full article

Ensuring thermal stability is a major concern in lithium-ion battery systems. Although phase change materials (PCMs) provide a passive approach for temperature regulation, they are limited by poor heat conduction and potential leakage during phase transitions. This study develops a novel composite PCM (CPCM) using paraffin (PA) as the matrix, copper foam (CF) as a conductive skeleton (10–30 pores per inch, PPI), and a low-melting-point alloy (LMA) as an encapsulant to prevent leakage. The effects of CF pore size on thermal conductivity, impregnation ratio, and leakage resistance were systematically investigated. Results show that CPCM with 10 PPI CF achieved the highest thermal conductivity (4.42 W·m⁻¹·K⁻¹), while LMA encapsulation effectively eliminated leakage. The thermal management performance was evaluated on both a single 18,650 LIB cell and a 2S2P module during rate discharging at 1C, 2C, and 3C. For the module at 3C, the 10 PPI CPCM significantly lowered the maximum temperature from 75.9 °C to 44.6 °C and critically reduced the maximum temperature difference between cells from 10.2 °C to a safe level of 1.2 °C, significantly improving temperature uniformity. This work provides a high-conductivity and leakage-proof CPCM solution based on LMA-encapsulated CF/PA for enhanced thermal safety and uniformity in LIB modules. Full article

Solar-electric propulsion offers a practical way to lengthen the endurance of small fixed-wing unmanned aerial vehicles while removing the noise, emissions, and upkeep that come with combustion engines. This work describes and tests a lightweight platform that couples a flexible thin-film photovoltaic array, a high-efficiency power-tracking controller, and a lithium–polymer battery to an electric brushless drivetrain. A ground-based flight emulator reproducing steady cruise allows continuous logging of the electrical flows between panel, battery, and motor. The results show that the solar subsystem can sustain most of the cruise demand, so the battery is called on only sparingly and is even able to recharge when sunlight is higher than a specific threshold. This balance translates into a clear endurance gain without upsetting the aircraft’s weight or handling. Full article

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article