Classical Energy Research Articles

The Interplay between Parameters of Light Pollution and Energy Efficiency for Outdoor Amenity Lighting

This paper is related to light pollution and the energy efficiency of outdoor amenity lighting. It concerns the standard design assessment parameters of light pollution, the Upward Light Ratio (ULR) and Upward Flux Ratio (UFR), and the classic energy efficiency parameter—Normalized Power Density (NPD). The motivation for this research was the observation of certain inaccuracies related to the applicability and interpretation of these parameters in practice and the lack of connection between parameters of light pollution and energy efficiency. The multi-variant computer simulations of the exemplary large-area parking lot lighting system were conducted. Over four hundred cases were carefully analyzed. Individual cases differ in the shape of the task area, luminaire arrangements, mounting height, luminous intensity distribution, aiming, and maintenance factor. The results confirmed that the criteria values of ULR and UFR are often overestimated for modern luminaires, which emit luminous flux emitted only downwards. In this case, the ULR and UFR values do not exceed the criteria values for even zones with lower ambient brightness. Thus, lighting solutions with much lower energy efficiency easily meet the requirements of these parameters. This situation is not rational. So, it is crucial to make the criteria of ULR and UFR much more stringent in all environmental zones. Moreover, the research confirms a strong positive linear correlation between UFR and NPD (0.92, p < 0.001), which means that light pollution can be reduced by ensuring an appropriate level of energy efficiency. It is a great help in designing sustainable outdoor amenity lighting.

Intelligent optimal demand response implemented by blockchain and cooperative game in microgrids

AbstractDistributed renewable energy supply (RES) is a new pattern for the transformation of power grids. As a characteristic case of RES, microgrids have an advantage in convenient operation. However, the energy management of microgrids remains as a major concern. With the emergence of the decentralized paradigm, blockchain potentially provides a reliable energy data metering and payment for the whole life cycle of energy management. In particular, the demand response (DR) in the microgrid can stimulate demanders to spontaneously manage their load consumption and maintain the balance of the energy trading market. To achieve optimal DR, a dynamic pricing strategy under the blockchain and game‐theoretic approach is proposed. First, the blockchain‐based architecture is applied to ensure the reliability of energy data and lay a foundation of binding agreements for games. Then, the pricing mechanism under the cooperative game is formulated to optimize DR. Moreover, to help resolve the optimal response quantity and reduce the supply punishment of the RES providers, the DR requires accurate forecasting of the energy generation and consumption profiles. Therefore, an ensemble method Long Short‐Term Gate Support (LSTGS) is designed to forecast the RES and load power for intelligent agent to make decision on effective energy scheduling and DR. Taking the classic distributed energy context as a case study, we demonstrate the effectiveness of our approach and show that it can achieve DR profits maximized and improve the stability of the energy‐trading market.

Next-Month Prediction of Hourly Solar Irradiance based on Long Short-Term Memory Network

Today, in parallel with the population growth and the advancement of technology, development concerns have started to arise in terms of country administrators. Therefore, alternative solutions to classical energy sources are sought. Renewable energy sources are one of the preferred energy sources today. The popularity of renewable energy sources, including solar energy, is increasing day by day. Solar energy has the potential and accessibility to spread faster than other renewable energy sources. Since Türkiye is located in a region with a high potential in terms of solar energy, which is generally called the sun belt, it is a right decision to prefer solar energy as an energy source in our region. In this study, time series prediction using Long Short-Term Memory (LSTM) Network method is used for short-term solar irradiance estimation. In order to demonstrate the success of the results, a comparison was made with the Artificial Neural Network (ANN) method. Finally, prediction results of solar irradiance were compared with statistical tests and error analyzes were given in numerically.

A necessary and sufficient condition of blow-up for a nonlinear equation

We investigate a nonlinear equation with quadratic nonlinearities, including a nonlinear model in Silva and Freire (J. Differ. Equ. 320:371–398, 2022). Using the classical energy estimate methods, we give a necessary and sufficient condition of blow-up of solutions to nonlinear equations. We answer a problem pointed out by Silva and Freire (J. Differ. Equ. 320:371–398, 2022).

Nucleation-mediated reshaping of facetted metallic nanocrystals: Breakdown of the classical free energy picture.

Shape stability is key to avoiding degradation of performance for metallic nanocrystals synthesized with facetted non-equilibrium shapes to optimize properties for catalysis, plasmonics, and so on. Reshaping of facetted nanocrystals is controlled by the surface diffusion-mediated nucleation and growth of new outer layers of atoms. Kinetic Monte Carlo (KMC) simulation of a realistic stochastic atomistic-level model is applied to precisely track the reshaping of Pd octahedra and nanocubes. Unexpectedly, separate constrained equilibrium Monte Carlo analysis of the free energy profile during reshaping reveals a fundamental failure of the classical nucleation theory (CNT) prediction for the reshaping barrier and rate. Why? Nucleation barriers can be relatively low for these processes, so the system is not locally equilibrated before crossing the barrier, as assumed in CNT. This claim is supported by an analysis of a first-passage problem for reshaping within a master equation framework for the model that reasonably captures the behavior in KMC simulations.

Optimal Integration of D-STATCOMs in Radial and Meshed Distribution Networks Using a MATLAB-GAMS Interface

This paper proposes an interconnection of the MATLAB and GAMS software interfaces, which were designed based on a master-slave methodology, to solve the mixed-integer nonlinear programming (MINLP) model problem associated with the problem regarding the optimal location and sizing of static distribution compensators (D-STATCOMs) in meshed and radial distribution networks, considering the problem of optimal reactive power flow compensation and the fact that the networks have commercial, industrial, and residential loads for a daily operation scenario. The objective of this study is to reduce the annual investment and operating costs associated with energy losses and the installation costs of D-STATCOMs. This objective function is based on the classical energy budget and the capacity constraints of the device. In the master stage, MATLAB software is used to program a discrete version of the sine-cosine algorithm (DSCA), which determines the locations where the D-STATCOMs will be installed. In the slave stage, using the BONMIN solver of the GAMS software and the known locations of the D-STATCOMs, the MINLP model representing the problem under study is solved to find the value of the objective function and the nominal power of the D-STATCOMs. To validate the effectiveness of the proposed master-slave optimizer, the 33-node IEEE test system with both radial and meshed topologies is used. With this test system, numerical comparisons were made with the exact solution of the MINLP model, using different solvers in the GAMS software, the genetic-convex strategy, and the discrete-continuous versions of the Chu and Beasley genetic algorithm and the salp swarm optimization algorithm. The numerical results show that DSCA-BONMIN achieves a global solution to the problem under study, making the proposed method an effective tool for decision-making in distribution companies.

Primordial non-Gaussianity from Galilean Genesis without strong coupling problem

Galilean Genesis is generically plagued with a strong coupling problem, but this can be avoided depending on the hierarchy between a classical energy scale of genesis and a strong coupling scale. In this paper, we investigate whether or not the models of Galilean Genesis without the strong coupling problem can explain the statistical properties of the observed CMB fluctuations based on two unified frameworks of Galilean Genesis. By focusing on the class in which the propagation speeds of the scalar and tensor perturbations are constant, we show that the models avoiding strong coupling and allowing a slightly red-tilted scalar power spectrum suffer from an overproduction of a scalar non-Gaussianity.

Development of a three-dimensional semi-coherent energy flux propagation model

The classic energy flux propagation model integrates an averaged intensity distribution over elevation angle for an azimuthally symmetric waveguide, and the method has also been extended to reincorporate some of the coherence in the mode-sum cross product, producing caustic features and convergence zones. Some ocean environments with asymmetric range-dependent bathymetry and acoustic medium properties can refract the acoustic wave horizontally which N-by-2D models cannot capture due to their assumed symmetry. Fully three-dimensional models can capture horizontal refraction but are typically computationally expensive to execute. Further development and study of the energy flux method is worthwhile since it avoids root-finding, is not a marching solution, and it invokes mode number invariance under the adiabatic modes assumption to map propagation angles and directly integrate the intensity distribution at the receiver location. A first-pass at analytically deriving a generalizable three-dimensional energy flux model was presented previously, and this current study focuses on further analytical development, numerical implementation, and efforts to verify the model using other three-dimensional acoustic propagation models.

Collective Proton Transfers in Cyclic Water–Ammonia Tetramers: A Path Integral Machine-Learning Study

We present results from machine-learning-based path integral molecular dynamics simulations that describe isomerization paths articulated via collective proton transfers along mixed, cyclic tetramers combining water and ammonia at cryogenic conditions. The net result of such isomerizations is a reverse of the chirality of the global hydrogen-bonding architecture along the different cyclic moieties. In monocomponent tetramers, the classical free energy profiles associated with these isomerizations present the usual symmetric double-well characteristics whereas the reactive paths exhibit full concertedness among the different intermolecular transfer processes. Contrastingly, in mixed water/ammonia tetramers, the incorporation of a second component introduces imbalances in the strengths of the different hydrogen bonds leading to a partial loss of concertedness, most notably at the vicinity of the transition state. As such, the highest and lowest degrees of progression are registered along OH···N and O···HN coordinations, respectively. These characteristics lead to polarized transition state scenarios akin to solvent-separated ion-pair configurations. The explicit incorporation of nuclear quantum effects promotes drastic depletions in the activation free energies and modifications in the overall shape of the profiles which include central plateau-like stages, indicating the prevalence of deep tunneling regimes. On the other hand, the quantum treatment of the nuclei partially restores the degree of concertedness among the evolutions of the individual transfers.

A regular version of the extremal RN spacetime

A modified extremal Reissner-Nordstrom geometry, void of singularities, is proposed in this work, by means of an exponential factor depending on a positive constant k. All the metric coefficients are positive and finite and the spacetime has no any horizon. The curvature invariants are regular at the origin of coordinates and at infinity. The energy conditions for the stress tensor associate to the imperfect fluid are investigated. The gravitational field presents repulsive properties near the gravitational radius associated to the mass m. With the choice k=1/m, the Komar energy WK of the mass m changes its sign at r=λ (λ is the Compton wavelength of m), when the classical energy mc2 equals the energy ħc/r.

Derivation of an elastic shell model of order as tends to infinity

Starting from three-dimensional linear elasticity and performing the dimensional reduction by integration over the thickness, we derive a general form of the areal strain energy density for elastic shells. To obtain the new constitutive model, we do not approximate the deformation fields as polynomials in the thickness coordinate, but rather we keep all terms in the thickness-wise series expansions. As a result, we deduce the explicit form of the shell strain energy density in which the constitutive coefficients are expressed as integrals depending on the thickness h and on the initial curvature. Then, to obtain the shell model of order , we expand the integral coefficients in the strain energy function as power series of h and truncate them to the power . In the case , we recover the classical strain energy density for combined bending and stretching of linear shells, which leads to the Koiter model. Finally, we prove that the proposed shell strain energy function is coercive for any , as well as for the general case .

Statistical Mechanical Design Principles for Coarse-Grained Interactions across Different Conformational Free Energy Surfaces.

Systematic bottom-up coarse-graining (CG) of molecular systems provides a means to explore different coupled length and time scales while treating the molecular-scale physics at a reduced level. However, the configuration dependence of CG interactions often results in CG models with limited applicability for exploring the parametrized configurations. We propose a statistical mechanical theory to design CG interactions across different configurations and conditions. In order to span wide ranges of conformational space, distinct classical CG free energy surfaces for characteristic configurations are identified using molecular collective variables. The coupling interaction between different CG free energy surfaces can then be systematically determined by analogy to quantum mechanical approaches describing coupled states. The present theory can accurately capture the underlying many-body potentials of mean force in the CG variables for various order parameters applied to liquids, interfaces, and in principle proteins, uncovering the complex nature underlying the coupling interaction and imparting a new protocol for the design of predictive multiscale models.

Single-Ended Readout Depth-of-Interaction Measurements Based on Random Forest Algorithm

The depth-of-interaction (DOI) information can reduce parallax errors and improve the spatial resolution of positron emission tomography (PET) imaging. Current DOI detectors, with either single-ended readout design or dual-ended readout design, usually require substantial changes to the crystal arrays and/or readout electronics. In this work, we proposed a single-ended readout DOI estimation approach with minimal changes in the detector hardware design, using a supervised machine learning algorithm, random forest (RF). The energy value measured from one end of the crystal, combined with sample points of the pulse signal, were used as inputs to the DOI estimation algorithm, which provided a DOI performance close to that of the dual-ended readout design. We compared the proposed single-ended RF method with the classic dual-ended energy ratio method and a dual-ended version of the RF method, using positioning error and resolution as performance metrics for DOI estimation. All three methods achieved average absolute DOI positioning errors of < 1.00 mm and average DOI resolutions of < 2.20 mm full width at half maximum (FWHM). More specifically, we achieved the averaged DOI resolutions of 2.15, 1.80, and 1.93 mm FWHM for the single-ended RF method, dual-ended RF method, and dual-ended energy ratio method, respectively. In conclusion, the single-ended RF method achieved comparable performance without extra photodetectors and readout electronics and may provide a feasible solution for DOI estimation.

Advanced Energy Management Strategy of Photovoltaic/PEMFC/Lithium-Ion Batteries/Supercapacitors Hybrid Renewable Power System Using White Shark Optimizer.

The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed hydrogen, an energy management strategy (EMS) based on the white shark optimizer (WSO) for photovoltaic/PEMFC/lithium-ion batteries/supercapacitors microgrid has been developed. The EMSs distribute the load demand among the photovoltaic, PEMFC, lithium-ion batteries, and supercapacitors. The design of EMSs must be such that it minimizes the use of hydrogen while simultaneously ensuring that each energy source performs inside its own parameters. The recommended EMS-based-WSO was evaluated in regard to other EMSs regarding hydrogen fuel consumption and effectiveness. The considered EMSs are state machine control strategy (SMCS), classical external energy maximization strategy (EEMS), and optimized EEMS-based particle swarm optimization (PSO). Thanks to the proposed EEMS-based WSO, hydrogen utilization has been reduced by 34.17%, 29.47%, and 2.1%, respectively, compared with SMCS, EEMS, and PSO. In addition, the efficiency increased by 6.05%, 9.5%, and 0.33%, respectively, compared with SMCS, EEMS, and PSO.

An Elastic Quartic Twist Theory for Chromonic Liquid Crystals

Chromonic liquid crystals are lyotropic materials which are attracting growing interest for their adaptability to living systems. To describe their elastic properties, the classical Oseen-Frank theory requires anomalously small twist constants and (comparatively) large saddle-splay constants, so large as to violate one of Ericksen’s inequalities, which guarantee that the Oseen-Frank stored-energy density is bounded below. While such a violation does not prevent the existence and stability of equilibrium distortions in problems with fixed geometric confinement, the study of free-boundary problems for droplets has revealed a number of paradoxical consequences. Minimizing sequences driving the total energy to negative infinity have been constructed by employing ever growing needle-shaped tactoids incorporating a diverging twist (Paparini and Virga in Phys. Rev. E 106: 044703, 2022). To overcome these difficulties, we propose here a novel elastic theory that extends for chromonics the classical Oseen-Frank stored energy by adding a quartic twist term. We show that the total energy of droplets is bounded below in the quartic twist theory, so that the known paradoxes are ruled out. The quartic term introduces a phenomenological length in the theory; this affects the equilibrium of chromonics confined within capillary tubes. Use of published experimental data allows us to estimate .

Detection of Outliers in Time Series Power Data Based on Prediction Errors

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.

Phosphorus-centered ion-molecule reactions: benchmark ab initio characterization of the potential energy surfaces of the X- + PH2Y [X, Y = F, Cl, Br, I] systems.

In the present work we determine the benchmark relative energies and geometries of all the relevant stationary points of the X- + PH2Y [X, Y = F, Cl, Br, I] identity and non-identity reactions using state-of-the-art electronic-structure methods. These phosphorus-centered ion-molecule reactions follow two main reaction routes: bimolecular nucleophilic substitution (SN2), leading to Y- + PH2X, and proton transfer, resulting in HX + PHY- products. The SN2 route can proceed through Walden-inversion, front-side-attack retention, and double-/multiple-inversion pathways. In addition, we also identify the following product channels: H--formation, PH2-- and PH2-formation, 1PH- and 3PH-formation, H2-formation and HY + PHX- formation. The benchmark classical relative energies are obtained by taking into account the core-correlation, scalar relativistic, and post-(T) corrections, which turn out to be necessary to reach subchemical (<1 kcal mol-1) accuracy of the results. Classical relative energies are augmented with zero-point-energy contributions to gain the benchmark adiabatic energies.

Concerning classical forces, energies, and potentials for accelerated point charges

Although expressions for energy densities involving electric and magnetic fields are exactly analogous, their connections to forces and electromagnetic potentials are vastly different. For electrostatic situations, changes in the electric energy can be related directly to electric forces and to the electrostatic potential. In contrast, discussions of magnetic forces and energy changes involve two fundamentally different situations. For charged particles moving with constant velocities, the changes in both electric and magnetic field energies are provided by the external forces that keep the particles' velocities constant; there are no Faraday acceleration electric fields in this situation. However, for particles that change speed, the changes in magnetic energy density are related to acceleration-dependent Faraday electric fields. Current undergraduate and graduate textbooks deal only with highly symmetric situations, where the Faraday electric fields are easily calculated from the time-changing magnetic flux. However, in situations that lack high symmetry, such as the magnetic Aharonov–Bohm situation, the back (Faraday) acceleration electric fields of point charges may seem unfamiliar. In this article, we present a simple unsymmetric example and analyze it using the Darwin Lagrangian. In all cases involving changing velocities of the current carriers, it is the work done by the back (Faraday) acceleration electric fields that balances the magnetic energy changes.

Estimation of anisotropic bending rigidities and spontaneous curvatures of crescent curvature-inducing proteins from tethered-vesicle experimental data.

The Bin/amphiphysin/Rvs (BAR) superfamily proteins have a crescent binding domain and bend biomembranes along the domain axis. However, their anisotropic bending rigidities and spontaneous curvatures have not been experimentally determined. Here, we estimated these values from the bound protein densities on tethered vesicles using a mean-field theory of anisotropic bending energy and orientation-dependent excluded volume. The dependence curves of the protein density on the membrane curvature are fitted to the experimental data for the I-BAR and N-BAR domains reported by C. Prévost et al. Nat. Commun., 2015, 6, 8529 and F.-C. Tsai et al. Soft Matter, 2021, 17, 4254-4265, respectively. For the I-BAR domain, all three density curves of different chemical potentials exhibit excellent fits with a single parameter set of anisotropic bending energy. When the classical isotropic bending energy is used instead, one of the curves can be fitted well, but the others exhibit large deviations. In contrast, for the N-BAR domain, two curves are not well fitted simultaneously the anisotropic model, although it is significantly improved compared to the isotropic model. This deviation likely suggests a cluster formation of the N-BAR domains.

Can an Energy Autarky Private House be Economical? An Analysis Based on Germany

The energy self-sufficiency of private households is a very interesting topic against the background of the current developments in the world, such as power failures as a result of weather catastrophes and new infrastructure challenges as a result of transport electrification. There are various options available for achieving this goal. In the context of this work, a newly built single-family house in Germany with a photovoltaic system with a power storage system and a heat pump with surface heat collectors will be used to investigate whether energy autarky (for electricity and heat) can be economically designed - in comparison to a classic energy supply with electricity from the grid, and a gas condensing boiler heating system. The results show that, through the high gas and electricity prices, scenarios in Germany allow such an economical operation compared to the classic energy supply. However, a condition for this is always that there is a connection to the grid in order to be able to compensate for the differences between electricity supply and demand.