Search Results (16)

Aluminium powder, an energetic material, is prone to thermal runaway upon water exposure under local heat sources, yet the nonadiabatic mechanisms of micron-sized accumulated aluminium powder under localised heating remain unclear. This study employs a proprietary characterisation platform to investigate the effects of particle size, water content, and local heat source power on heat transfer in the dry state and on parameters including induction time, onset temperature, peak heat release rate, and reaction heat during the induction and main reaction phases. In the dry state, decreasing particle size enhances effective thermal conductivity and accelerates temperature rise, whereas elevated local heat source power exacerbates thermal inertia. Under local heating upon water exposure, reduced particle size significantly enhances reactivity; the reaction heat of 2 μm powder reaches 983 J/g, approximately fourfold that of 106 μm powder. Water content exhibits a nonmonotonic effect, with the onset temperature reaching a minimum of 66.4 °C at a water content of 25%, while the reaction heat peaks at 33% water content. Interestingly, increasing local heat source power was found to suppress reaction intensity, and reaction heat at 10 W is one sixth of that at 2.5 W, attributed to rapid product layer densification and the possible steam-film barrier effect shifting the controlling mechanism from chemical to diffusion control. A coupled multifactorial predictive model incorporating the three factors was established with a correlation coefficient R² of 0.92, providing a theoretical basis and practical guidance for hazard assessment and safe storage of aluminium powder. Full article

Barley lodging—specifically stem lodging—occurs when the bending moments from wind and ear weight exceed the culm’s load-bearing capacity. Lodging risk decreases as plant height decreases and culm strength increases. Geometry (stem diameter, culm wall thickness) and material strength determine culm bending strength. By studying changes in stem mechanical properties (at three positions along the culm) in two genotypes (grown in a greenhouse), we found that culm strength (assessed with a three-point bending test) slightly diminished through ripening owing to a decline in both area moment of inertia (i.e., strength due to geometry alone) and apparent material strength, presumably due to turgor loss. When the stem segments collected from fully ripe plants were dried to a moisture content typical of harvest maturity, however, strength rose to a maximum. Thus, minimum stem bending resistance occurs during a window in which plants are fully ripe but have not yet reached harvest-dry moisture content. Hence, in the absence of rain—which would severely reduce the mechanical strength of dry, ripe plants—the physiological risk of stem lodging is highest when the crop is fully ripe but not yet harvest-dry. However, the actual lodging risk increases as harvest approaches, because summer storms are frequent at this time of year and dry straw loses rigidity when wetted. Full article

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy. Full article

With the rapid development of power systems rich in renewable energy, inertia shortages pose significant challenges to frequency security. There is an urgent need for appropriate market pricing mechanisms to quantify the economic value of inertia and incentivize inertia resources to participate in system frequency regulation. Existing market pricing mechanisms struggle to address non-convex generation scheduling problems involving inertia constraints, often resulting in substantial uplift payments that undermine market efficiency and reduce market transparency. To address this issue, this paper proposes a novel convex hull pricing framework specifically designed for the integrated energy–inertia market. The core innovation lies in combining Dantzig–Wolfe decomposition with column generation algorithms to efficiently solve non-convex optimization problems by dynamically constructing the convex hull of feasible dispatch schemes. Based on transient frequency security metrics, the method derives the minimum inertia requirement constraint for the system and calculates the economic value of inertia in non-convex markets using convex hull pricing. Simulation studies on a modified IEEE 39-node system demonstrate two major breakthroughs: the method accurately assesses the economic value of synchronous inertia, with prices reflecting scarcity as wind penetration increases and significantly reduces total system uplift payments compared to integer relaxation pricing schemes. Consequently, this research provides a transparent, incentive-compatible, and cost-effective tool for designing and operating future inertia ancillary service markets. Full article

In response to the decline in the inertia level of the power system caused by the large-scale integration of new energy, this paper proposes a grid-side pumped storage configuration strategy considering inertia constraints. The general pumped storage configuration ignores the duration of pumped storage and selects only single-duration units for capacity and power configuration. A single unit cannot balance rapid frequency response and long-term energy transfer, forcing thermal power to operate at high costs continuously to provide inertia support, while also causing a sharp increase in wind and solar power curtailment. This paper breaks through the limitations of the traditional single-duration pumped storage configuration and proposes a configuration-operation collaborative optimization strategy that combines inertia constraints and pumped storage duration selection. Firstly, starting from the system’s inertia requirements, the minimum inertia required by the system is obtained, respectively, based on the constraints of the system’s frequency change rate and the lowest point of the frequency. Furthermore, the minimum inertia demand constraint of the power system is constructed, and a capacity configuration strategy for grid-side pumped storage is proposed with the goal of minimizing the total operating cost of the power system throughout its entire cycle, taking into account the penalty term of the peak-valley difference index of the load curve and the penalty of the inertia guarantee value of medium and long-term units, while considering the inertia constraint. Finally, the effectiveness and superiority of the proposed method were verified through simulation analysis. Full article

To address the challenges to power system frequency stability under high penetration of renewable energy, this paper proposes a coordinated inertia support strategy for wind–PV–thermal storage systems, overcoming the limitations of conventional inertia parameter adjustment. The core of the strategy lies in optimizing unit control activation logic and establishing a scenario-adaptive batch activation mechanism. Specifically, virtual inertia characteristic models for wind, PV, and storage units are developed, with key parameters optimized via fuzzy-logic-based coordinated control. An inertia demand assessment model under frequency security constraints is constructed to quantify the minimum system inertia requirement. Furthermore, disturbance reference power is generated based on the inertia reserve capability of each unit, and disturbance intervals are classified to achieve coordinated optimal allocation of virtual inertia. Simulation results on a built 3-machine, 9-node system demonstrate that the proposed strategy can intelligently coordinate the activation timing, role assignment, and regulation resources of wind, PV, and storage according to the type and severity of disturbances. Under various scenarios such as sudden load increase and decrease, the system effectively mobilizes resources to maintain frequency within the secure range while avoiding frequent actions of any single unit. The results verify that the strategy significantly enhances the system’s capability to handle bidirectional power disturbances and provide frequency support, offering a practical solution for inertia management in renewable-dominated power systems. Full article

The increasing penetration of inverter-based renewable generation has reduced rotational inertia in power systems worldwide, causing steeper frequency drops after severe contingencies and increasing the risk of load shedding. In the Honduran context, this study evaluates the dynamic response of the National Interconnected System (NIS) operating in island mode through detailed DIgSILENT PowerFactory simulations, explicitly incorporating the national Under-Frequency Load Shedding (UFLS) scheme. Five disturbance scenarios were analyzed, including generation losses of 100 MW, 200 MW, and 262 MW, to assess the frequency support provided by Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs). Results show that, in the base case, frequency decreased to 55.3 Hz during a 200 MW loss, confirming the system’s high vulnerability. The integration of a 75 MW BESS improved frequency stability to 58.74 Hz, preventing UFLS activation, while a 320 MW equivalent FESS provided only short-term inertial support with limited effectiveness. Quantitatively, the BESS reduced the minimum frequency, delayed UFLS activation by approximately 3.5 s, and provided sustained support, whereas the FESS contributed mainly during the first 5 s of the disturbance. In the most severe contingency (262 MW generation loss), the combined operation of BESS and FESS prevented total system collapse, improving the frequency nadir to 58.6 Hz. These results confirm that BESS provides more robust and sustained frequency support than FESS under the analyzed conditions, highlighting its effectiveness for improving system stability in low-inertia networks such as Honduras. The findings offer useful insights for future studies on storage integration and frequency regulation strategies. Full article

Background and Objectives: To assess the effects of adding dapagliflozin to an antidiabetic regimen, compared to standard of care (SOC) antidiabetic therapy and a cohort of patients who remained on a non-SGLT2i antidiabetic regimen without treatment modification despite persistent HbA1c elevation, on cardiometabolic control over a 12-month period in real-world settings. Materials and Methods: This ambispective (retrospective and prospective) observational study enrolled adults with type 2 diabetes who had received first-line metformin therapy, or an alternative antidiabetic agent in cases of intolerance or contraindication, excluding SGLT2 inhibitors, for a minimum of 12 months prior to recruitment. Patients were allocated to one of three groups: DAPA, SOC, or therapeutic inertia. Each patient attended three visits. The retrospective (V1–V0) and prospective (V0–V2) follow-up periods each extended for a minimum duration of six months. The primary endpoint was the change in cardiometabolic control from baseline to week 26 (V0), defined as HbA1c reduction ≥ 0.5%, weight loss ≥ 2 kg, and a systolic blood pressure drop ≥ 2 mmHg. Results: Five hundred thirty-five diabetes patients were included (39.1% women), with mean age (SD) 63.8 (8.2) years, body mass index 30.4 (4.9) kg/m², and HbA1c 6.93 (0.9) %. More patients achieved cardiometabolic control with dapagliflozin (21.8%) vs. SOC + inertia (1.9%), OR 12.7 (95% CI 5.3–30.6), p < 0.001. The difference became greater over the entire study period. Event rates were low, but dapagliflozin exhibited fewer events numerically. The safety profile of dapagliflozin was consistent with previous findings. Conclusions: Treatment with dapagliflozin as add-on therapy was associated with improved cardiometabolic control over time compared to SOC, along with a numerical reduction in events. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials. Full article

In order to ensure the sustainable development of energy, the development of new power systems with a high penetration of renewable energy has become a key research direction in the field of power systems. This paper studies the system frequency response process and key indicators from the perspective of high-penetration renewable power systems and proposes an inertia and primary frequency response requirement assessment method for power system planning under high renewable penetration. First, by analyzing the frequency dynamic response process, the key parameters affecting frequency stability are determined, and the evolution trend of system inertia with increasing renewable penetration is analyzed. Second, based on the real-system data, the inertia and primary frequency response parameters for each generator are obtained. With the planning generation mix and load as the goal, whether the synchronous generators in the target system can meet the frequency stability requirements is determined. Finally, with the system inertia demand under the maximum rate of change of frequency (RoCoF) constraint as the starting point, we iteratively increase inertia and the primary frequency response capacity until the minimum matching configuration is found. The simulation results verify the correctness of the proposed assessment method. This method considers various processes in frequency response and multiple influencing factors, providing a practical evaluation tool for the inertia and primary frequency response requirements of high-penetration renewable power systems. Full article

The integration of power electronics-interconnected generation systems to the grid has fostered a significant number of concerns on power system operations, particularly on the displacement of synchronous generators that leads to a reduction in the grid’s overall inertia and frequency response. These concerns have raised a significant amount of state-of-the-art mathematical proposals on how to estimate system inertia; however, the majority of the proposals do not differentiate generator inertia from load inertia. When inertia prediction for control room applications is required in real-time, the current state-of-the-art proposals use the inertia of generators as a proxy for a minimum, overall inertia estimate, counting the number of units committed in real-time and adding up their inertia. However, as dynamic conditions are becoming challenging with the integration of power electronics-interconnected generation systems, it is important to quantify the amount of inertia from the loads, for which the state-of-the-art proposals present very limited advancement, particularly in applications with real data. This work presents a set of recorded actual events in the Chilean power system to estimate the contribution of loads to inertia and frequency response to assess whether the loads have a significant role in frequency stability. The contribution of this work is as follows: first, reporting real data of a power system from the PMU and SCADA systems that are usually classified as not public; and, second, to derive a conclusion from the data to assess the role of loads in frequency stability in a real case. Full article

The main aim of this study was to examine the relationship between body weight, absolute and relative strength and power variables in a flywheel Romanian deadlift. A secondary aim was to assess the inter-day reliability of a novel power assessment protocol previously used to determine the inertial load that produced the maximum power output in Flywheel Inertia Training. Ten physically active males took part in this study. Participants had some experience with flywheel devices, but all had a minimum of 24 months of traditional resistance training experience. The first testing session consisted of three sets of 10 repetitions with a different inertial load for each set (0.050, 0.075, and 1.00 kg·m²). Each set’s first and second repetitions were used to build momentum and were excluded from data analysis. The order of inertial load used in each trial was standardized for all participants: first, 0.050 kg·m², second, 0.075 kg·m², and last, 0.100 kg·m². The secondary testing session followed the same procedure as the first. No statistically significant (p < 0.05) effect was found between any of the variables in the correlation analysis. There were large positive correlations between the 1 repetition max flywheel Romanian deadlift and peak concentric power, relative strength, and peak concentric and eccentric peak powers. Both body weight and relative strength showed moderate negative correlations with % eccentric overload, whereas moderate positive correlations were observed between 1RM and peak eccentric power. Both concentric power and eccentric power showed excellent reliability, while the reliability for % eccentric overload ranged from poor to excellent depending on the inertial load. In conclusion, this study shows that a protocol to assess the maximum power output has excellent reliability for both ECC and CON power and may be used in future flywheel training. The results also showed that body weight, maximum strength, and relative strength were not largely related to power variables. An individualized approach to flywheel training is required. Full article

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis. Full article

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefore, a coupled heat-transfer model was developed in this study, and large eddy simulations were performed to verify the influence of inertia and buoyancy—which are mutually exclusive forces but coexist in large-space building environments—on inhomogeneous thermal environments. Furthermore, an artificial neural network (ANN) model was designed to overcome the limitations of the nonlinear relationships between thermal parameters and predicted mean vote (PMV) values. PMV indexes can be predicted using the ANN model when thermal parameters are used as input data. Subsequently, a genetic algorithm, harmony search algorithm, gravitational search algorithm, and whale optimization algorithm were adopted to optimize the neural network structure to prevent its confinement in a local optimum. Finally, with 5000 data sets, the minimum-error neural network structure 6-22-23-1 of the ANN-GA neural network model had high prediction accuracy, mean relative error < 1.38, root mean square error < 1.34, and a regression coefficient of ~1. The proposed ANN model can help improve the assessment of the thermal environment and thermal comfort of buildings. Full article