Search Results (4,382)

Renewable energy is being increasingly integrated into power grids. As a result, the three-phase grid-connected inverter (GCI) faces power transfer limitations caused by small-signal stability issues. To improve energy utilization and enhance stability, this paper employs an impedance-based method to analyze the small-signal stability power boundary of the GCI. This boundary is then quantified using the generalized Nyquist criterion (GNC). Our analysis reveals that the power boundary decreases as the grid short-circuit ratio (SCR) decreases or the phase-locked loop (PLL) bandwidth increases. To address this problem, we propose an impedance reshaping method that cancels the negative resistance effect introduced by PLL feedforward. This approach raises the small-signal stability power limit to the rated power and ensures stable operation under grid impedance variations and high PLL bandwidth. Finally, impedance analysis and experimental verification confirm both the theoretical correctness and the practical effectiveness of the proposed method in extending the stability power boundary. Full article

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO${}_2$ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

To tackle the intermittency of renewable energy and realize the repurposing of abandoned oil and gas wells, this study proposes a compressed air energy storage (CAES)-based cogeneration system integrated with geothermal energy and abandoned oil and gas wells, and conducts a five-dimensional comprehensive analysis covering exergy, exergoeconomic, exergoenvironmental, economic and environmental performance. The optimal operating parameters are determined as air compressed to 200 bar, an ORC turbine inlet pressure of 16 bar and an inlet temperature of 110 °C. The system’s annual total power generation is 2,971,416.5 kWh during low-power daytime operation, and 20,131,785 kWh during high-power nighttime operation. Compared with conventional CAES systems, the proposed system reduces total exergy destruction by 4121.35 kW and increases exergy efficiency from 48.49% to 63.38%. Coolers, geothermal heat exchangers and compressors are the main sources of exergy destruction cost and capital investment, while COM1, HE1 and HOT1 are the key components causing environmental impacts. The system realizes cogeneration of power, hydrogen and pure water, with a static payback period of about 5.4 years and significantly reduced TEWI value at elevated turbine inlet pressure. This system achieves multi-objective synergies in energy efficiency, economy and environment, providing a feasible scheme for the green repurposing of abandoned oil and gas wells and cascaded utilization of renewable energy. Full article

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Multi-terminal voltage-source-converter-based HVDC (VSC-MTDC) systems are increasingly used to integrate renewable energy and interconnect asynchronous AC grids, but conventional fixed-coefficient droop control cannot simultaneously limit DC-voltage deviations, reduce operating losses, and preserve converter power margins during operating-point switching. This paper hypothesizes that a rule-based fuzzy adjustment of the droop slope can provide smooth multi-objective coordination without inter-station communication. A dual Mamdani fuzzy controller is developed: one controller adjusts the weighting between loss-oriented and power-margin-oriented droop coefficients according to converter power margin, while the other introduces a voltage-deviation correction according to DC-bus voltage. The controller is implemented and verified in a five-terminal MMC-based VSC-MTDC model built in PSCAD/EMTDC, where simulation data are generated under heavy-load, light-load, and power-reference switching scenarios using specified line and converter parameters. Compared with conventional droop control, the proposed strategy improves power-margin utilization, reduces operating-point discontinuities, and raises the minimum DC voltage from 370.2 kV to 381.4 kV in the severe switching case. The results confirm that fuzzy-slope droop control can achieve smoother operating-point switching and better coordinated optimization among voltage stability, operating loss, and converter reserve margin. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

Cellulose is the most promising abundant renewable polymer material with the highest potential for the future low-carbon biorefineries. However, its utilization in industry is limited by the structural recalcitrance as a result of organization of crystalline domains, fibrillar architecture hierarchy and intramolecular and intermolecular hydrogen bonding which is responsible for access restriction for the catalysts and consequent cleavage of the glycosidic bonds. Therefore, efficient depolymerization of cellulose is of paramount importance as a step in biomass conversion into the low molecular products. In this review, the recent advances in cellulose depolymerization are discussed. The chemical, enzymatic, thermal, thermochemical, mechanochemical, oxidative and hybrid catalytic method is thoroughly discussed. Attention is paid to the mechanism of the depolymerization reaction steps as glycosidic bond activation as hydrolytic, radical mediated, and energy assisted pathways. Selectivity and conversion efficiency based on substrate morphology, solvent system and catalyst design are also discussed. Further, there is a comparison of key performance metrics which are relevant for the industrial process as product yield, carbon efficiency, energy demand, stability of the catalyst, solvent recyclability and impact to the environmental lifecycle. The pros and cons of the various methods are also represented. Processes based on mineral acids enable rapid conversion. However, they suffer from corrosion, waste handling issues and degradation by-products. On the other hand, enzymatic depolymerization processes offer relatively high selectivity but they are limited in terms of feedstock sensitivity and slow reaction kinetics. The downstream valorization mechanisms are also described with the result being that no single available technology is capable of satisfying all industrial requirements. Thus, future progress expects integrated circular processes where advanced catalysis, process intensification and digital optimization strategies take place. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

The rapidly increasing global demand for high-performance thermal insulation materials necessitates a significant shift towards more sustainable and cost-effective solutions. This study unveils a novel and efficient pathway to synthesize engineered cordierite, a highly coveted magnesium aluminosilicate ceramic, by intelligently harnessing biogenic silica extracted directly from rice husk. Rice husk, an abundant agricultural by-product, represents a readily available and often underutilized resource. The methodology involved a precise precipitation method to successfully yield high-purity silica from rice husk ash. This extracted silica was then meticulously combined with commercial magnesium oxide (MgO) and aluminum oxide (Al₂O₃) through a solid-state reaction to synthesize the desired cordierite. The study systematically investigated the profound impact of various sintering temperatures, ranging from 850 °C to 1100 °C, on both the cordierite yield and its crucial physicochemical properties. Our experiments revealed that a sintering temperature of 1100 °C achieved a remarkable 66.5% cordierite yield. Beyond yield, the material processed at 1100 °C exhibited exceptional mechanical and thermal characteristics: a compressive strength of 65 kN/m², a flexural strength of 44 kN/m², a tensile strength of 17.5 kN/m², and a remarkably low thermal conductivity of just 3.2 W/m·K. These attributes match the mechanical requirements for structural insulation, with a thermal conductivity of 3.2 W/m·K. While higher than some high-porosity commercial cordierites (typically 1.2–2.0 W/m·K), the biogenic version offers a 40% reduction in production energy and utilizes 100% recycled silica, balancing thermal performance with superior sustainability. By utilizing agricultural waste, this method reduces CO₂ emissions associated with mineral extraction and minimizes reliance on non-renewable raw materials, providing a practical pathway for the circular economy. Full article

Coal mining has historically been a central economic, cultural, and social cornerstone of Appalachian communities. The decline of the coal industry, driven by technological changes, competition from natural gas and renewable energy, environmental regulations, and evolving energy markets, has created major economic and environmental challenges for coal-dependent regions. This study examines Kentucky residents’ perceptions of coal decline and how socio-demographic factors shape environmental concern. Data was collected from 685 Kentucky residents through a statewide online survey conducted in December 2023. Ordered logistic regression was used to examine the influence of gender, age, rural residence, and political affiliation on concerns regarding climate change, environmental degradation, extinction of endangered species, air pollution, and water pollution. Respondents identified health and safety concerns, cleaner energy alternatives, government incentives, and technological changes as major contributors to coal decline, while climate change was viewed as less significant. The findings also reveal support for workforce retention and training in sectors such as construction, transportation, utility work, and renewable energy. Female respondents expressed high levels of environmental concern, while rural residents and Republicans reported lower concern regarding climate change and environmental degradation. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

Background: The diagnostic value of surface electromyography (sEMG) for identifying muscles affected by myofascial trigger points (TrPs) remains controversial. However, advances in pain neurophysiology and discussions regarding TrPs within the International Classification of Diseases (ICD-11) have renewed interest in objective diagnostic approaches. Objective: To synthesize current evidence on the diagnostic utility of sEMG for detecting TrP-related muscle alterations across different electromyographic signal analysis domains. Methods: A scoping review was conducted following JBI guidance and PRISMA-ScR guidelines. PubMed, Scopus, Web of Science, CINAHL and Cochrane were searched for studies involving adults with symptomatic or asymptomatic TrPs, myofascial pain syndrome, or TrP-related referred pain. Fifteen studies met the inclusion criteria. Analyses included amplitude-, frequency-, time–frequency-, and spatial-domain sEMG parameters. Results: Muscles affected by TrPs showed increased resting electromyographic activity and reduced activation during maximal voluntary contraction in several studies. Frequency domain analyses indicated changes in median frequency and muscle fatigue index, whereas time–frequency analyses suggested redistribution of sEMG signal energy toward lower-frequency components or altered spectral power during experimentally provoked referred pain. Spatial analyses revealed altered activation patterns, although these findings did not consistently correspond with TrP anatomical locations. Overall, the limited number of studies assessing diagnostic sensitivity and specificity prevents firm conclusions. Conclusions: sEMG may be useful as a non-invasive complementary tool for functional assessment and monitoring of TrP-related muscle dysfunction. However, current evidence does not support its use as a standalone diagnostic method. Time–frequency, machine learning-supported and spatial analyses appear promising for future clinical research, but standardized protocols and external validation are required before clinical diagnostic criteria can be proposed. Full article

The growing share of intermittent renewable electricity has increased the need for long-duration storage in industrial energy systems. Meanwhile, many industrial processes still release recoverable low-grade waste heat. Introducing this heat into pumped thermal energy storage (PTES) can improve thermal integration, but industrial waste heat is often unsteady, and its temperature and mass flow fluctuations may disturb the charging process. This study investigates an air-based reverse Brayton PTES system assisted by an industrial hot-water waste heat stream of approximately 100 °C. A dynamic model was developed in Simulink/Simscape. The shaft speed is fixed at 3000 rpm, and a PID controller regulates the molten-salt flow rate to maintain the thermal storage temperature. The results show that increasing the waste heat temperature from 95 °C to 105 °C mainly changes the charging-side heat distribution. The waste heat utilization power increases from 36.0 MW to 37.9 MW, while the regenerator power decreases from 126.8 MW to 122.0 MW. The thermal storage power increases slightly from 117.0 MW to 119.0 MW, with the mechanical input fixed at 81.0 MW. The influence of waste heat temperature is concentrated near the low-temperature heat exchanger, regenerator, and turbine outlet. Under dynamic disturbances, faster temperature ramps increase short-term deviations, but the PID-based molten-salt flow regulation keeps the storage temperature close to 550 °C, indicating that the proposed control strategy can suppress moderate thermal disturbances during charging. When waste heat temperature and mass flow rate vary together, same-direction changes strengthen the disturbance, whereas opposite-direction changes partly offset it. These results clarify the disturbance propagation mechanism of fluctuating industrial waste heat in the PTES charging loop and provide a basis for the dynamic design and temperature-control strategy of waste-heat-assisted PTES systems. Full article

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article