Search Results (271)

The energy transition requires efficient management of decentralized resources, in which Renewable Energy Communities (RECs) play an increasingly important role. However, the variability of solar generation and the unpredictability of consumption create complex balancing challenges. To address the limitations of existing planning tools—which often rely on synthetic profiles or small-scale validations—this study presents a data-driven Digital Twin-inspired simulation framework The unique contribution of this work lies in the combination of three elements: the use of high-resolution sub-hourly smart-meter data, the application of a novel demographic filtering methodology to accurately isolate permanent community load profiles, and the integration of an AI-driven N-HiTS (Neural Hierarchical Interpolation for Time Series) forecasting model. The framework was implemented using the PyECOM simulation engine and applied to the Culatra Island Energy Community, Portugal, processing empirical data from 338 dwellings. Multiple scenarios were evaluated, including demand flexibility, photovoltaic (PV) expansion, and battery energy storage (BESS) deployment. The baseline scenario revealed a substantial dependence on the external grid, with a Self-Sufficiency (SS) rate of 12.51%. Expanding PV capacity by 200 kWp increased SS to 32.1% but generated significant energy surpluses. The optimal configuration, integrating a 600 kWh BESS, increased SS to 37.3% while restoring the Self-Consumption (SC) rate to 99.8%. Furthermore, the integrated N-HiTS predictive model achieved a coefficient of determination of 0.64 under highly variable weather conditions. Ultimately, the results demonstrate the critical value of combining empirical simulation, optimized storage sizing, and advanced forecasting techniques to support robust REC planning. 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

In November 1916, the Imperial German Navy conducted an ambitious raid against the Russian forces in the Baltic Sea. With the aim of attacking Russian ships in the coastal waters of Estonia, a squadron was dispatched from Libau. The operation proved to be very costly, with seven ships sunk by Russian mines and only minimal damage caused to the port of Paldiski. In 2025, the Estonian Maritime Museum conducted a survey of three of the seven Imperial German Navy large torpedo boats sunk north of Hiiumaa island in Estonia. The aim was to identify the wrecks and determine the potential threat the wrecks may pose to the environment from the fuel oil the boats carried. The assessment was time critical as the wrecks had sunk more than a hundred years ago, and due to the location, any oil spill from the wrecks could potentially impact Estonia, Finland, and/or Sweden. The research concluded that all three surveyed wrecks could still contain substantial amounts of fuel oil, and although the wrecks are not leaking and the collapse of the wrecks is not considered imminent, the risk mitigation should be carried out as soon as possible; the possibility for successful oil removal diminishes every year as the wrecks corrode and disintegrate. 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

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

Land subsidence is one of the most critical geological hazards in Hainan Province, primarily driven by groundwater overexploitation. This study integrates regional-scale SBAS-InSAR deformation results with groundwater observations from nine representative monitoring wells to investigate the spatiotemporal evolution of land subsidence and its groundwater-related response mechanisms in Hainan Province. Sentinel-1A imagery from 2019 to 2023 was used to derive LOS deformation time series, which were converted into vertical land subsidence using local incidence-angle correction under the assumption of negligible horizontal displacement. Seasonal and Trend decomposition using Loess (STL), Pearson correlation analysis, dynamic time warping (DTW), and lag correlation analysis were applied to separate multiscale signals and examine groundwater–subsidence responses in representative hydrogeological settings. The results indicate that (1) land subsidence in Hainan Province is mainly concentrated in coastal plains, with Haikou, Wenchang, and Danzhou identified as the main subsidence centers, where local annual subsidence rates exceed −50 mm/yr; (2) representative well-based analysis shows that groundwater-level decline is closely synchronized with cumulative subsidence in major subsidence-sensitive areas, with DTW distances consistently below 10, indicating high temporal consistency; long-term groundwater depletion is an important driver of cumulative subsidence in these representative areas; (3) lag correlation analysis reveals spatially heterogeneous lag responses of 1–6 months between groundwater-level fluctuations and land subsidence, with lag time and phase relationship closely related to aquifer structure, low-permeability layer distribution, and groundwater extraction intensity. This study provides a scientific basis for land subsidence mitigation and sustainable groundwater management in tropical island regions. Full article

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article

The increasing frequency of high-impact, low-probability climate events has highlighted the limitations of conventional reliability criteria, including N-1 planning assumptions, and the need for dynamic resilience architectures in electrical systems. This article analyzes the evolution, trends, and technological challenges associated with microgrid resilience under extreme-weather disruptions. The study adopts a hybrid research design that combines quantitative bibliometric mapping of 283 Scopus-indexed article records for 2016–2025 using CiteSpace version 7 with a structured technical synthesis of the selected literature. The structural analysis identified nine thematic clusters and indicated a transition from service restoration and component-level recovery toward multi-energy energy-management systems, resilient distribution-system planning, and mobile restoration resources. The technical synthesis shows that modern resilience is increasingly associated with hierarchical control architectures: optimization and forecasting methods support tertiary-level energy management, while grid-forming inverters can provide primary-layer voltage references that support islanded operation and black-start sequences under appropriate design, protection, and validation conditions. The article concludes that future research should bridge stochastic planning and real-time physical operation through standardized interoperability frameworks, reproducible dynamic metrics, and experimentally validated control strategies for autonomous critical microgrid operation under extreme-weather conditions. Full article

Carbon sequestration is considered one of the key factors for sustainable development. The marshes of Aniva Bay (Sakhalin Island) are ecosystems currently undergoing intense natural carbon accumulation (450–930 g∙C∙m⁻²∙year⁻¹) 2–4 times higher than the average speed for similar ecosystems, making this area highly promising for the implementation of “carbon farms.” Carbon sequestration could be accelerated by installing structures on mudflats that capture suspended organic matter from tidal waters. A model experiment was conducted to assess the suitability and biocompatibility of logging waste from coniferous species (Picea ajanensis, Larix leptolepis, Abies sachalinensis) for such structures by simulating their immersion in seawater. The content of phenols and tannins in the resulting water extracts was determined, and the composition of water-soluble substances was analyzed by GC-MS. Extract toxicity was investigated using the halophilic test organism Artemia salina. The experiments revealed the release of tannins in concentrations of up to 14 mg/L, which is nearly 1.5 times the maximum permissible concentration (MPC) and could potentially negatively impact the coastal ecosystem. Furthermore, the concentration of tannins leached from L. leptolepis bark exceeded the MPC by a factor of 5.5. A critical finding is the presence of highly toxic compounds in wood waste, for which targeted analysis is absent in state regulatory documents for hazard assessment. Specifically, immersion of A. sachalinensis wood led to the leaching of juvabione into saltwater at concentrations causing 100% mortality in Artemia salina. Based on the results, the most promising species for the terraformation of mudflats is the use of P. ajanensis logging waste. Full article

A strong seismic swarm occurred in the south Aegean Sea from late January to March 2025. Here, we apply natural time analysis (NTA), which can identify the critical stage before strong earthquakes, and find that all earthquakes of magnitude five or larger have been preceded by criticality. Moreover, we study the frequency–magnitude distribution of the seismic swarm and suggest a generalization of the Gutenberg–Richter law, which is compatible with Tsallis nonextensive statistical mechanics. This suggestion is tested for different earthquake catalogs available for this seismic swarm with successful results. Since NTA has already been applied to the strongest seismic swarm ever recorded in Japan, observed in 2000 in the Izu Islands region, we apply the present suggestions to the case of another swarm observed during June 1980, which was the strongest seismic swarm in the same region since 1979. The strongest magnitude class M7 earthquake of that swarm was also preceded by criticality in the NTA. Finally, we show that earthquake nowcasting could be applied to predict in advance that no earthquake exceeding magnitude 6.5 would occur during the south Aegean seismic swarm. Full article

Urban heat islands (UHIs) significantly influence microclimatic conditions, energy consumption, and public health. This research leverages ensemble models and correlation analysis based on Landsat 5-8 satellite data to forecast LST and explore its environmental relationships. This study employed the XGBoost machine learning algorithm to model seasonal LST dynamics in three rapidly urbanizing Arkansas cities, including Fort Smith, Little Rock, and Northwest Arkansas, using Landsat imagery from 2001 to 2021. The results show significant increases in urban heat, particularly in the summer, with Fort Smith seeing an increase in the area classified in higher-temperature bins (35–45 °C) from approximately 33% in 2001 to more than 83% by 2021. Model validation showed high predictive performance (R² = 0.74–0.78, RMSE ≤1.46 °C), indicating reliable project-based estimation of spatial LST variability for 2026 and 2031. The results revealed a substantial intensification of built-up area expansion, to 9.8% by 2026 and 20.7% by 2031, accompanied by cropland reductions of 13.2% and 25.5%, respectively. This rapid urban growth is projected to elevate summer LSTs above 45 °C across more than 700 km² combined, and winter LSTs to ≥25 °C across nearly 125 km² in the region by 2031. The integration of Landsat time series data and machine learning provide valuable insights for urban planners and policymakers, underscoring the critical importance of targeted climate-resilient strategies and sustainable urban development practices. Full article

The stabilization of public infrastructure on active volcanic slopes presents significant geotechnical challenges, particularly in coastal regions exposed to extreme hydrological stressors. This paper presents a forensic diagnosis and the structural rehabilitation of the Porto da Cruz Cemetery (Madeira Island, Portugal), which suffered severe progressive failure following localized, shallow-founded interventions in 2004. Historical inclinometer data (2015–2022) revealed continuous deep-seated creep within the volcanic colluvium (Geotechnical Zone 2–ZG2) at rates up to 0.17 mm/day, triggered by basal fluvial undercutting. To mitigate these kinematic drivers, a systemic “Toe-to-Crest” stabilization paradigm was implemented. Following the hydraulic confinement of the slope’s lower boundary, a high-capacity deep foundation network—comprising 26 m rock-socketed micropiles and 600 kN active multi-strand anchors—was executed to bypass the failure plane and encastre directly into the competent basaltic bedrock (Geotechnical Zone 1–ZG1). The structural performance was validated through rigorous load testing and a real-time robotic Structural Health Monitoring (SHM) system. Post-construction telemetry confirmed absolute kinematic stabilization, maintained continuously throughout the critical execution phases and subsequent monitoring period (2024–2025). By integrating deep bedrock anchoring, pore-pressure mitigation, and digital telemetry, this case study validates the economic and geomechanical superiority of systemic subsurface bypass over reactive surface maintenance. Ultimately, it establishes a scalable, climate-adaptive engineering blueprint for safeguarding critical coastal heritage across Macaronesia against escalating environmental multi-hazards. Full article

Frequency instability caused by reduced system inertia in inverter-dominated islanded microgrids represents a critical challenge in renewable-integrated power systems. Conventional fixed-parameter controllers exhibit limited adaptability to uncertain and time-varying low-inertia conditions. This paper proposes an LSTM–MPC + VIC framework that embeds a Long Short-Term Memory (LSTM) surrogate predictor directly within a Model Predictive Control (MPC) optimisation loop, coordinated with a Virtual Inertia Controller (VIC) for immediate transient support. The LSTM provides data-driven frequency predictions without requiring precise analytical system modelling, while the VIC supplies reactive inertial damping within the same control cycle. The proposed controller is evaluated against Proportional–Integral–Derivative (PID), PSO-optimised PID, and standard MPC baselines on a 50 Hz islanded microgrid. Results demonstrate the lowest maximum frequency deviation of 0.009748 Hz, fastest settling time of 36.34 s, and minimum integral absolute error of 0.12283 Hz·s among all controllers. A Lyapunov-based Input-to-State Stability (ISS) analysis, incorporating the load disturbance term via Young’s inequality, confirms an ISS ultimate bound of 0.057866 Hz and an effective decay rate of 1.2952 s⁻¹. Robustness is further validated through multi-scenario testing, parametric sensitivity analysis, component ablation, and computational feasibility assessment, confirming suitability for real-time deployment in low-inertia microgrid systems. Full article

The subtropical and tropical islands of East Asia host a unique and highly endemic aquatic insect fauna threatened by a variety of anthropogenic stressors (e.g., invasive species, habitat fragmentation, pollution, and climate change). This review synthesizes the impacts of these stressors on aquatic insect diversity across this region based on 206 articles published over the past 40 years (1985–2025) to evaluate the impacts of these stressors on insular aquatic insect diversity. The islands of East Asia include all or parts of China, Japan, Taiwan, and South Korea. The annual number of publications demonstrates a steady upward trend over time and has been accelerating in the last decade. Our systematic analysis reveals a large geographic disparity. Research is heavily concentrated on major islands, with Honshu Island (42%) and Taiwan Island (24%) accounting for two-thirds of the total literature, while small islands (<10,000 km²) comprise only 20%. Furthermore, current research tends to focus on independent impacts of single stressors, largely overlooking the complex additive, synergistic, or antagonistic interactions that characterize stressors on these fragile ecosystems. These research gaps, compounded by a lack of long-term monitoring data (i.e., only ~22% of the studies span more than 3 years), hinder efforts to distinguish natural inter-annual variability from anthropogenic shifts. The extinction of cryptic or endemic species may occur before these species are identified and described. In addition, the disentanglement of these interactive impacts on aquatic insect communities in East Asian islands is critical for predicting ecosystem responses to further local and global changes. Identification of non-linear ecological tipping points through these long-term monitoring networks, coupled with proactive, science-guided habitat restoration, is essential to mitigate imminent extinctions and to rebuild the functional integrity of these imperiled freshwater ecosystems. Full article

Small-scale island communities whose livelihoods depend on aquaculture are increasingly vulnerable under interacting climatic and non-climatic stressors. Conventional indicator-based assessments are useful for describing the level of vulnerability, but many empirical assessments remain less able to explain how multiple stressors are mediated through local social–ecological structures and feedback processes to produce different vulnerability patterns. This study aims to explain how vulnerability is formed in island aquaculture communities by linking social–ecological system structures with vulnerability processes and by examining empirically informed feedback pathways. Drawing on evidence from three island aquaculture communities in southeastern China, household survey data were first used to classify community types through hierarchical clustering. Semi-structured interviews, field observations, and documentary materials were then qualitatively coded to develop empirically informed conceptual causal loop diagrams (CLDs) for each type. Key variables and recurring feedback pathways were identified through loop-based structural analysis and cross-case comparison. The analysis indicates that vulnerability formation in island aquaculture communities is associated with recurring reinforcing feedbacks within local social–ecological system structures, through which multiple climatic, ecological and socio-economic stressors are translated into differentiated vulnerability outcomes. Across the case communities, resource overexploitation and marine pollution reinforce an ecology–livelihood degradation loop, while labor outmigration erodes social capital, disrupts intergenerational knowledge transmission, and weakens collective action and adaptive capacity, exacerbating socio-ecological vulnerability. At the same time, dominant stressors, key drivers, and feedback configurations vary across community types, generating divergent vulnerability trajectories and highlighting the context-dependent nature of vulnerability dynamics. These results suggest that governance interventions targeting isolated stressors or relying on static vulnerability analyses are insufficient where reinforcing feedbacks dominate. Effective adaptation strategies should explicitly target critical feedback pathways and strengthen stabilizing processes. By integrating social–ecological systems thinking with vulnerability analysis, this study provides a feedback-oriented approach for diagnosing vulnerability formation and supports more feedback and context-sensitive governance in small-scale island aquaculture communities. Full article