Realistic Energy Research Articles

Graphene as Nanocarrier for Gold(I)-Monocarbene Complexes: Strength and Nature of Physisorption.

Gold(I) metal complexes are finding increasing applications as therapeutic agents against a variety of diseases. As their potential use as effective metallodrugs is continuously confirmed, the issue of their administration, distribution and delivery to desired biological targets emerges. Graphene and its derivatives possess attractive properties in terms of high affinity and low toxicity, suggesting that they can efficaciously be used as drug nanocarriers. In the present study, we computationally address the adsorption of a gold(I) N-heterocyclic monocarbene, namely, IMeAuCl (where IMe = 1,3-dimethylimidazol-2-ylidene), on graphene. The Au(I) N-heterocyclic carbene family has indeed shown promising anticancer activity and the N-heterocyclic ring could easily interact with planar graphene nanostructures. By means of high-level electronic structure approaches, we investigated the strength and nature of the involved interaction using small graphene prototypes, which allow us to benchmark the best-performing DFT functionals as well as assess the role of the different contributions to total interaction energies. Moreover, realistic adsorption enthalpies and free energy values are obtained by exploiting the optimal DFT method to describe the drug adsorption on larger graphene models. Such values (ΔHads = -18.4 kcal/mol and ΔGads= -7.20 kcal/mol for the largest C150H30 model) indicate a very favorable adsorption, mainly arising from the dispersion component of the interaction, with the electrostatic attraction also playing a non-negligible role.

A matheuristic for the electric vehicle routing problem with time windows and a realistic energy consumption model

We address an Electric Vehicle Routing Problem with Time Windows (E-VRPTW) considering several real-like factors in the energy consumption model, e.g., the payload and the vehicle speed. We model E-VRPTW as a Mixed Integer Linear Program (MILP) where the speeds of vehicles are continuous variables that can vary between a minimum and a maximum value. Moreover, the proposed MILP formulation is cloneless since it allows using more than once the same recharging station without introducing dummy copies of it. To efficiently solve large-sized instances of the problem, we design a Random Kernel Search (RKS) matheuristic approach, based on the cloneless MILP formulation, that in turn exploits another matheuristic, called Random k-Degree Search (RkDS), to generate an initial feasible solution. We compare the results produced by a MILP solver using the MILP formulation with the ones obtained by the RKS on instances up to 100 customers derived from the benchmark instances of E-VRPTW. We show that the proposed matheuristic outperforms the cloneless MILP formulation on the medium/large-sized instances and also that it is robust, being not significantly sensitive to the values of the parameters used by the RkDS to generate the initial solution and to the initial time limit for the restricted MILP models.

A State-of-the- Art Holistic Charge Scheduling for Direct Charging and Battery Swapping Station under a Collaborative Environment

This paper proposes a scalable holistic day-ahead scheduling framework for EV charging by considering spatial-temporal properties of EVs for considering the amalgamation of Plug-in charging station (CS) and Battery Swapping Station (BSS). This work explores the factors for increasing the number of EVs charged and the profit of service providers through a novel algorithm including collaboration among aggregators. Using a 25-node transportation network, this framework is tested for realistic traffic scenarios using both non-collaborative and collaborative inter-aggregator schemes. The results under these scenarios prove the efficacy of the proposed framework in maximizing the scheduled vehicles and enhancement of the profit of aggregators against the non-collaborative scheme in each scenario. The proposed framework for day-ahead scheduling is thus capable of charging EVs with various energy demands during a day on its way at an appropriately assigned direct charging station or battery swapping station under a collaborative framework while incorporating realistic energy purchase for different traffic scenarios with cancellation penalties.

The need to make funding more available to explore global hotspots as key part of a realistic energy transition

The need to make funding more available to explore global hotspots as key part of a realistic energy transition

Development of an Optimal Control Strategy for Temperature Regulation and Thermal Storage Operation of a Solar Power Plant Based on Fresnel Collectors

Concentrating solar power (CSP) plants with thermal energy storage (TES) systems are a promising sustainable technology to meet the increasing global energy consumption and reduce the greenhouse gases emission. From a control point of view, these plants have state constraints, nonlinear dynamics, and discontinuities which must be taken into account when a control system is designed so that a good performance over the whole operation range is achieved. In this regard, an optimal control strategy aiming to regulate the captured solar energy, and to manage properly the charge and discharge of the TES systems to maximize the revenue according to a variable energy tariff, is proposed in this work. Both goals are tackled considering a decentralized strategy with a rigorous perspective to show which properties can effectively be guaranteed using such an approach. Extensive numerical simulations with realistic energy tariff prices and weather data of Brazil are presented to illustrate the performance of the proposed control methodology. Furthermore, a comparative study with a classic control strategy demonstrates the improvements obtained with the proposed controller in the revenue of the produced energy in such a scenario. Potential gains up to 13.5%, in terms of annual revenue, are estimated in case the optimal strategy is adopted.

Explaining the Muon g − 2 Anomaly in Deflected AMSB for NMSSM

We propose to embed the General NMSSM (Next-to-Minimal Supersymmetric Standard Model) into the deflected AMSB (Anomaly Mediated Supersymmetry Breaking) mechanism with Yukawa/gauge deflection contributions. After the integration of the heavy messenger fields, the analytical expressions of the relevant soft SUSY breaking spectrum for General NMSSM at the messenger scale can be calculated. We find that successful EWSB (Electroweak Symmetry Breaking) and realistic low energy NMSSM spectrum can be obtained in some parameter regions. In addition, we find that the muon g−2 anomaly and electron g−2 anomaly (for positive central value electron g−2 experimental data) can be jointly explained to 1σ and 2σ range, respectively. The Z3 invariant NMSSM, which corresponds to ξF=0 in our case, can also jointly explain the muon and electron anomaly to 1σ and 2σ range, respectively.

Effect of Physically Realistic Potential Energy Form on Spatial Pattern Complexity in a Collective Motion Model

Collective motion models most often use self-propelled particles, which are known to produce organized spatial patterns via their collective interactions. However, there is less work considering the possible organized spatial patterns achievable by non-self-propelled particles (nondriven), i.e., those obeying energy and momentum conservation. Moreover, it is not known how the potential energy interaction between the particles affects the complexity of the patterns. To address this, in this paper, a collective motion model with a pairwise potential energy function that conserved the total energy and momentum of the particles was implemented. The potential energy function was derived by generalizing the Lennard–Jones potential to reduce to gravity-like and billiard-ball-like potentials at the extremes of its parameter range. The particle model was simulated under a number of parameterizations of this generalized potential, and the average complexity of the spatial pattern produced by each was computed. Complexity was measured by tracking the information needed to describe the particle system at different scales (the complexity profile). It was found that the spatial patterns of the particles were the most complex around a specific ratio in the parameters. This parameter ratio described a characteristic shape of the potential energy function that is capable of producing complex spatial patterns. It is suggested that the characteristic shape of the potential energy produces complex behavior by balancing the likelihood for particles to bond. Furthermore, these results demonstrate that complex spatial patterns are possible even in an isolated system.

Energy Performance Certificate renewal — An analysis of reliability of simple non-domestic buildings ’ EPC ratings and pragmatic improving strategies in the UK

Energy Performance Certificates (EPCs) are designed to provide consistent and comparable energy efficiency ratings. Since 2008, any building constructed, rented or sold is required to obtain an EPC in the UK. 4% of simple office buildings have renewed their EPC to date in England and Wales. In this paper, by comparing the original and renewed EPCs of these buildings, we examine the reliability of non-domestic EPC ratings and discover realistic energy efficiency improvement strategies reflected in the changes. We have found that buildings with their conditions remaining unchanged can be better rated by approximately 10 points on average when their initial rating is above the minimum requirement in the regulation. The ones initially rated below the minimum requirement are expected to have a greater scale of improvement on ratings while making no efforts which brings doubts on the effect of the “Minimum Energy Efficiency Standard” policy. Evidenced human factors are also jeopardising the fairness of EPC ratings. Based on the changes in the EPCs, we have found 4 clusters of improved buildings and 5 clusters of deteriorated buildings. We derived practical energy efficiency improvement strategies that people have already naturally adopted.

Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Improving Cumulus Parameterization

Abstract Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms.

On Consistent Parameterizations for Both Dominant Wind‐Waves and Spectral Tail Directionality

AbstractNumerical wave models have been developed to reproduce the evolution of waves generated in all directions and over a wide range of wavelengths. The amount of wave energy in the different directions and wavelength is the result of a number of physical processes that are not well understood and that may not be represented in parameterizations. Models have generally been tuned to reproduce dominant wave properties: significant wave height, mean direction, dominant wavelengths. A recent update in wave dissipation parameterizations has shown that it can produce realistic energy levels and directional distribution for shorter waves too. Here, we show that this new formulation of the wave energy sink can reproduce the variability of measured infrasound power below a frequency of 2 Hz, associated with a large energy level of waves propagating perpendicular to the wind, for waves with frequencies up to 1 Hz. The details are sensitive to the balance between the non‐linear transfer of energy away from the wind direction, and the influence of dominant and relatively long waves on the dissipation of shorter waves in other directions.

Assessing urban micro-climates with vertical and horizontal building morphological cutting deep transfer learning neural networks

The weather condition is the major external factor that affects annual building energy usage and determines the reliability of building energy assessment and simulation models. In city-scale studies, buildings’ energy assessment and simulation rely on one universal Typical Meteorological Year weather condition for the whole city. However, due to the unique morphological features and environmental conditions, each location in a city has its unique microclimate conditions. To predict and simulate more accurate and realistic building energy usage, this study developed a deep transfer learning neural network that can integrate morphological features and frontal areas' depth distribution. The frontal areas are represented as stripes with different cutting methods to reflect the impact of depth distribution. To validate the proposed method, this study conducted an on-campus experiment with a wireless sensing system. The results suggest that the proposed stripe-enabled geometry segmentation method can effectively improve the accuracy of local microclimate condition prediction. Also, the combined vertical and horizontal cutting method shows the highest performance.

A Detailed Numerical Study of a Nanofluid-Based Photovoltaic/THERMAL Hybrid System under Non-Uniform Solar Flux Distribution

The concentrated photovoltaic/thermal system (CPVT) adopting spectral beam splitting is a promising field of solar energy research. However, the thermo-electric properties of fluid-based CPVT collectors, which depend strongly on the non-uniform concentrated energy flux, remain unclear. This study aims to fill the gap and explore the thermo-electric properties of fluid-based CPVT collectors under non-uniform energy flux based on the finite volume method (FVM) with the Monte Carlo Ray-Trace (MCRT) method. The actual solar flux distribution on the receiver surface is obtained using Tracepro software. Then, the realistic non-uniform energy flux was employed in ANSYS Workbench 2022R1 software as a boundary condition to increase the accuracy of the CFD modeling of the system. The model is validated by comparing the results of the reference data. Moreover, the impact of uniform and non-uniform energy flux on the PV cell temperature is analyzed. In addition, the effects of mass flow rate on the electrical and thermal performance of the system are investigated. The results show that the PVT hybrid system has high conversion efficiency, with a total efficiency of more than 50%. Notably, the extreme non-uniformity of the solar-concentrated energy flux can result in local overheating of the PV cell, which may lead to irreversible damage.

4D dynamical whole-cell simulations of a growing minimal cell.

JCVI-syn3A is a genetically minimal bacterial cell with a single chromosome consisting of 493 genes that has retained few regulatory proteins or small RNAs. We recently developed a 3D fully dynamical whole-cell model that simulates the first 20 minutes of the cell cycle for this minimal cell before replication and growth. This model, based on experimental characterization of Syn3A and measurements from numerous biochemical experiments in related organisms, consisted of complete reaction networks for genetic information processing and metabolism totaling over 7200 chemical reactions. We now extend this 4D model to simulate the trajectory of a complete cell cycle while we dynamically update the shape of the membrane, chromosome configuration (including replication state), and polysome structures. To capture the broad range of length- and time-scales of the various dynamic processes, we periodically communicate cell state information among several coupled simulation techniques, including: reaction-diffusion and chemical master equation simulations for stochastic reactions, numerical integration of ODE's for metabolic reactions, and coarse-grained Brownian dynamics for the chromosome and membrane. Membrane shapes during division are inferred from experimental fluorescent images of dividing cells. The chromosome configurations are placed using physically realistic energy functions. From the model, we determine time-dependent concentrations, spatial distributions, and reaction fluxes that offer insight into the principles of life for this minimal cell, for example: prediction of doubling time matching the experimental value, realistic distributions of mRNA half-lives, and time-dependent balance of ATP costs and production for all reactions in the cell.

Rational construction of efficient ZnS quantum dots-supported g-C3N4 with Co3O4 heterostructure composite for bifunctional electrocatalytic hydrogen evolution reaction and environmental pollutant degradation

Bifunctional heterostructure composite catalysts of ZnS quantum dots (QDs) are incorporated with Co3O4-coupled g-C3N4 (GCOZS) for enhanced electrocatalytic hydrogen evolution reaction (HER) and photocatalytic aqueous dye degradation performance. The phase purity, chemical composition, surface/deep morphology, and specific surface area of the as-obtained catalyst materials are determined using powder XRD, FT-IR, high-resolution XPS, FESEM with EDX mapping, HRTEM and BET analyses. The as-developed GCOZS composite catalysts exhibit an effective HER performance through a less Tafel slope of 71 mV dec−1 and low overpotential of − 304 mV at the current density of 10 mA/cm2. Moreover, the GCOZS composite catalysts exhibit excellent aqueous Rhodamine B (Rh B) dye degradation efficiency (∼93.05 %) within 180 min of light exposure. In addition, the catalytic performance of the ternary heterostructure composite is improved owing to the efficient charge transfer pathway among intimate interfacial contacts with a high specific surface area by incorporating functional ZnS QDs and Co3O4 to the g-C3N4 layer surface. As a result, providing an efficient bifunctional electro/photocatalyst system has been of great interest for realistic energy conversion in environmental applications, which may offer new designing possibilities for novel catalysts utilizing carbon-based materials.

Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation

Energy consumption data are crucial for various smart energy management applications, such as demand forecasting, customer segmentation, and energy efficiency analysis. However, collecting sufficient training data can be a challenge due to data privacy concerns, technical barriers, and cost issues. This paper presents a novel data generation model called Federated-WDCGAN, which combines federated learning with an enhanced generative adversarial network to generate scalable and realistic energy consumption data while preserving privacy. The proposed model is trained in a federated manner, where the generator is trained to generate realistic energy consumption data, and the discriminators are trained to distinguish the generated data from real data. The proposed model is evaluated through analysis of the generated data and its use in a machine learning task for the classification of household characteristics, demonstrating its ability to produce high-quality data comparable to real data in terms of statistics, patterns, and classification performance. The privacy-preserving capabilities of the model are also assessed using non-independently identically distributed and independently identically distributed data, achieving satisfactory results.

Novel secured distributed energy management structure for solar based smart grids incorporating miners

Addressing the blockchain technology (BCT) challenges, such as the high energy consumption is unavoidable in smart power systems to take maximum advantage. As an antithetical point, there is a structural duality between using BCT in the electrical grid due to its advantages and energy consumption. To this end, this research work is an inclination toward controlling the miner’s energy consumption based on a realistic and efficacious two-stage energy mechanism toward the distributed energy concept and optimal placement of data mining devices. In this circumstance, such a new structure allows miners to mainly sustain their hash rates as a prominent index for security achievement and optimally decrease their cost. This paper suggests a novel distributed model for miners coincided with the smart electrical grid getting inspired by the Prima-Dual Method of Multipliers (PDMM) based distributed approach to settle down the needed power bartering between miners and energy supply resources. In addition, for a similar purpose, it seems to reach the ideal resolution needs to perform the modified reaffirmation of miners (the mining pool) within the secured grid. To execute this task, this research recommends the Intelligent Priority Selection (IPS) techniques as well. When all is said and done, the result of this approach can aid researchers with performing in the field of improving social advantages by the usage of the suggested method.

Test signal generator for simulating electron events from a momentum microscope

Preparing experiments that utilize free electron lasers is challenging because obtaining access to the facility is difficult. The integration of detectors into the data acquisition system needs to be tested before the beamtime starts. In this study, we develop a test signal generator that simulates the signals from a delay-line detector used in a time-of-flight electron spectrometer. The output signals of the simulator are connected to the time-to-digital converter electronics and simulate a realistic energy spectrum of a real spectrometer. Through this method, the detector electronics can be tested and integrated into the data acquisition system at the free electron laser before the actual instrument is available on site. In addition, the saturation behavior of the signal processing chain can be tested by changing the number of simulated electrons per pulse.

Modeling of spark ignition in gaseous mixtures using adaptive mesh refinement coupled to the thickened flame model

In standard LES of spark ignition using the thickened flame model (TFM) and a static mesh, in other words without the use of adaptive mesh refinement (AMR), a progressive increase of the thickening factor F from unity to the nominal value is a consequence of the progressive increase of the cell size away from the spark plug. If the refined mesh is limited to a small region around the spark plug, the computational cost is limited but F will increase very rapidly as soon as the flame leaves the spark region, leading to an erroneous prediction of the total heat release rate as shown in this study. Conversely, using a large refined mesh region will allow such a progressive increase of F but at the cost of a strongly increased computational cost. To solve this issue, two new spark ignition models called TFM-AMR-I and TFM-AMR-I-Ftrans are proposed for TFM. These models are based on TFM-AMR Mehl et al.(2021) which employs AMR to ensure the desired thickening level and a constant number of grid points across the flame. It also uses a realistic spark plug energy model as in Colin et al.(2019). To avoid the aforementioned issue, a physical criterion is proposed to start the increase of F only when the flame kernel can be considered self-sustained. As this criterion is given by a model transport equation, the possible convection of the kernel outside the spark region doesn’t prevent the model to work properly. Secondly, in both models the increase of F is no more dictated by the cell size of the static mesh but it is given by an additional transport equation that controls the growth rate of F in time.These models are applied to the laminar propane/air ignition experiment presented in Colin et al.(2019). It is first shown that the experimental MIE is correctly recovered when using a DNS type resolution, that is, F=1 at any time. Then both proposed LES ignition models are applied to the same case: they recover correctly the MIE of the DNS provided the stretch correction of Quillatre (2014) is applied, which confirms the need to correct the effect of stretch with TFM. At the same time, TFM-AMR-I under-predicts the total heat release rate, which is attributed to the jump of the thickening factor at each AMR level removal. On the contrary, TFM-AMR-I-Ftrans compares better with the DNS, which is attributed to the continuous evolution of F with this model. Finally, it is shown that both models lead to a computational time reduction of a factor between 2 and 6 compared to static mesh TFM simulations.

An Effective Charge Neutralization Enabled by Graphene Overlayer in Ambient Pressure XPS Measurements of Insulators

AbstractAmbient pressure XPS is a powerful technique capable of performing measurements with samples kept at mbar pressure. The filled gas, also X‐ray ionized, provides electrons to neutralize positive charges built up on insulating samples. However, this convenient neutralization scheme does not solve the charging problem completely. In this study, the effectiveness of how the electrons generated from conducting mechanical parts mounted in the immediate front of insulating samples neutralize positive charges on the samples during XPS measurements is tested. The mechanical parts range from fine gold mesh, holey carbon film to graphene monolayer, and measured insulating samples are polished sapphire and rough pellets of CaCO3 powder. All these mechanical add‐ons reduce the charging to different degrees at the expense of sample signal. Amazingly, the graphene monolayer is found to effect perfect charge neutralization for both smooth sapphire and rough CaCO3 pellet, evidenced by realistic spectral profile and binding energy values. The finding that an untreated CaCO3 pellet is often terminated with Ca(OH)(HCO3) is consistent with earlier reports. It is speculated that a likely conformal covering with graphene on the sample with rough morphology holds the key to its superb performance in charge compensation.

A full-scale Monte Carlo Potts model and real time conversion under non-uniform temperature distribution

Prediction of microstructure evolution is important to design structural materials, and grain growth is the most fundamental but important one which determines mechanical properties. The Monte Carlo Potts is a widespread simulation model for grain growth with high computational efficiency but has not been considered suitable for the quantitative prediction of practical processes since the physical meaning of variables is not fully understood yet. Efforts have been made to assign realistic physical meaning to variables. The existing equation for converting simulation time to real time involves a strong temperature dependence, which causes a problem under non-uniform temperature distribution, as in additive manufacturing. This problem could be solved by assigning a temperature dependence to the simulation result, which was possible by using realistic temperature and energy values. We found that the simulated activation energy for grain growth could be equated to the experimental value by adjusting the activation energy for grain boundary migration within a reasonable range in the simulation. The modified model made the time conversion equation completely temperature-independent and was free from the above-mentioned problem in time conversion. The Monte Carlo Potts simulation can now more reasonably predict grain growth in various materials under various temperature conditions.