Search Results (1,237)

Wind power, photovoltaic (PV), hydropower, and energy storage, along with other multi-type clean energy sources, transmitted via islanded voltage source converter based high voltage direct current (VSC-HVDC) systems, will become an important form of delivery for renewable energy bases. However, due to the volatility and uncertainty of renewable energy, its high-proportion integration significantly exacerbates system frequency fluctuations and voltage violation risks, posing severe challenges to the stable operation of the system. To strike a balance between maximizing clean energy integration and maintaining the stability of the islanded system, this paper presents a capacity optimization approach for multiple types of clean energy within an islanded VSC-HVDC transmission system. First, typical wind power and PV output scenarios are obtained via Monte Carlo simulation, and a virtual slack bus is introduced to establish a power flow calculation model for the islanded VSC-HVDC transmission system. Second, the active power is regulated through fast VSC-HVDC support and droop control mechanisms, while a quadratic programming model for voltage is established based on the relationship between reactive power and voltage, aiming to drive the virtual slack bus power to zero, thereby improving system frequency and voltage stability. Finally, a genetic algorithm (GA) is employed to achieve optimal capacity configuration for maximizing renewable energy integration, and the corresponding optimal energy storage capacity is determined accordingly. Simulation results demonstrate that, while satisfying operational constraints, the proposed method identifies the maximum installable capacities of wind power and PV while simultaneously reducing the required energy storage capacity. Full article

In recent years, high penetrations of inverter-based resources are posing significant challenges to the medium-voltage networks, in which protection schemes based on high fault currents and unidirectional power flow may not perform as expected. This paper proposes a dynamic fault-detection and relay-coordination scheme for a medium-voltage network with high penetration of grid-forming inverter sources. A detailed 33 kV system model comprising six battery energy storage system (BESS) feeders and a four-distributed-load model was built in DIgSILENT Power Factory and tested under various grid-connected and islanded system conditions using the complete short-circuit method. Four simultaneous fault checks, including sequence component analysis, symmetrical voltage variation, superimposed current with voltage restraint, and current waveform analysis, are used to detect the fault in a specific part of the medium-voltage network. After-fault detection, dynamic pickup scaling and relay blocking are coordinated through IEC 61850 GOOSE and DNP3 so only the closest unblocked relay or relay pair trips. The dynamic pickup settings are adjusted considering the ratio of fault levels in the conventional system versus the inverter-based resources-fed medium-voltage system. Simulation results show successful overcurrent coordination retention even when inverter fault current limitation is set at 1.3 p.u. or lower with 10% generation margin. The proposed scheme allows traditional relays with existing infrastructure to function correctly in fully inverter-dominated medium-voltage systems without any synchronous backup. The novelty is the integration of fault confirmation, pickup scaling and a blocking scheme with retention of an independently operating local backup. Compared to fixed grid-connected settings, the proposed scheme recovers islanded-mode pickup values while maintaining primary–backup grading margin for the relay. Full article

Doppler Light Detection and Ranging (LIDAR) systems and a mesoscale meteorological model coupled with computational fluid dynamics (CFD) for the monitoring of low-level wind and turbulence have been extensively applied for the Hong Kong International Airport. This study represents the first application in Hong Kong to apply such techniques for the exploration of providing meteorological support for the operation of Unmanned Aircraft Systems (UASs) in a sandbox project in Hong Kong. The flight route under consideration is between the western coast of Hong Kong Island and an outlying island called Lamma Island, with a sea channel in between. Based on the LIDAR observations in three different prevailing wind directions, low-level turbulence may arise from wind flow disruptions by natural terrain and human-made buildings. Simulations of the wind and turbulence are attempted using the GPU-based FastEddy, with the turbulent kinetic energy equation being used to output the eddy dissipation rate (EDR). Comparisons between observed and simulated fields showed broadly consistent patterns across wind speed, wind direction, and EDR. Quantitative validation yielded RMSE of 1.35 m/s for wind speed, 28.4° for wind direction, and 0.032 m²/s² for EDR, with corresponding R² values of 0.72, 0.48, and 0.07, respectively. However, point-to-point comparison as in the scatter plot of the two datasets is still challenging, due to low correlation for EDR. Nonetheless, FastEddy is found to shed preliminary insights to generate reasonable simulations of low-level winds and turbulence to support the operation of UASs for the cases under study. These findings should be considered preliminary and exploratory given the limited number of case studies analyzed. More cases would need to be studied to find out the performance of FastEddy in other meteorological conditions. Full article

As Cuba advances toward ambitious renewable energy targets, large-scale energy storage is essential to ensure grid stability and operational flexibility. Pumped Hydro Energy Storage (PHES) is the most mature technology for providing long-duration storage. This study proposes an integrated GIS-based screening and engineering validation methodology for PHES site selection and presents the first academic application of the Australian National University (ANU) Global Pumped Hydro Atlas to the Cuban context. Rather than replacing historical engineering studies, the proposed methodology complements them through automated geospatial analysis, local infrastructure assessment, environmental screening, and engineering validation. The GIS analysis identified 92 potential off-river closed-loop PHES sites across Cuba, including 14 sites in the 500 GWh storage class and 78 sites in the 150 GWh storage class, substantially expanding the national inventory. Comparison between the GIS-derived candidates and historical investigations demonstrates strong agreement between both approaches, while the Mayarí project serves as a representative case study for local validation. The results confirm significant PHES potential distributed across Cuba’s three main mountainous regions, with several sites exhibiting favourable hydraulic heads and cost classifications. The proposed methodology provides a transferable framework for integrating legacy engineering knowledge with modern GIS-based planning tools in islanded and developing power systems while highlighting the need for supportive regulatory frameworks to accelerate future PHES deployment. Full article

Anti-islanding certification is generally reported as pass or fail, although response times across the active- and reactive-power (P-Q) matrix contain additional information. We analyzed 925 Condition-A measurements from 37 grid-connected photovoltaic (PV) inverters tested under Korean Industrial Standard KS C 8565 at 60 Hz. A signed 5 × 5 map and within-product contrasts used each inverter as its own reference. Relatively long responses followed the ΔQ = 0 row rather than the balanced cell alone. Mean response was 0.221 s at balance, 0.140 s at signed |ΔQ| = 10% endpoints, and 0.208 s at signed |ΔP| = 10% endpoints. The direct P- versus Q-axis difference was 0.068 s (95% confidence interval (CI), 0.040–0.095 s; p < 0.001). Across the matrix, the ΔQ = 0 row exceeded the |ΔQ| = 10% rows by 0.061 s (95% CI, 0.036–0.085 s; p < 0.001). All values met the 0.5 s criterion. Reactive-power balance, rather than a single balanced cell, therefore marked a corridor of relatively long compliant response. This analysis converts certification records ordinarily summarized as pass/fail outcomes into a directional performance profile. The analysis procedure, but not the numerical PV response times, is proposed for future grid-connected power conversion system (PCS) testing, which requires separate charge- and discharge-mode validation. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

Energy planning is a growing challenge driven by the global push for decarbonization and the need to modernize aging power grids. This issue is particularly critical for islands, which often endure energy vulnerability and a high dependency on imported fossil fuels. While the electrification of transport is a popular option to reduce emissions, the integration of Electric Vehicles into weak island grids presents significant stability challenges due to the intermittent nature of renewable sources. This article provides a systematic bibliometric analysis to identify the advances made in bridging the gap between long-term energy balance and short-term dynamic stability in isolated systems. This paper analyzes islands’ stability needs and showcases smart charging systems, exploring their roles as distributed energy storage and as providers of ancillary services. First, the most relevant international scientific journals are identified to allow the subsequent selection and quantitative and qualitative analysis of articles dealing with EV-island stability pathways. A total of 7469 publications were screened, of which 284 articles were finally selected. Full article

This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations. Full article

The Greek power system includes 42 non-interconnected islands grouped into 28 autonomous electrical systems operated by the Hellenic Electricity Distribution Network Operator. Although these systems possess substantial wind and solar potential, the technical constraints of isolated microgrids lead to systematic renewable energy curtailment. Building on our previous methodology for estimating curtailed wind energy and hydrogen production, this study develops and evaluates a dispatch-oriented power-to-power pathway in which curtailed wind electricity is converted into hydrogen and subsequently reconverted into electricity. The study integrates hydrogen-to-power technology selection, annual energy recovery, dispatch strategy, and operational environmental and economic benefits for a representative non-interconnected island. A comparative assessment of commercially relevant hydrogen-to-power technologies identified proton exchange membrane fuel cells as the most suitable option because of their absence of direct CO₂ and NO_x emissions, rapid start-up, load-following performance, modularity, and compatibility with remote island operation. Applying the previously developed curtailment methodology to 2024 data yielded 9334.5 MWh of exploitable curtailed wind energy. This energy could produce 155.6–233.4 tonnes of hydrogen and recover 2437.1–4277.9 MWh of electricity annually. Two dispatch strategies were evaluated: continuous integration of hydrogen-derived electricity into the island’s generation mix, and strategic hydrogen storage with priority dispatch during periods of emergency diesel generator operation. Under the reference case, both strategies recovered approximately 2935.1 MWh annually, avoided 1868.9 tonnes of CO₂ emissions, and reduced fuel expenditure by €359,000. Full article

Detecting naval vessels at extreme distances using infrared search and track (IRST) systems presents severe physical challenges, notably the complete loss of geometric texture and the non-linear submersion of weak target signals within high-dynamic-range sea clutter. Traditional pure data-driven convolutional neural networks (CNNs) rely heavily on visual appearances and suffer from critical feature blind spots under such extreme physical degradation. To overcome this, we propose a Physics-Aware Deep Coupling Network that shifts the detection paradigm from appearance-based feature extraction to physics-guided attribute recognition. Our method deconstructs the degraded infrared signal into three complementary physical domains: an adaptive radiation energy mapping, corresponding to the energy domain, to rescue weak targets; a bio-inspired spatial saliency filtering mechanism, corresponding to the frequency domain, to maximize the signal-to-clutter ratio; and a PSF-coherent gradient topology framework, corresponding to the gradient domain, to discriminate genuine point targets from chaotic sun glints and island edges. These processed priors, alongside the raw image, are integrated into a 4-channel tensor and fused via a Cross-Domain Attention Module, ensuring deep network coupling. To evaluate this architecture, we conduct extensive experiments on the real-world Maritime-SIRST dataset. Since the original dataset provides only pixel-level segmentation masks, we generate axis-aligned bounding-box detection labels from these masks and retrain both the proposed method and a suite of state-of-the-art YOLO detectors under a unified detection paradigm. Extensive benchmarking demonstrates that our physics-aware methodology consistently outperforms these detectors, achieving a mAP50 of 0.923 and an F1 score of 89.92%, thus providing a highly interpretable and robust solution for maritime domain awareness under extreme physical constraints. Full article

Virtual synchronous generator (VSG) control is widely used to improve the frequency stability of low-inertia microgrids. However, most existing adaptive VSG strategies tune the virtual inertia and damping coefficient mainly according to local frequency deviations of the energy storage converter, while the effect of supplementary wind turbine frequency support on the admissible VSG parameter range is rarely considered. To address this limitation, this paper proposes a wind–storage coordinated frequency control strategy that combines an energy storage fuzzy VSG with active-power-frequency droop support from a doubly fed induction generator (DFIG). The scientific contribution of this study is that the DFIG droop support term is incorporated into a reduced-order wind–storage small-signal model, and an admissible scheduling region for the virtual inertia and damping coefficient is constructed according to prescribed damping ratio and natural angular frequency constraints. This region is used to constrain the online fuzzy parameter scheduling of the energy storage VSG. In addition, bell-shaped membership functions are introduced to obtain smoother parameter variation and are compared with triangular membership functions under the same operating conditions. MATLAB/Simulink simulations are conducted under grid-connected/islanded transition, load switching, and renewable-power fluctuation conditions. Compared with the benchmark strategies, the proposed method reduces the maximum and average frequency deviations to 0.181 Hz and 0.016 Hz, respectively. The maximum discharge power, RMS power, and cumulative energy throughput of the energy storage system are reduced to 236.853 kW, 155.822 kW, and 5.751 kWh, respectively. These results indicate that the proposed coordinated strategy improves frequency regulation while reducing the transient regulation burden of the energy storage system within the investigated operating conditions. Full article

With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composite disturbances under strong noise remains a critical technical challenge. The existing single time–frequency transformation methods cannot simultaneously capture transient time-domain details and fine frequency-domain features of steady-state harmonics, while mainstream deep learning classification networks contain redundant parameters and introduce excessive computational overhead, failing to meet the real-time deployment requirements of power edge terminals. To address these limitations, a lightweight Coordinate Attention ResNet network named ResNet–LCA is proposed based on the dual-modal time–frequency fusion of the Continuous Wavelet Transform and Short-Time Fourier Transform. First, the two transforms are implemented separately to generate two groups of complementary time–frequency maps, which are concatenated along the channel dimension to fully extract the coupling features between the steady-state harmonics and the transient impulses. Second, a Haar wavelet subband mean aggregation module is designed for dimensionality reduction with negligible information loss. This module eliminates the channel redundancy introduced by the multimodal fusion and reduces the overall computational overhead at the input stage. Finally, a lightweight residual network integrated with Coordinate Attention is constructed, with Grouped Half-Convolution adopted to compress the model parameters. CA offsets the feature attenuation induced by the lightweight structural design and further improves the model’s noise immunity. A simulation verification was carried out on a simulated dataset covering 25 types of single and superimposed composite disturbances. At a signal-to-noise ratio of 20 dB, the proposed method achieved an average classification accuracy of 97.92%, with only 5.32 M total parameters and a single-sample GPU inference latency of 0.33 ms. Compared with standard ResNet-18 under 20 dB noisy conditions, the total parameter volume was reduced by 52.7%, the inference latency was shortened by 0.13 ms, and the classification accuracy was improved by 0.60 percentage points. The proposed method achieves coordinated optimization of classification accuracy, noise immunity and inference efficiency, and it can provide lightweight technical support for online intelligent power quality monitoring at the edge nodes of microgrids and islanded power systems. Full article

Freeze desalination (FD) is being reconsidered as a low-temperature desalination route because it separates water through ice formation rather than evaporation or membrane pressure. This review examines FD from the perspective of sustainable water–energy systems, with emphasis on applications where conventional desalination may face technical or energy limitations. Unlike general reviews focused mainly on freezing principles, this work connects crystallization mechanisms, experimental performance, energy integration, and scale-up barriers. The literature analyzed, consisting primarily of studies published between 2015 and 2026, was grouped into four areas: modeling and simulation, experimental and pilot-scale validation, technological integration, and energy–economic assessment. Recent progress has been reported in eutectic freeze crystallization, vacuum-assisted ice–brine separation, ice morphology control, LNG cold recovery, solar-assisted FD, and hybrid systems that combine desalination with cooling or energy recovery. Reported performance varies widely. Reported SEC varies by more than an order of magnitude: values near 3 kWh/m³ occur mainly under favorable integration or external-cold assumptions, whereas conventionally refrigerated laboratory and pilot systems can require substantially more energy. This difference shows that FD performance depends strongly on crystallizer design, feedwater composition, separation strategy, and cold-energy recovery. FD should not be viewed as a direct replacement for RO, MED, or MSF. Its strongest potential is in hypersaline brine treatment, LNG terminals, cold regions, off-grid systems, island communities, and decentralized water production coupled with renewable or waste-cold sources. Full article

The rapid growth of artificial intelligence (AI) data centers introduces highly variable and mission-critical load profiles that challenge conventional power supply strategies. This paper proposes an islanded microgrid gas turbine generator (GTG) and long-duration energy storage (LDES) hybrid architecture to provide both short-term load balancing and extended energy support under prolonged outage conditions. A probabilistic multi-phase workload model is developed to capture the temporal characteristics of training, fine-tuning, and inference processes, incorporating both high-frequency fluctuations and multi-day workload variations. Based on reliability requirements, an LDES sizing methodology is formulated to ensure long-duration autonomy for mission-critical operation in a 12 MW power-block AI data center system, with the storage capacity determined based on a 12-h autonomy criterion. The GTG operating point is then evaluated using four storage performance metrics: charge/discharge transition frequency, charging time ratio, state-of-charge (SoC) deviation, and cumulative energy movement. The results indicate that the optimal GTG operating point ranges from approximately 40–73.3% of the initially selected rating, closely tracking the time-varying average load and significantly reducing LDES utilization and storage stress. While GTG fixed-output operation may induce SoC drift under sustained workload variations, applying the identified optimal operating point maintains SoC within the desired range, demonstrating stable LDES operation without dynamic adjustment. The proposed framework provides quantitative design and operational guidelines for GTG–LDES hybrid systems in next-generation AI data centers. Full article