搜索结果：ACS energy letters

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. This study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption

Using repeat imaging of a galaxy cluster taken over a seventeen-year baseline, we examine the impact that degraded Charge Transfer Efficiency (CTE) has on photometric measurements of extended sources using the ACS/WFC on HST. We examine how measured brightnesses depend on time since ACS installation, source location on the WFC detectors, source brightness, and local background level in individual exposures. We find that global brightness measurements using large apertures are generally reliable within $\sim$0.05 magnitudes across the WFC detectors if exposure backgrounds are above $20e^-/{pixel}$ and sources are brighter than $\sim300e^-$ in a single exposure. However, brightness measurements on smaller scales can suffer deficiencies in excess of 0.1 mags (sometimes, significantly more) in recent data unless sources are very close to the CCD serial registers ($\lesssim 512$ pixels), or brighter than $\sim3000\,e^-$ in a single exposure. We also show how degraded CTE can result in artificial asymmetries in galaxy light distributions, which are largely mitigated if backgrounds are $>20e^-/{pixel}$ and targets are not far ($>1536$ pixels) from the serial registers. As expected,

We present a new approach to identify satellite trails (or other linear artifacts) in ACS/WFC imaging data using a modified Radon Transform. We demonstrate that this approach is sensitive to features with mean brightness significantly below the background noise level, and it is resistant to the influence of bright astronomical sources (e.g., stars, galaxies) in most cases. Comparing with a set of satellite trails identified by eye, we find a trail recovery rate of 85\% and a false detection rate (after removing diffraction spikes that are easily filtered) of 2.5\%. By performing an analysis using a much larger ACS/WFC data set where false trails are identified by their persistence across multiple images of the same field, we identify the Radon Transform parameter space and image properties where our algorithm is unreliable, and estimate a false detection rate of $\sim10\%$ elsewhere. We apply our method to ACS/WFC data taken between 2002 and 2022 to determine both the frequency of satellite trail contamination in science data and also the typical trail brightness as a function of time. We find the rate of satellite trail contamination has increased by approximately a factor of two

Hydrogen energy plays an important role in the transformation of low-carbon energy, and electric hydrogen coupling will become a typical energy scenario. Aiming at the operation flexibility of low-carbon electricity hydrogen coupling system with high proportion of wind power and photovoltaic, this paper studies the flexibility margin of electricity hydrogen coupling energy block based on model predictive control (MPC). By analyzing the power exchange characteristics of heterogeneous energy, the homogenization models of various heterogeneous energy sources are established. According to the analysis of power system flexibility margin, three dimensions of flexibility margin evaluation indexes are defined from the dimension of system operation, and an electricity hydrogen coupling energy block scheduling model is established. The model predictive control algorithm is used to optimize the power balance operation of the electro hydrogen coupling energy block, and the flexibility margin of the energy block is quantitatively analyzed and calculated. Through the example analysis, it is verified that the calculation method proposed in this paper can not only realize the on-line power balance

This letter presents an innovative energy harvesting (EH) and communication scheme for Internet of Things (IoT) devices by utilizing the emerging noise modulation (Noise-Mod) technique. Our proposed approach embeds information into the mean value of real Gaussian noise samples, enabling simultaneous power transfer and data transmission. We analyze our system under the Rician fading channels with path loss and derive the bit error probability (BEP) expression. Our simulation results demonstrate that the proposed scheme outperforms conventional modulation schemes in terms of energy harvesting capability across various channel conditions. This scheme offers a novel solution by directly embedding data into the noise-modulated signal to enable information decoding through mean-based detection. Furthermore, it increases energy harvesting capability thanks to the utilized Gaussian waveform.

Recently, the ACS team applied an Ubercal framework to assess the photometric repeatability of stars observed across the WFC detector using 15 years of post-SM4 calibration data in the globular cluster 47 Tuc (Ryan et al., 2024). A surprising finding was an apparent 0.05 mag global difference in sensitivity between the WFC1 and WFC2 chips, which had not been seen in prior tests of sensitivity variations around the field-of-view. Given the many degenerate variables within the Ubercal framework such as CTE losses, time-dependent sensitivity, and flat-field corrections, we obtained new calibration data to perform a straightforward test of the reported $\sim$5$\%$ flux offset between detectors. We observed three white dwarf standards with three filters at four positions on the detector (each on a different amplifier), but with the same number of x and y pixel transfers to mitigate differential CTE-related effects. For the F606W and F814W filters, the agreements are good to 0.4$\%$ on average, and always 1$\%$ or better in individual cases. The consistency of these two filters over all three stars and the four dither positions provides very strong evidence against the large global sensi

We study the Compton-rocket effect of strong radiation force accelerating electrons in an opaque fireshell (or fire spot) of dense photons and electron-positron pairs, whose temperature is spatially inhomogeneous and exceeds the electron mass. We find the possibility of the charged-particle acceleration and the avalanche runaway process, leading to a non-trivial probability of ultra-high-energy (UHE) electrons and protons, which subsequently produce very-high-energy (VHE) photons and neutrinos. In a simplified one-dimensional model, we qualitatively show such peculiar dynamics using the fireball, Gamma-Ray Burst central engine, whose inner part inflows and forms a gravitationally trapped fireshell (halo) around the horizon of a black hole. The fireshell is metastable, cooling via UHE particle emissions and blackbody radiation. We calculate the UHE particle luminosity varying in time, and discuss the peculiar features of such produced UHE particles, which lead to VHE particles, in connection with possible numerical simulations, observations and experiments.

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constra

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and infinity-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional st

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. The Mixed-Attention is applied to fit the conditions of the equipment, additionally, Multi-Agent Soft Actor-Critic(MA-SAC) and (Multi-Agent Win or Learn Fast Policy Hill-Climbing)MA-WoLF-PHC are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5kW in 24 hours and achieve a cost reduction of 16

In this minireview article, we examine the inconsistent results of thermal parameters derived from various models in high-energy collisions. Through a comprehensive literature review and based on the average transverse momentum or the root-mean-square transverse momentum, we propose model-independent parameters to address these inconsistencies. The relevant parameters include: the initial temperature, the effective temperature, the kinetic freeze-out temperature, and the average transverse velocity. Our findings indicate that these four parameters are larger in central collisions, within central rapidity regions, at higher energies, and in larger collision systems. As collision energy increases, excitation functions for all four parameters rise rapidly (slowly) within ranges below (above) approximately 7.7 GeV. At higher energies (>39) GeV, fluctuations occur in trends for these excitation functions, with only slight changes observed in their growth rates. Additionally, this work reveals a mass-dependent multi-temperature scenario pertaining to both initial states and kinetic freeze-out processes.

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Ne

After reviewing the sound speeds in various forms and conditions of matter, we investigate the sound speed of hadronic matter that has decoupled from the hot and dense system formed during high-energy collisions. We comprehensively consider factors such as energy loss of the incident beam, rapidity shift of leading nucleons, and the Landau hydrodynamic model for hadron production. The sound speed is related to the width or standard deviation of the Gaussian rapidity distribution of hadrons. The extracted square speed of sound lies within a range from 0 to 1/3 in most cases. For scenarios exceeding this limit, we also provide an explanation.

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with opt

Knowledge of the centre-of-mass energy at LEP2 is of primary importance to set the absolute energy scale for the measurement of the W-boson mass. The beam energy above 80 GeV is derived from continuous measurements of the magnetic bending field by 16 NMR probes situated in a number of the LEP dipoles. The relationship between the fields measured by the probes and the beam energy is calibrated against precise measurements of the average beam energy between 41 and 55 GeV made using the resonant depolarisation technique. The linearity of the relationship is tested by comparing the fields measured by the probes with the total bending field measured by a flux loop. This test results in the largest contribution to the systematic uncertainty. Several further corrections are applied to derive the the centre-of-mass energies at each interaction point. In addition the centre-of-mass energy spread is evaluated. The beam energy has been determined with a precision of 25 MeV for the data taken in 1997, corresponding to a relative precision of 2.7x10^{-4}. This is small in comparison to the present uncertainty on the W mass measurement at LEP. However, the ultimate statistical precision on the W

Here, we present the angular diameter distance measurement obtained from the measurement of the Baryonic Acoustic Oscillation (BAO) feature using the completed Dark Energy Survey (DES) data, summarizing the main results of [Phys. Rev. D 110, 063514] and [Phys. Rev. D 110, 063515]. We use a galaxy sample optimized for BAO science in the redshift range 0.6 < z < 1.2, with an effective redshift of $z_{\rm eff}$ = 0.85. Our consensus measurement constrains the ratio of the angular distance to the sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51 $\pm$ 0.41. This measurement is found to be 2.13$σ$ below the angular BAO scale predicted by Planck. To date, it represents the most precise measurement from purely photometric data, and the most precise from any Stage-III experiment at such high redshift. The analysis was performed blinded to the BAO position and is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics.

An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.

This white paper describes the LSST Dark Energy Science Collaboration (DESC), whose goal is the study of dark energy and related topics in fundamental physics with data from the Large Synoptic Survey Telescope (LSST). It provides an overview of dark energy science and describes the current and anticipated state of the field. It makes the case for the DESC by laying out a robust analytical framework for dark energy science that has been defined by its members and the comprehensive three-year work plan they have developed for implementing that framework. The analysis working groups cover five key probes of dark energy: weak lensing, large scale structure, galaxy clusters, Type Ia supernovae, and strong lensing. The computing working groups span cosmological simulations, galaxy catalogs, photon simulations and a systematic software and computational framework for LSST dark energy data analysis. The technical working groups make the connection between dark energy science and the LSST system. The working groups have close linkages, especially through the use of the photon simulations to study the impact of instrument design and survey strategy on analysis methodology and cosmological pa