Optimal Size Research Articles

Optimal sizing of battery energy storage system for mitigation of RES fluctuation

The integration of renewable energy sources (RES), such as wind and solar, into the electricity grid is crucial in the pursuit of sustainable energy. However, their inherent intermittency poses challenges to grid stability and reliability. Battery Energy Storage Systems (BESS) offer promising solutions to buffer these fluctuations, providing flexibility and scalability. The study examines optimal sizing methods for BESS to mitigate the unpredictability of RES output. It identifies strategies such as forecasting models, stochastic optimization, and hybrid systems that enhance predictability. Additionally, it addresses challenges like battery degradation, safety concerns, and grid integration hurdles. Innovations in Battery Management Systems (BMS) are highlighted for their potential to extend battery life and improve cost-effectiveness. Ultimately, optimizing BESS integration is critical to maximize renewable energy utilization, reduce fossil fuel dependence, and support a sustainable energy future.

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

Optimal sizing and siting of single tunned passive filter based on jellyfish algorithm

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.</span></p>

Computed tomography for the diagnosis of gastroesophageal varices and risk assessment in patients with cirrhosis: a systematic review and meta-analysis.

This meta-analysis aimed to evaluate the diagnostic accuracy of computed tomography (CT) for gastroesophageal varices (GEVs) and identify high-risk GEVs in patients with cirrhosis. A comprehensive search of databases identified 28 studies reporting on CT-based diagnosis for GEVs confirmed via endoscopy. Meta-analyses were conducted to calculate the pooled sensitivity (SEN) and pooled specificity (SPE), positive likelihood ratio (PLR) and negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC). Based on the number of patients (or varices), the pooled SEN, SPE, PLR, NLR, DOR, and AUC of CT-based diagnosis were estimated at 0.91 (0.92), 0.81 (0.45), 4.82 (1.67), 0.11 (0.17), 42.47 (10.26), and 0.93 (0.94), respectively, for any GEV and at 0.89 (0.89), 0.90 (0.79), 8.86 (4.28), 0.12 (0.14), 75.71 (30.19), and 0.95 (0.85), respectively, for high-risk GEVs. Subgroup analyses indicated that CT had a higher diagnostic accuracy for esophageal varices compared with gastric varices (AUC: 0.93 vs. 0.89, P < 0.05), and the 64-slice CT yielded superior SEN compared with 16-slice and <16-slice CT (AUC: 0.97 vs. 0.92 and 0.82, respectively, P < 0.05). Prospective studies demonstrated higher diagnostic accuracy than retrospective studies (AUC: 0.95 vs. 0.90, P < 0.05). Regarding variceal size, a cut-off of 3 mm and 5 mm discriminated between low- and high-risk individuals, respectively, with high diagnostic accuracy (AUC: 0.992 vs. 0.997, P > 0.05). CT demonstrates promising diagnostic accuracy for identifying GEVs and distinguishing high-risk GEVs in patients with cirrhosis. Further research validating optimal variceal size cut-offs is warranted to enhance clinical utility. Such a high diagnostic accuracy of CT scans for predicting varices is clinically meaningful for patients with cirrhosis accompanied by portal hypertension. If high-risk varices are identified at CT scans, early intervention would be helpful to reduce the risk of variceal bleeding.

A Comprehensive Review of Existing and Pending University Campus Microgrids

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses’ microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation.

A kinetic Monte Carlo approach for Boolean logic functionality in gold nanoparticle networks

Nanoparticles interconnected by insulating organic molecules exhibit nonlinear switching behavior at low temperatures. By assembling these switches into a network and manipulating charge transport dynamics through surrounding electrodes, the network can be reconfigurably functionalized to act as any Boolean logic gate. This work introduces a kinetic Monte Carlo-based simulation tool, applying established principles of single electronics to model charge transport dynamics in nanoparticle networks. We functionalize nanoparticle networks as Boolean logic gates and assess their quality using a fitness function. Based on the definition of fitness, we derive new metrics to quantify essential nonlinear properties of the network, including negative differential resistance and nonlinear separability. These nonlinear properties are crucial not only for functionalizing the network as Boolean logic gates but also when our networks are functionalized for brain-inspired computing applications in the future. We address fundamental questions about the dependence of fitness and nonlinear properties on system size, number of surrounding electrodes, and electrode positioning. We assert the overall benefit of having more electrodes, with proximity to the network’s output being pivotal for functionality and nonlinearity. Additionally, we demonstrate an optimal system size and argue for breaking symmetry in electrode positioning to favor nonlinear properties.

Optimal Sizing and Management of Hybrid Wind Turbine-Diesel-Battery System for Reverse Osmosis Seawater Desalination in NEOM City

Optimal sizing and management of hybrid wind turbine-diesel-battery system for reverse osmosis seawater desalination in NEOM city is the objective of the paper. Therefore, the paper explored the different factors to optimize and introduce a technoeconomic evaluation and energy management of a stand-alone wind turbine (WT) system, diesel generator (DG), and battery storage (BS). The suggested WT/DG/BS system is implemented to feed seawater reverse osmosis (SWRO) unit in NEOM. The necessitated desalinated water per day is 100 m3. To determine the optimal size of WT/DG/BS corresponding to the minimum cost of energy (COE) and net present cost, two different ratings of the SWRO units (SWRO-100 and SWRO-150), three control dispatch strategies (load following, cycle charging, and combined dispatch), and five types of batteries are considered. HOMER software is performed to simulate and optimize the WT/DG/BS. The optimization results indicated that the best battery storage is the Trojan SAGM battery. In this case, the COE ranged between $0.337/kWh and $0.564/kWh. The lowest COE of $0.377/kWh is obtained when using a combined control strategy and SWRO-100 unit, whereas the worst COE of $0.564/kWh is obtained when using load following control strategy and SWRO-150 unit. The best option of the WT/DG/BS system to supply the SWRO unit is option number 26. This system includes one wind turbine of 90 kW, DG of 25 kW, 47 Trojan SAGM batteries, a 23.8 kW converter, a SWRO-100 unit, and a combined control strategy. The net present cost and the initial cost are $950,725 and $221,495, respectively. The annual operating cost and annual consumed fuel are $56,409 and 36,396 L, respectively. Compared with using only a 25 kW diesel generator, the COE reduced from $0.373/kWh (using only DG/BS) to $0.337/kWh (using the best option) by around 9.65%. Under this condition, the values for the internal rate of return, return on investment, and simple payback are 11%, 7.8%, and 8.3 years, respectively.

Efficient ablation, further GHz burst polishing, and surface texturing by ultrafast laser

A large variety of ultrafast laser‐matter interaction regimes and different processed surface finishing qualities of stainless steel can be achieved by varying the laser processing parameters. The optimization of the laser fluence to the most efficient ablation also leads to the lowest surface roughness of the ablated material. High laser fluence together with a high pulse repetition rate leads to heat accumulation, which results in the formation of microstructures with various morphology and scales. The use of GHz bursts allows a rough stainless‐steel surface to be smoothed and even polished. In this work, the fast and high‐quality milling of 2.5D cavities was demonstrated. Laser beam spot optimization was used to increase the throughput of the high‐power laser (67.8 W), resulting in an ablation rate of 13 mm3/min. The complex cavities were produced by laser milling and cutting in layers. The formation of surface structures on stainless steel because of laser irradiation has been demonstrated. The possibilities of further GHz burst polishing were examined on previously laser‐milled stainless steel using with lowest possible surface roughness. The ability to polish the surface with microstructures below the original roughness was demonstrated by bursts of ultrashort pulses with a repetition rate in the GHz range. It has been demonstrated that mold milling in stainless steel designed for LED diffusers can be fabricated using a modern femtosecond laser source together with beam size optimization technique for efficient ablation with low surface roughness, layer‐by‐layer milling techniques, and GHz burst polishing techniques.This article is protected by copyright. All rights reserved.

Comparison with surgically resected mucinous cystic neoplasm of pancreas and branch-duct type intraductal papillary mucinous neoplasm considering clinico-radiological high-risk features: a reassessment of current guidelines.

To perform a comparative analysis of surgically resected mucinous cystic neoplasm (MCN) of pancreas and branch-duct type intraductal papillary mucinous neoplasms (BD-IPMN) considering clinico-radiological high-risk predictors for malignant tumors using the current management guidelines. 224 patients who underwent surgical resection and had histopathologically confirmed MCNs (benign 73; malignant 17) or BD-IPMNs (benign 110; malignant 24) and had pre-operative CT or MRI were retrospectively reviewed. Tumors classified as either high-grade dysplasia or invasive carcinoma were considered malignant, whereas those with low-grade dysplasia were considered benign. Imaging features were analyzed by two radiologists based on selected high-risk stigmata or worrisome features proposed by prevalent guidelines except tumors with main pancreatic duct dilatation (> 5mm) were excluded. MCNs and BD-IPMNs showed significant differences in aspects like tumor size, location, the presence and size of enhancing mural nodules, the presence of wall or septal thickening, and multiplicity. Multivariate analyses revealed tumor size (OR, 1.336; 95% CI, 1.124-1.660, p = 0.002) and the presence of enhancing mural nodules (OR, 67.383; 95% CI, 4.490-1011.299, p = 0.002) as significant predictors of malignant MCNs. The optimal tumor size differentiating benign from malignant tumor was 8.95cm, with a 70.6% sensitivity, 89% specificity, PPV of 27.6%, and NPV of 96.9%, demonstrating superior specificity than the guideline-suggested threshold of 4.0cm. For malignant BD-IPMNs, the presence of enhancing mural nodules (OR, 15.804; 95% CI, 4.439-56.274, p < 0.001) and CA 19 - 9 elevation (OR, 19.089; 95%CI, 2.868-127.068, p = 0.002) as malignant predictors, with a size of enhancing mural nodule threshold of 5.5mm providing the best malignancy differentiation. While current guidelines may be appropriate for managing BD-IPMNs, our results showed a notably larger optimal threshold size for malignant MCNs than that suggested by current guidelines. This warrants reconsidering existing guideline thresholds for initial risk stratification and management of MCNs.

Detector size selection for long-range targeting performance in the extended shortwave infrared band

Interest in the eSWIR band is growing due to focal plane array technology advancements with mercury cadmium telluride and type-II superlattice materials. As design and fabrication processes improve, eSWIR detector size, weight, and power can now be optimized. For some applications, it is desirable to have a smaller detector size. Reduced solar illumination in the 2 to 2.5 μm spectral range creates a fundamental limit to passive imaging performance in the eSWIR band where the resolution benefit of small detectors cannot out-compete the reduced SNR in photon-starved environments. This research explores the underlying theory using signal-to-noise ratio radiometry and modeled target discrimination performance to assess the optimal detector size for eSWIR dependent upon illumination conditions. Finally, we model continuous-wave laser illumination in the eSWIR band to compare the effect of detector size on active and passive imaging for long-range object discrimination.

Cross-Linking CdSO4-Type Nets with Hexafluorosilicate Anions to Form an Ultramicroporous Material for Efficient C2H2/CO2 and C2H2/C2H4 Separation.

A 44.610.8 topology hybrid ultramicroporous material (HUM), {[Cu1.5F(SiF6)(L)2.5]·G}n, (L = 4,4'-bisimidazolylbiphenyl, G = guest molecules), 1, formed by cross-linking interpenetrated 3D four-connected CdSO4-type nets with hexafluorosilicate anions is synthesized and evaluated in the context of gas sorption and separation herein. 1 is the first HUM functionalized with two different types of fluorinated sites (SiF6 2- and F- anions) lining along the pore surface. The optimal pore size (≈5Å) combining mixed and high-density electronegative fluorinated sites enable 1 to preferentially adsorb C2H2 over CO2 and C2H4 by hydrogen bonding interactions with a high C2H2 isosteric heat of adsorption (Qst) of ≈42.3kJmol-1 at zero loading. The pronounced discriminatory sorption behaviors lead to excellent separation performance for C2H2/CO2 and C2H2/C2H4 that surpasses many well-known sorbents. Dynamic breakthrough experiments are conducted to confirm the practical separation capability of 1, which reveal an impressive separation factor of 6.1 for equimolar C2H2/CO2 mixture. Furthermore, molecular simulation and density functional theory (DFT) calculations validate the strong binding of C2H2 stems from the chelating fix of C2H2 between SiF6 2- anion and coordinated F- anion.

Design and sample size determination for multiple-dose randomized phase II trials for dose optimization.

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk-benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This article focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org.

Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia

Abstract Garlic is one of the most important cash crops cultivated by smallholder farmers throughout Ethiopia. However, due to inappropriate agronomic practices including clove size and intra-row spacing, the productivity of the crop is low in the country. Therefore, a field experiment was conducted during the 2019 main cropping season under rain feed conditions to identify the optimum clove size and intra-row spacing for the economical production of garlic in Lay Gayint district, Northwestern Ethiopia. Local garlic cultivar obtained from farmers was used as a test crop. A factorial combination of three clove sizes and four intra-row spacings was used in a randomized complete block design with three replications. The results revealed that the main as well as the interaction effects of clove size and intra-row spacing influenced most of the tested growth and yield parameters of garlic. The longest plant height (73.77 cm) was recorded from garlic plants grown at 5 cm intra-row spacing, while the shortest plant height (66.98 cm) was from plants grown at 12.5 cm. The treatment combination of large-sized clove and 12.5 cm intra-row spacing recorded the longest leaf length (61.07 cm), the highest leaf number (7.79), bulb diameter (4.98 cm), bulb weight (33.83 g), and clove weight (2.71 g) of garlic. However, the treatment combination of large-sized cloves and 7.5 cm intra-row spacing produced the highest marketable (13.44 t ha−1) bulb yield. Based on the partial budget analysis, however, medium-sized cloves (1.5–1.99 g) planted at 10 cm intra-row spacing recorded the highest net benefit (723,205 Eth-Birr ha−1) with an acceptable marginal rate of return (3277.3%). Therefore, growing garlic using medium-sized cloves (1.5–1.99 g) at 10 cm intra-row spacing is recommended for economical production of garlic by smallholder farmers in the study area and areas with similar agroecology.

Research on Energy Loss of Optimization of Inducer–Impeller Axial Fit Dimensions Based on Wave-Piercing Theory

With the development of modern fluid machinery, the energy density of pumps is gradually being improved, and at the same time, higher demands are being placed on the cavitation performance, hence the introduction of the inducer and centrifugal impeller to form a dynamic–dynamic series structure. However, there are strict constraints on the axial size of pumps in fields such as firefighting and aerospace. The traditional empirical formula no longer satisfies the need to fit the axial dimensions between the induced wheel and the impeller at high velocities. Therefore, based on the wave-piercing theory, the drag reduction coefficient is introduced to explore the optimal axial fit size from the perspective of energy characteristics. This paper focuses on the influence of the inducer’s wake on the energy characteristics of downstream impellers, and conducts the following research: by adjusting the axial matching dimensions between the upstream inducer and the centrifugal impeller in the initial model, ten sets of axial distance models with matching dimensions of KD are designed, and the drag reduction coefficient is embedded to determine the optimal axial distance. The results show that the optimal axial distance is 0.2D, which is far lower than the axial distance value of 0.42D obtained from the traditional empirical formula for axial matching dimensions. Meanwhile, this paper uses tangential velocity, the inlet flow angle of the impeller, entropy production theory, and other indicators to analyze the internal energy loss of the high-speed vehicular fire pumps one by one. All of them confirm that the impeller in the high-speed vehicular fire pump has the lowest energy loss and optimal performance at an axial distance of 0.2D. Specifically, at this axial distance, the head can reach 259 m, and the hydraulic efficiency is as high as 83.62%. Thus, the feasibility of determining the axial placement of the impeller using the drag coefficient is validated. This research provides new insights into determining the axial coordination dimensions between the inducer and the impeller.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.

Maximum span determination and optimal sizing of cable for improved performance of droop-controlled DC microgrid

DC microgrids are seen as smart solutions to interface DC loads and distributed energy resources (DERs). However, in DC microgrids, the placement of sources and loads as well as the size of cable impact the system’s span, regulation of voltage, and losses. This paper proposes novel algorithms to determine the maximum span and optimal size of cable for the improved performance of a droop-controlled DC microgrid. The proposed algorithms utilize an improved power flow analysis (IPFA) method based on Newton-Raphson technique to determine the maximum span and the optimal size of cable, enhancing the voltage regulation and reducing the cost. Additionally, the impacts of power rating, droop constants, and size of cable on the DC microgrid’s maximum span are investigated and reported. Owing to the intricate nature of the problem concerning the ideal size of cable for the droop-controlled DC microgrid, a heuristic optimization approach is employed. Further to enhance the rate of convergence and computational performance, an improved particle swarm optimization (IPSO) is also proposed. The constraint of keeping the bus voltage variations below the allowable voltage regulation limits is applied to the objective function of the optimization problem. In the case of a droop-controlled, DC microgrid having a specified configuration, the suggested algorithm can determine the ideal size of cable, guaranteeing both the least cost and enhanced voltage regulation. Comprehensive numerical and modelling results have been presented for a droop-controlled DC microgrid with different loads and DERs to verify the effectiveness of the proposed methods. Furthermore, the analysis reveals that configuration III of the DC microgrid with an optimal cable size of 19 mm2 lowers the absolute voltage regulation to as low as 1.06 V. The results of the detailed analysis validate the enhanced performance of the proposed algorithms and are highly useful to both system designers and consumers of the DC microgrids, eventually paving the way for the widespread use of DC microgrids in the future.

Source reservoir controls on the size, frequency, and composition of large-scale volcanic eruptions.

Large-scale, explosive volcanic eruptions are one of the Earth's most hazardous natural phenomena. We demonstrate that their size, frequency, and composition can be explained by processes in long-lived, high-crystallinity source reservoirs that control the episodic creation of large volumes of eruptible silicic magma and its delivery to the subvolcanic chamber where it is stored before eruption. Melt percolates upward through the reservoir and accumulates a large volume of low-crystallinity silicic magma which remains trapped until buoyancy causes magma-driven fractures to propagate into the overlying crust, allowing rapid magma transfer from the reservoir into the chamber. Ongoing melt percolation in the reservoir accumulates a new magma layer and the process repeats. Our results suggest that buoyancy, rather than crystallinity, is the key control on magma delivery from the source reservoir. They identify an optimum reservoir size for the largest silicic eruptions that is consistent with data from natural systems and explain why larger magnitude eruptions are not observed on Earth.

Analysis of landscape pattern and ecological risk change characteristics in Bosten Lake basin based on optimal scale

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

On the planning of zone-based electric on-demand minibus

An on-demand transportation system can enhance traffic efficiency through its flexible services. In this study, we introduce a planning problem for an on-demand electric minibus service within urban areas. We are considering a zone-based operation, meaning that the on-demand minibus is restricted to picking up or dropping off passengers within a single zone for each trip, and the minibus does not stop in other zones. For this zonal electric minibus system, we plan the service level for each zone, the construction of dedicated charging piles in each zone, and the optimal size of the bus fleet. To model the planning problem, we propose a mixed-integer nonlinear second-order cone program. Additionally, we introduce an adaptive discretization algorithm to expedite the problem-solving process. Our numerical tests illustrate the planning for the on-demand minibus system in Manhattan. Furthermore, these tests underscore the benefits of the proposed on-demand minibus system. The most important findings of this study are: (i) the proposed mode replaces more trips with cars, thus reducing the traffic flow and alleviating traffic congestion; (ii) the service zone division, the setting of the fare, and the number of seats in minibus play an essential role in the revenue of the on-demand minibus system; (iii) considering endogenous zone selecting, number of dedicated charging piles, and mode choice in each zone is essential to the planning of the on-demand minibus system.