Search Results (15,522)

High-value components in the organic fraction of both municipal and industrial waste are still underused. In fact, there are two components in organic matter with high energy and emission value: carbohydrates (up to 46%) and fats (3.9–25%). The technological potential of using an integrated sequential biorefinery route, including lipid extraction for HVO/SAF, carbohydrate fermentation for bioethanol, and pyrolysis for renewable hydrogen generation, is not fully exploited. The objective of this work is to propose an approach based on a systematic six-step engineering methodology to determine the feasibility of its recovery. This integrated strategy achieves an attractive economic performance, with payback periods between 1.97 and 3.00 years, significantly outperforming traditional waste-to-energy models such as anaerobic digestion or composting. While current green hydrogen production costs range from USD 4.28 to USD 6.86, our model positions urban waste as a competitive feedstock for energy transition, achieving a selling price of 4.84 EUR/kg at midpoint values. For the remaining organic matter, a definitive thermal barrier for the 100% removal of microplastics is proposed, to prevent them from reaching agricultural soils. At the same time, efficient waste characterization, aligned with the European RED III directive, will allow the identification of high-value products and the application of the best available techniques for their extraction and use. Full article

Over the years, industrial demands have determined the main course of electric drives research and development. Modern drive trains are forced to provide extremely efficient operation under a variety of unfavorable circumstances. Moreover, the maintenance of the drive is often a critical factor, including both its reliability in the long-term perspective and deployment costs. In addition, the sophistication of up-to-date industrial machinery increases the number of stochastic disruptions that affect the final control quality. Thus, the Control Theory satisfies the need for a novel, robust strategy by proposing the Active Disturbance Rejection Control (ADRC) algorithm. It stands out with great dynamic performance and versatility. It has been widely tested in a variety of different industrial applications, including aviation, autonomous and unmanned vehicles, marine robots, automotive solutions, renewable energy, and power systems. Many of the above-mentioned applications use electric drive units. This paper elaborates on the review of the current state-of-the-art in the field of electric drive control with the ADRC strategy employed. Then, the ADRC designs regarding multi-mass drive trains are reviewed with emphasis on the speed control issue. This paper evaluates its variants and control approaches depending on the application purpose. Moreover, an exemplary dynamic properties analysis is performed to verify the default effectiveness of the algorithm. Then, the summary section is followed by an indication of possible future research directions. Full article

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% renewable energy sources. The model was tested using a techno-economic assessment applied to the Bravar-Jasenice case study in Croatia featuring 12 MW of solar PV, 10 MW of wind power, and a 9.3 MW biogas cogeneration plant. This integrated approach can achieve 80–90% energy self-sufficiency and reduce electricity expenditures for participating enterprises by approximately 15%. Furthermore, the system facilitates an annual reduction of roughly 20,000 tonnes of CO₂ emissions, thus directly supporting European Green Deal objectives. The study also highlights the potential for industrial symbiosis, including green hydrogen production, data centre integration, and waste heat recovery. Ultimately, the proposed framework provides a robust strategy for enhancing industrial competitiveness and ensuring energy security through localized, sustainable energy management. Full article

As renewable energy source-based distributed generation is more widely connected to the grid, stable current control and power quality improvement in grid-connected inverters (GCIs) become more important. To satisfy increasing power demand, multi-inverter systems connected to the grid in parallel are being widely adopted. However, parallel operation may degrade current quality and stability because of inverter interactions under harsh grid conditions. In particular, grid voltage harmonics, voltage imbalance, and frequency variations can also impair current control performance and system stability. To address these concerns, a frequency-adaptive current controller integrated with a Kalman filter (KF)-based observer is developed to ensure a stable operation of multiple GCIs. Moreover, a stability evaluation is presented for multi-inverter systems by using admittance-based stability analysis. A Kalman filter-based state observer is applied to improve the estimation accuracy under noisy measurement conditions. In addition, a moving average filter-based phase-locked loop (MAF-PLL) is applied to improve the detection accuracy and reliability of the grid frequency and phase angle under harsh grid conditions to ensure an effective frequency-adaptive control design. The effectiveness and performance of the proposed current controller are assessed through the PSIM simulations. The simulation results show that the MAF-PLL reduces the maximum frequency fluctuation from $\pm$7 Hz to $\pm$1.1 Hz. In addition, the KF-based observer reduces the RMS estimation error to 0.0001 A. On the other hand, those values are 1.3 A with the conventional observer and 0.0003 A with the LQR-based observer, respectively. The practicality of the proposed scheme is also confirmed experimentally using 2 kW parallel multiple GCI prototype systems under harsh grid conditions. Full article

The growing demand for efficient and sustainable materials for air purification has stimulated interest in activated carbons derived from renewable biomass resources. In this study, activated carbons were prepared from Rice Husk, Wheat Straw, Sawdust, and Walnut shells and systematically investigated as sorbents for toxic gases and volatile organic compounds. The materials were characterized using nitrogen and water vapor sorption isotherms, scanning electron microscopy, thermogravimetric analysis, Fourier-transform infrared spectroscopy, energy-dispersive X-ray and XPS analysis to evaluate their textural properties, morphology, thermal stability, and surface chemistry. The results showed that the precursor type strongly influences the pore structure and functional group composition of the activated carbons. Wheat straw and Rice Husk-derived activated carbons exhibited the highest total pore volume and a well-developed porous structure, together with a high content of oxygen- and silicon-containing elements. Gas breakthrough experiments with different probes showed that Wheat Straw-derived activated carbon excels in non-polar VOC—cyclohexane removal due to its highly microporous structure. In contrast, Rice Husk-derived activated carbon displays strong affinity toward inorganic gases such as NH₃ and, after urea modification, achieves enhanced performance for SO₂. These results underscore the versatility and practical applicability of carbon materials obtained from plant residues. Full article

This study conducts a comprehensive investigation into prospective policy alternatives within Pakistan’s power sector using the PAK-TIMES model, targeting the critical challenges of energy scarcity and environmental degradation. Focused on the period from 2022 to 2050, the research evaluates the impact of various policies on energy consumption, supplies, carbon emissions, and expenditures in alignment with Pakistan’s Nationally Determined Contributions (NDC) directed at combatting climate change. The study explores three distinct scenarios: a business-as-usual (BAU) scenario, along with five policy (5% Eff, 10% Eff, 15% REN, 30% REN, 50% REN) scenarios categorized into energy efficiency and renewable integration. The first scenario concentrates on the deployment of energy-efficient devices, while the second scenario delves into diverse levels of renewable energy integration. Key results reveal that energy demand is projected to surge substantially under the BAU scenario, increasing significantly from 3459 PJ in 2022 to 7912 PJ by 2050. In contrast, scenarios prioritizing energy efficiency can potentially curb the total energy supply by 2.3%, while renewable energy integration can expand up to 1.3% compared to business-as-usual by 2050. These alternative scenarios also exhibit the potential to slash greenhouse gas (GHG) emissions from the power sector by up to 15%. Notably, the PAK-TIMES model emerges as a valuable decision support tool for the Pakistani government to facilitate the execution of energy efficiency and renewable energy policies aimed at fulfilling its NDCs, while also contributing to the fulfillment of Sustainable Development Goals (SDGs) 7 (affordable and clean energy) and 13 (climate action). The study underscores the pivotal role of policy interventions in simultaneously mitigating energy challenges and combatting climate change for sustainable development. Full article

Chlorophyll, a natural photosynthetic pigment, is gaining interest for its sustainable and eco-friendly applications in renewable energy, particularly as a photosensitizer in dye-sensitized solar cells (DSSCs). This study investigates the feasibility of chlorophyll extracted from Moringa oleifera as a natural photosensitizer in DSSCs, building on our previous work demonstrating its high chlorophyll content and long-term stability. Chlorophyll was extracted using acetone under optimal conditions (45 °C, 60 min) and applied in DSSCs comprising a TiO₂ photoanode, iodide/triiodide electrolyte, and platinum counter electrode. The TiO₂ photoanode was characterised using UV-Vis spectroscopy, FE-SEM, XRD, and Raman spectroscopy, confirming the presence of pure anatase phase TiO₂ with uniform spherical nanoparticle morphology. The fabricated DSSCs achieved a short-circuit current density of 0.197 mA cm⁻², an open-circuit voltage of 0.44 V, a fill factor of 32%, and a photoconversion efficiency (PCE) of 0.027%. While this performance is lower than the highest reported chlorophyll-based DSSC efficiency (4.6%), the results demonstrate that M. oleifera is a viable and sustainable source of chlorophyll for DSSC applications. The findings highlight the importance of dye–semiconductor interactions and suggest that further optimisation through co-sensitization, TiO₂ surface modification, and improved dye anchoring could enhance device performance. Full article

Modern power systems require increasingly precise forecasts of electricity consumption, which are crucial for effective planning, reducing the risk of power shortages, and integrating renewable energy sources. This article presents the results of comparative benchmarking studies on short-term load forecasting (STLF) at a 15 min resolution. The study uses the rolling-window training method, and the results were compared with the official forecasts of the Transmission System Operator (TSO). The analysis is based on time series and machine learning methods, with the aim of improving the operational accuracy necessary for stable and secure management of the power management system. Full article

Wind turbines are critical components in renewable energy systems, where early fault detection is essential to ensure reliable operation and reduce maintenance costs. Vibration-based monitoring using multichannel signals provides rich information about the dynamic behavior of the system, although it also introduces challenges related to high dimensionality and feature redundancy. This paper proposes a machine learning-based methodology for fault classification that combines Discrete Wavelet Transform (DWT) for time–frequency feature extraction with Linear Discriminant Analysis (LDA) for dimensionality reduction within a structured processing pipeline. The approach incorporates a Group K-Fold cross-validation strategy to prevent data leakage and ensure a reliable evaluation when working with segmented signals. Experimental results show that the proposed framework achieves high classification performance, reaching a mean accuracy of $98.84\pm 1.16\%$ and a weighted F1-score of $0.9905\pm 0.0089$ using a Support Vector Machine (SVM) classifier over five Group K-Fold splits. The results also indicate that dimensionality reduction plays a critical role in improving class separability, having a greater impact than the specific choice of wavelet transform. Findings demonstrate that the proposed DWT–LDA-based approach provides an effective solution for rotor imbalance detection in the laboratory-scale wind turbine evaluated in this study. Full article

This study poses the following research question: Where and how can low- to medium-enthalpy geothermal resources in Northern México be harnessed to promote a territorially anchored, socially inclusive energy transition? Hence, the potential contribution of geothermal direct uses to sustainable local development in Sonora—one of México’s largest and most economically diverse states—is examined in this article. In Sonora, a semi-arid region with dispersed populations and underutilized geothermal resources, the research integrates spatial analysis and socio-territorial indicators to identify areas where geothermal direct uses can deliver inclusive development benefits. Thermal data of 88 thermal springs and 36 wellbores were examined, in which in situ temperatures and geothermal gradients were found from 30 to 80 °C and 20–200 °C/km, respectively. This resulted in a catalog of 28 direct uses based on the energy needs and demands of the population near the sites. Then, a composite methodological framework was developed that combined the Geothermal Suitability Index (GSI), the Socio-Productive Energy Demand Index (SPEDI), and the Territorial Vulnerability Index (TVI). These indices and the catalog were overlaid to detect municipalities where high geothermal potential, energy needs, and social vulnerability intersect. Results show that sites such as Bacadehuachi, Cajeme, and Fronteras offer high-priority opportunities for agri-food processing, aquaculture, and heating/cooling applications. The findings contribute to broader debates on rural energy access, energy justice, and decentralized planning, providing evidence-based guidance for policy design that aligns renewable energy deployment with regional equity and resilience goals. Full article

With the widespread integration of renewable energy, microgrid environmental economic dispatch (EED) faces challenges such as uncertainties in wind and solar power outputs and multi-objective conflicts. This paper proposes a stochastic expected dispatch framework based on an improved multi-objective dung beetle optimization algorithm (MO-CLDBO). First, considering both wind–solar uncertainties and demand response, a Gaussian Copula function is employed to characterize the 24-h temporal correlations among wind speed, solar irradiance, and load, and typical scenarios are generated via Monte Carlo sampling and simultaneous backward reduction; a time-of-use demand response model is also introduced. Second, taking expected operational cost and environmental emission as dual objectives, three improvements are proposed to address the issues of uneven initial population, easy local convergence, and Pareto front collapse in the standard dung beetle algorithm: a Folded Two-Dimensional Modified Coupled Logistic-Sine Map (Folded 2D-MCLSM) is used to initialize a high-quality population, a non-dominated sorting mechanism is introduced, and a dynamic lens imaging backward learning strategy is designed. Finally, the proposed algorithm is compared with several classical algorithms in the mathematical model of microgrid optimal dispatch through 50 independent runs. Experimental results show that the improved dung beetle optimization algorithm achieves not only the lowest average operating cost, but also the best hypervolume (HV) indicator, demonstrating excellent comprehensive performance in multi-objective search convergence and solution set diversity. Full article

Rare earth elements (REEs), including yttrium, scandium and lanthanides, are essential for advanced technologies, particularly in electronics, defense and renewable energy systems. The main primary REE sources include bastnaesite, monazite and ion-adsorption clays, while secondary sources comprise permanent magnets, phosphors, LEDs and other technological waste. The growing demand, together with China’s dominant position in the global REE market and export restrictions, has increased concerns regarding the security of the REE supply in the European Union. This study evaluates selected primary REE resources and their processing possibilities using hydrometallurgical methods, with an emphasis on the thermodynamic aspects of REE leaching. The research focuses on the construction and analysis of Eh–pH diagrams generated using HSC Chemistry software to predict the stability of dissolved and solid species under different leaching conditions. These diagrams help identify suitable conditions for selective REE extraction and improve the understanding of the mechanisms governing hydrometallurgical processing. The results provide insight into the stability regions of REE species and indicate favorable conditions for selective leaching and recovery. Full article

Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small Island Developing States (SIDS). This paper develops a market-based framework for the co-optimization of energy and operating reserves in low-inertia island power systems, in which reserve requirements are established using historically observed extreme generation or load deviations that represent operationally validated high-risk system conditions, while reserve allocation and pricing emerge from the co-optimization process. By relying on observed operational variability, the proposed approach avoids explicit probabilistic uncertainty modeling while retaining sensitivity to system stress conditions. The approach is evaluated using a stylized island power system representative of Caribbean SIDS. Results show that reserve requirements are highly sensitive to operating conditions, reaching up to 26.7% of demand under high variability and significantly exceeding conventional fixed reserve criteria. The framework reduces non-served energy, improves reserve allocation efficiency, and generates scarcity-consistent reserve prices under stressed conditions. These findings demonstrate that the proposed methodology provides a practical intermediate solution between deterministic and probabilistic reserve sizing approaches while remaining suitable for data-constrained island power systems. Full article

This study evaluates the resource, technical, economic, and CO₂ mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, and CMIP6 climate outputs, we estimate renewable-energy potentials through a consistent national-scale screening framework and cost–supply curve analysis. The results show clear spatial heterogeneity among the three energy sources. Hydropower potential is concentrated mainly in the Yangtze River basin, Pearl River basin, and Southwestern International Rivers. Wind-power potential is relatively high in northwestern, northeastern, and plateau regions, while PV potential is particularly large in northwestern, northern, northeastern, and selected southeastern regions. Under the adopted assumptions, PV shows the largest resource and technical potential, followed by wind power and hydropower; however, this ranking reflects resource potential rather than comprehensive deployment superiority. Practical development is also constrained by ecological flow requirements, land-use competition, grid integration, storage demand, transmission capacity, curtailment risk, and regional demand matching. The findings provide a national-scale comparative reference for renewable-energy planning and CO₂ mitigation, while highlighting the need for future work that incorporates dynamic land use, system-level integration costs, detailed turbine or power-curve modeling, and dynamic grid-emission factors. Full article

This paper evaluates the potential for green hydrogen production in Tunisia using nearly 3 GW of renewable electricity distributed across four strategically selected sites: Haouaria, Zriba, Sbikha, and Feriana. These locations were chosen for their proximity to the Trans-Mediterranean (TransMed) natural gas pipeline linking Algeria to Italy, as well as their strong but underexploited solar and wind energy resources. Each site was optimized according to land availability and renewable energy potential: Haouaria is wind-dominant, Zriba employs a hybrid solar-wind configuration, Sbikha focuses on solar, and Feriana integrates both solar and wind over a large area. The analysis reveals a total green hydrogen production capacity supported by approximately 3.1 GW of installed renewable power, with a base-case LCOH ranging from $1.21 to $2.05 per kilogram. El Haouaria emerges as the most cost-effective site due to its highly favorable wind conditions, while the sensitivity analysis shows that LCOH can reach up to approximately $3.8 per kilogram under higher CAPEX assumptions. The findings underscore the viability of a multi-site development strategy and highlight northern Tunisia’s comparative advantage for low-cost green hydrogen production, thanks to its superior resource mix, existing infrastructure, and better water availability relative to Tunisia’s southern regions. Full article