Search Results (27,545)

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

Mozambique’s rapidly urbanizing landscape presents both opportunities and challenges for achieving Sustainable Development Goals (SDGs) 7 and 11, which aim to ensure access to clean energy and sustainable cities. This study employs the HESPECT analytical framework—emphasizing Historical, Economic, Social, Political, Ecological, Cultural, and Technological dimensions of the energy context—to examine the factors shaping renewable energy transitions in Mozambican cities. The analysis reveals a dual dynamic: facilitating factors such as abundant solar and wind potential, expanding urban energy demand, and growing policy support; and inhibiting factors including deforestation-driven ecological stress, poverty, infrastructural deficits, and uneven access to technology and education. By linking renewable energy development to urban planning, service delivery, and social inclusion, the study underscores how energy systems shape the sustainability and livability of Mozambique’s cities. The paper concludes that advancing Mozambique’s renewable energy agenda requires targeted interventions to mitigate constraints while leveraging enabling factors to strengthen institutional capacity, enhance social inclusion, and accelerate progress toward guaranteeing clean and affordable energy to all (SDG 7) and livable, sustainable cities (SDG 11). Full article

Supply–demand balance in wind–solar dominant energy transition is challenged by the volatility of wind–solar power. Complementarity of wind–solar power has been introduced to suppress this volatility. Although multiple indices have been developed to quantify complementarity, a quantitative index with explicit physical meaning remains lacking. Additionally, complementarity’s temporal stability, which is imperative for wind–solar site selection, is unclear. In this study, these knowledge gaps are closed through developing a Daily Complementarity Index of wind–solar generation (DCI) and a nuanced national assessment of complementarity in China. The results of the comparison of our index with existing indices and site validation confirm the reasonability of the DCI and its improvements in interpretability. The average DCI of China ranges from 0.06 to 0.88, with a pronounced low-DCI zone across the Sichuan Basin and Chongqing municipality, and a high–DCI zone along the Three-North Shelterbelt. Temporally, the complementarity of wind–solar power in China follows a slight increase trend (3.96 × 10⁻⁵ year⁻¹), with evident seasonal characteristics, in which the highest and lowest are 0.37 and 0.17, respectively. This study introduces an effective tool for quantifying complementarity, and these findings can offer valuable reference for China’s renewable energy transition. Full article

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R² = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R² = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R² across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources. Full article

Ensuring the long-term sustainability of mining and post-mining practices is crucial for balancing resource extraction with environmental and social responsibilities. This study critically examines the role of science in addressing the complex challenges posed by mining, particularly in the context of the Sustainable Development Goals (SDGs). It identifies key responsibilities for science, including the development of sustainable extraction technologies, innovative land reclamation and ecosystem restoration strategies, and equitable frameworks for resource distribution that prioritize affected communities. The study emphasizes the importance of interdisciplinary approaches, the concept of Responsible Research and Innovation (RRI), and effective knowledge dissemination to minimize adverse impacts while enhancing mining’s contribution to renewable energy transitions. By exploring the interplay between mining, renewable energy, and sustainable development, this study underscores the transformative potential of science to balance humanity’s resource needs with ecological preservation and social equity. The findings offer actionable insights for aligning mining practices with sustainability principles, fostering resilience and equity in mining-impacted regions. Full article

The growing demand for renewable fuels has renewed interest in biodiesel production, prompting exploration beyond conventional reactors. This review assesses three-dimensional (3D) printed microreactors for biodiesel synthesis via transesterification, with a focus on their potential for enhanced process efficiency, sustainability, and modular deployment. Compared with conventional batch and stirred-tank reactors, 3D-printed microstructured systems often offer superior mass and heat transfer, enabling biodiesel yields up to ~99% in some studies, with critically short residence times (e.g., as low as ~5 s) and reported energy reductions of 60% to 90% under optimal conditions. Optimized configurations in recent work achieved energy requirements as low as ~0.05 to 0.12 kWh L⁻¹, substantially lower than the typical 0.25 to 0.60 kWh L⁻¹ for conventional setups. However, existing studies remain limited in number and scope: issues such as catalyst leaching, chemical and thermal stability of printing materials, dimensional inaccuracies, and scalability of microreactor networks remain under-investigated. Long-term durability, real-world feedstock variation (e.g., high-FFA waste oils), and comprehensive lifecycle assessments are often lacking, limiting confident extrapolation to industrial scale. Despite these challenges, the emerging evidence suggests significant promise for 3D-printed microreactors as a pathway toward modular, energy-efficient, and potentially low-carbon biodiesel production, provided that future work addresses their practical limitations and validates performance under industrially realistic conditions. Full article

The massive influx of pelagic Sargassum in the Caribbean poses a serious environmental, social, and economic problem, as the stranded biomass is often treated as waste and deposited in landfills. This literature review synthesizes recent research highlighting its potential for valorization in various industries, turning this challenge into an opportunity. Sargassum has low levels of protein and lipids. Still, it is particularly rich in carbohydrates, such as alginates, fucoidans, mannitol, and cellulose, as well as secondary metabolites, including phenolic compounds, flavonoids, pigments, and phytosterols with antioxidant and bioactive properties. These biochemical characteristics allow for its application in renewable energy (bioethanol, biogas, biodiesel, and combustion), agriculture (fertilizers and biostimulants), construction (composite materials, cement additives, and insulation), bioremediation (adsorption of heavy metals and dyes), and in the health sector (antioxidants, anti-inflammatories, and pharmacological uses). A major limitation is its high bioaccumulation capacity for heavy metals, particularly arsenic, which increases environmental and health risks and limits its direct use in food and feed. Therefore, innovative pretreatment and bioprocessing are essential to mitigate these risks. The most promising approach for its utilization is a biorefinery model, which allows for the sequential extraction of multiple high-value compounds and energy products to maximize benefits, reduce costs, and sustainably transform Sargassum from a coastal pest into a valuable industrial resource. Full article

To enhance grid stability with high renewable energy penetration, this paper proposes an adaptive virtual synchronous generator (VSG) control using a backpropagation neural network (BPNN). Traditional VSG control methods exhibit limitations in handling nonlinear dynamics and suppressing power oscillations. Distinguishing from existing studies that apply BPNN solely for damping adjustment, this paper proposes a novel strategy where BPNN simultaneously regulates both VSG virtual inertia and damping coefficients by learning nonlinear relationships among inertia, angular velocity deviation, and its rate of change. A key innovation is redesigning the error function to minimize angular acceleration changes rather than frequency deviations, aligning with rotational inertia’s physical role and preventing excessive adjustments. Additionally, an adaptive damping coefficient is introduced based on optimal damping ratio principles to further suppress power oscillations. Simulation under load disturbances and grid frequency perturbations demonstrates that the proposed BPNN strategy significantly outperforms constant inertia, bang–bang, and radial basis function neural network methods. Full article

This study investigates the feasibility of absolute decoupling—where economies expand while CO₂ (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization. Full article

Understanding the spatiotemporal evolution of forest biomass energy potential is essential for supporting low-carbon land-use planning and regional energy transitions. China, characterized by pronounced spatial heterogeneity in forest resources and ecological conditions, provides an ideal case for examining how biophysical endowments and management factors shape biomass energy potential. This study constructs a province-level panel dataset for China covering the period from 1998 to 2018 and investigates long-term spatial patterns, regional disparities, and driving mechanisms using spatial visualization, Dagum Gini decomposition, and fixed-effects estimation. The results reveal a gradual spatial reorganization of forest biomass energy potential, with the national center of gravity shifting westward and northwestward, alongside a moderate dispersion of high-potential clusters from coastal areas toward the interior. Interregional transvariation is identified as the dominant source of regional inequality, indicating persistent structural differences among major regions. To explore future dynamics, a random forest model is employed to project provincial forest biomass energy potential from 2018 to 2028. The projections suggest moderate overall growth, smoother distributional structures, and a partial reduction in extreme provincial disparities. Central, southwestern, and northwestern provinces are expected to emerge as important contributors to future growth, reflecting ecological restoration efforts, expanding plantation forests, and improved forest management. The findings highlight a continued upward trend in national forest biomass energy potential, accompanied by a spatial shift toward inland regions and evolving regional disparities. This study provides empirical evidence to support region-specific development strategies, optimized spatial allocation of forest biomass resources, and integrated policies linking ecological sustainability with renewable energy development. Full article

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy. Full article

The TEAPOTS project aims to meet farmers’ waste management needs by converting agricultural waste into renewable energy and, in parallel, plant biostimulants. Surveys conducted in Germany, Greece, and Italy identified farmers’ waste management practices and their willingness to participate in the TEAPOTS Integrated Solution (TIS). Results show general interest in providing waste to TIS owners. Financial returns and soil improvement are key motivators, with the logistics of waste collection and transfer emerging as major challenges. The study highlights the potential of TIS while emphasizing the need for logistics solutions and enhanced pro-environmental attitudes. Full article

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP₉₀ = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

This paper addresses stability issues, including voltage fluctuation, a frequency offset, and broadband oscillation resulting from the high penetration of renewable energy in a photovoltaic high-permeability distribution network. This paper proposes an active support control strategy which is energy storage grid-connected based on a virtual synchronous generator (VSG). This strategy endows the energy storage system with virtual inertia and a damping capacity by simulating the rotor motion equation and excitation regulation characteristics of the synchronous generator, and effectively enhances the system’s ability to suppress power disturbances. The small-signal model of the VSG system is established, and the influence mechanism of the virtual inertia and damping coefficient on the system stability is revealed. A delay compensator in series with a current feedback path is proposed. Combined with the damping optimization of the LCL filter, the instability risk caused by high-frequency resonance and a control delay is significantly suppressed. The novelty lies in the specific configuration of the compensator within the grid–current feedback loop and its coordinated design with VSG parameters, which differs from traditional capacitive–current feedback compensation methods. The experimental results obtained from a semi-physical simulation platform demonstrate that the proposed control strategy can effectively suppress voltage fluctuations, suppress broadband oscillations, and improve the dynamic response performance and fault ride-through capability of the system under typical disturbance scenarios such as sudden illumination changes, load switching, and grid faults. It provides a feasible technical path for the stable operation of the distribution network with a high proportion of new energy access. Full article