Search Results (30,590)

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, LCOE, and the environmentally adjusted cost of electricity, LCOE_env, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article

Active control of multi-mode light–matter interactions is crucial for advancing quantum photonic technologies. Although triple-mode plasmon–exciton systems involving two distinct excitonic transitions offer a pathway to multi-level polaritonic states, achieving reversible electrical tuning at room temperature remains challenging. Here, we numerically investigate an electrically tunable triple-mode strong-coupling system comprising a J-aggregate-coated Au@Ag nanorod coupled with monolayer WS${}_2$. The simulated spectra show a UPB–LPB energy separation of approximately 239 meV near the zero-detuning condition. A modest gate voltage (2.0 V to 3.8 V) selectively modulates the middle and lower polariton branches over ∼46 meV, while the upper branch remains largely unaffected. This selective control is elucidated via a triple-mode coupled-oscillator model and Hopfield coefficient analysis, linking the polariton response to the excitonic composition. These results establish a framework for electrically reconfigurable multi-level polaritonic devices, offering potential for ultracompact optical modulators, high-sensitivity multiplexed sensors, and programmable quantum photonic circuits. Full article

The aim of this study is to provide a preliminary assessment of the feasibility of using a three-arm buoy as a small-scale point-absorber wave energy converter under the moderate hydrodynamic conditions of the Baltic Sea. The analysed concept combines an axisymmetric three-floater geometry with two energy-conversion pathways: an electric generator and a pneumatic energy-storage subsystem based on compressed air. The study defines the geometrical and buoyancy parameters of the structure and applies two complementary modelling levels: a simplified screening-level energy estimate and a first-order heave-response model. The extended analysis includes the influence of effective operational density, added mass, PTO damping, conversion-path efficiency, heave RAO and hydrostatic stability. The baseline screening estimate indicates that the total daily energy output may amount to approximately 0.409 kWh under average wave conditions and approximately 0.920 kWh for higher waves. The first-order heave-response model shows that, for an assumed electrical conversion efficiency of 10%, the daily electrical energy estimate ranges from approximately 0.88 kWh/day for the lightweight configuration to approximately 4.12 kWh/day for the most heavily ballasted analysed case. The RAO analysis indicates that increasing the operational mass shifts the natural period towards longer wave periods, although the system remains outside resonance tuning for the reference wave period of 6 s. The hydrostatic analysis indicates that the three-arm configuration increases the waterplane second moment of area compared with a single circular buoy of the same waterplane area and provides a more directionally balanced stability response. The results should be interpreted as conceptual and parametric estimates rather than experimentally validated wave-to-wire performance. Further work should include BEM/CFD-based hydrodynamic coefficients, irregular-wave modelling, multi-degree-of-freedom dynamics, mooring-system coupling and laboratory validation. Full article

Supplying electricity, heat and cold is an essential part of the development of more sustainable city districts. Previous studies have illustrated interest in using the exergy efficiency concept to decompose the problem and rank the technology combinations for heating or cooling according to their overall efficiency. This methodology is extended herein to include network losses that depend on the temperature level and grid losses that play a role when comparing electricity supplied by the grid rather than by local cogeneration units. This extended method is then applied by considering two emerging technologies. The first is very-low-temperature district heating and cooling (DHC), directly supplying air-conditioning needs via simple heat exchangers, as well as heating needs via local heat pumps. It is part of what are called fifth-generation DHCs or “anergy networks” and based on water or, better, on CO₂ heat-transfer fluid. The main features of the five generations of networks, as well as the average aggregate user needs, are summarized in pinch technology composites, but replacing the temperature axis with a heat exergy axis to graphically highlight exergy losses. The average aggregate heating and cooling needs of users result from the application of a geographic information system to a district in a real city. The second emerging technology considers hybrid SOFC–GT cogeneration units, with or without CO₂ separation, supplying electricity to all network users, including decentralized heat pumps, while optimizing the recovery of waste heat. The full synergy between heat providers and users is highlighted, allowing districts without cooling towers or chimneys except at one energy balancing plant. Integration of the advanced SOFC–GT cogeneration unit considered herein into the same anergy network allows an increase in exergy efficiency from 13.6% to 21.3%, as compared with electricity being supplied entirely from the grid and produced with a similar natural gas fuel. Full article

This study evaluates the spatial and seasonal feasibility of residential PV integration across 52 Spanish municipalities representing the country’s main urban areas. The assessment is based on the normalized photovoltaic sizing indicator (A_PV,N), defined as the PV area required to offset electricity demand per square metre of conditioned floor area. Simulations were performed under current climate conditions and future projections for 2050 and 2100 using the RCP4.5 scenario. Results reveal strong climatic and seasonal contrasts. Under current conditions, annual PV generation offsets approximately 17–18% of residential electricity demand. Southern and Mediterranean municipalities show the highest feasibility, with annual A_PV,N values of approximately 2–2.5, whereas northern and inland regions present severe winter limitations, with A_PV,N values frequently exceeding 15–20. Summer is the most favourable season, with PV systems covering more than 50% of seasonal demand in several southern municipalities. Future climate projections indicate a progressive improvement in PV feasibility. Under RCP4.5, annual A_PV,N decreases by approximately 5–10% by 2100, while the production-to-consumption (P/C) ratio improves by about 15–20% relative to present conditions, mainly due to reduced heating demand. The results demonstrate that future climate conditions may improve the viability of residential PV systems in Spain, particularly in southern and coastal urban areas, while northern regions will remain constrained during winter. The study provides quantitative benchmarks for climate-sensitive PV planning and long-term urban energy strategies. Full article

The rapid expansion of wind energy as a key pillar of sustainable electricity generation has intensified the need for reliable and efficient wind turbine operation, particularly in minimizing failures of critical components such as gearboxes, which significantly impact maintenance costs, downtime, and overall lifecycle sustainability. This study proposes a vibration-based fault diagnosis framework integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN) for wind turbine gearbox condition monitoring. The approach decomposes non-stationary vibration signals into Intrinsic Mode Functions (IMFs) to capture meaningful oscillatory characteristics, which are then processed through parallel multiscale convolutional branches to learn both transient and long-term signal patterns. Experimental validation using the NREL Gearbox Reliability Collaborative dataset demonstrates that the proposed CEEMDAN-MSCNN model demonstrates strong performance compared to conventional machine learning methods and single-scale CNN architectures, achieving 99.50% accuracy on an unseen holdout dataset. The proposed framework supports predictive maintenance strategies by enabling reliable fault diagnosis, reducing unplanned downtime, and improving the operational efficiency and long-term sustainability of wind energy systems. Full article

This article describes the study of electric drive performance with a basic scalar control algorithm for an induction motor affected by wear and tear and specific inverter characteristics. The deterioration or defect pattern of the electric motor is represented as a resulting change in the magnetizing inductance. We cover methods of mathematical and simulation modeling, along with an analysis of the equivalent circuit parameters of an induction motor according to its technical specifications. The mathematical and simulation models of three inverter configurations are shown, both with and without distortion and voltage drop. The influence of each factor on output signal waveforms is evaluated. Laboratory bench tests were conducted, proving the adequacy and reliability of the models. The simulation and experimental results support the hypothesis that the energy characteristics of an electric drive can be preserved during degradation, taking into account the specifics of the control system and the inverter. We outlined the main conclusions and provided practical recommendations for applying each of the considered inverter models in electric drive systems. Full article

Limited access to clean water and reliable electricity infrastructure remains a major challenge in many remote regions of Indonesia, particularly for building-scale domestic use. Conventional water treatment systems are often constrained by high operational costs and dependence on grid power, highlighting the need for sustainable and autonomous infrastructure solutions. This study presents the design, development, and performance evaluation of an integrated solar-powered clean water treatment system for smart building applications in remote areas using a Research and Development (R&D) approach. The proposed system combines off-grid polycrystalline photovoltaic panels with a multi-stage water treatment process consisting of a floss (mud) filter, activated carbon filter, water hyacinth cellulose bio-filter, ultraviolet (UV) sterilization unit, storage tank, and an IoT-based real-time water quality monitoring system. System performance was evaluated through microbiological, physical, and chemical water quality testing, with monitoring conducted via Wi-Fi-enabled sensors connected to the Blynk platform. The results demonstrate substantial improvements in treated water quality. Escherichia coli and total coliform bacteria were eliminated (100% reduction). Total dissolved solids (TDSs) decreased from 450 mg/L to 218 mg/L (51.6%), and dissolved manganese was reduced from 30 mg/L to 0.01 mg/L (99.97%), while nitrate levels decreased by 50%. Water pH and temperature remained stable and within regulatory limits. All treated water parameters complied with national clean water standards for hygiene and sanitation. The system operated independently using solar energy and achieved a clean water production capacity of 1000–1500 L/day. These findings indicate that the proposed system is a feasible, cost-effective, and sustainable civil engineering solution for clean water infrastructure in remote building environments. Full article

Peeling behavior of soft materials is important in a wide range of applications, e.g., electronics, healthcare, etc. When applied on soft substrates, soft adhesives demonstrate unique mechanical behaviors compared to adhesives applied on rigid substrates. Adhesive properties can be conveniently measured by “peel testing”. The focus of this work is characterization of commercial glues on fabric substrates using commonly used peel tests. We investigate energy dissipation on textile substrates. For practical applications, we aim to develop a systematic approach for selecting adhesives for soft, flexible substrates. Here, we developed a multi-criteria framework for evaluating adhesives using data from peel tests. The criteria used here consider the shape and stability of the T-peel trace. The results of the multi-criteria evaluation were compared to traditionally used peel strength and fracture energy. Although E6000 produced the highest peel force ($1.82\pm 0.27 \mathrm{N} \mathrm{m}{\mathrm{m}}^{-1}$) and the largest apparent fracture energy, ${\mathrm{G}}_{\mathrm{c}}$ ($8673\pm 1545 \mathrm{J} {\mathrm{m}}^{-2}$), it showed large force oscillation ($\mathrm{S}\mathrm{S}\mathrm{A}=4.05\pm 0.83 \mathrm{N}$). Fabri-Fuse was selected based on its low oscillation ($\mathrm{S}\mathrm{S}\mathrm{A}=0.69\pm 0.29 \mathrm{N}$), lowest $\mathrm{C}\mathrm{o}{\mathrm{V}}_{{\mathrm{F}}_{\mathrm{c}\mathrm{i}}}$(4.0%), high peel stability index (PSI), and high displacement at break. Functional evaluation showed that Fabri-Fuse increased strain-to-electrical-failure to $34.95\pm 2.43\mathrm{\%},$ higher than direct printing on fabric or printing on E6000 (highest peel strength). These results suggest that metrics that consider the shape of the peel trace and inter-sample repeatability provide a useful alternative for selecting adhesives other than highest peel strength. Full article

The microstructure of as-cast Al-xSi (x = 4, 7, 10) alloys solidified under various cooling rates was characterized using scanning electron microscopy (SEM). To overcome the multicollinearity among eutectic silicon parameters, Principal Component Regression (PCR) analysis was employed to quantitatively evaluate the effects of silicon morphology, scale, and content on the electrical conductivity of the alloys. The results demonstrate that rapid solidification significantly refines the plate-like eutectic silicon and reduces its volume fraction, leading to improved electrical conductivity. The PCR model shows that a hierarchical mechanism: volume fraction (PC1) acts as the principal determinant, increasing baseline resistance primarily by truncating the electron mean free path (MFP); meanwhile, within identical alloy systems, morphological parameters (PC2) play a dominant regulatory role. A semi-quantitative electron drift path model was established, confirming that the morphological deviation of eutectic silicon from a spherical shape (i.e., increased aspect ratio) causes a non-linear increase in the amplitude of electron detours. This geometric elongation significantly degrades electrical conductivity, providing theoretical guidance for the microstructural design of high-conductivity Al-Si alloys, which can be practically applied to the manufacturing and optimization of lightweight, heat-dissipating enclosures for new energy vehicle (NEV) motors and power distribution systems. Full article

Water is an indispensable resource for the survival of all living organisms on Earth. However, many coastal villages continue to face challenges in accessing potable water, particularly during extended droughts. This comprehensive study evaluates the implementation and performance of a solar desalination system that employs photovoltaic (PV) panels and a parabolic solar concentrator to meet clean water demand in a drought-prone area of Indonesia. The system harnesses both solar-generated electricity and thermal energy to power an advanced desalination apparatus, effectively converting seawater into safe drinking water. Over a rigorous 4-month testing period, the device maintained an average steam outlet temperature of 105.9 °C, enabling a direct single-stage evaporation and condensation desalination process. Under optimal sunlight conditions, the system produced 1500 mL of purified water every 30 min, resulting in a total daily output of approximately 12 L (1500 mL × 8 cycles over 4 h). Laboratory analysis revealed a decrease in pH from 8.0 in raw seawater to 6.8 in treated water after post-treatment pH adjustment, meeting established safety standards for human consumption. Electrical conductivity measurements fell from 40–50 mS/cm to 480–500 µS/cm, confirming substantial salt removal. These results demonstrate the system’s capacity to generate potable water using sustainable energy sources and support circular economy principles by repurposing renewable resources for water desalination in water-scarce environments. Full article

Socio-spatial inequality remains a defining feature of climate vulnerability in South Africa, where historically formed patterns of segregation continue to shape uneven access to infrastructure, services, and environmental resources. This study presents a narrative review of how historical spatial planning has structured persistent disparities in exposure, sensitivity, and adaptive capacity across urban and rural landscapes. Evidence from the literature demonstrates that apartheid-era spatial planning established durable inequalities in water and sanitation provision, green infrastructure distribution, and proximity to environmental hazards, which continue to influence contemporary climate risk profiles. These inequalities are further reinforced through socio-economic stratification, particularly in the context of energy transitions, where access to private renewable energy systems is concentrated among wealthier households, while poorer communities remain dependent on unstable public electricity infrastructure. The review also incorporates land and soil systems as critical but often minimized dimensions of vulnerability, showing how soil degradation and unequal access to productive land contribute to livelihood insecurity and reinforce rural and peri-urban marginalization. In addition, emerging responses such as just transition frameworks, grassroots environmental justice movements, and energy democracy initiatives are examined with regard to the structural constraints that limit their effectiveness in addressing entrenched inequalities. Overall, the analysis highlights that climate vulnerability in South Africa is deeply embedded in historical and ongoing socio-spatial and socio-economic inequalities that continue to shape differentiated environmental outcomes. Full article

The report presents the electrical, structural, and microstructural properties of high-pressure–high-temperature-treated (HPHT) composites composed of δ-like Bi₂O₃ nanograins embedded in an aluminosilicate glassy matrix. Nanocomposites were obtained by heat treatment of the Bi₂O₃-Al₂O₃-SiO₂ ternary glass system, followed by high-pressure molding (above 750 MPa). The total oxygen conductivity σ_t of the studied nanocomposites was high and approached a value of 4.5 × 10⁻⁴ S/cm at 600 °C. Due to HPHT treatment, we could also determine the intragrain conductivity of δ-Bi₂O₃ nanocrystallites. In this case, the value of σ_δ was even higher and was equal to 1.3 × 10⁻³ S/cm at 600 °C. It was also possible to study the temperature dependence of intragrain conductivity, showing two activation energies, which probably reflect the order–disorder transition within the sublattice of mobile O²⁻ ions. The obtained nanocomposites exhibited promising properties for applications in electrochemical devices operating in the intermediate temperature range from 300 to 600 °C. Full article

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article