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A system for combined heat and electric power generation, preferably including a heat reservoir and one or more electric generators, each preferably including a heat source and an energy converter. A method for combined heat and electric power generation, preferably including activating an electric generator, deactivating the electric generator, and/or providing heat from a heat reservoir.
The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.
An engineering-scale electrochemical processing skid is proposed to perform the denitration of Hanford tank waste, which would help to mitigate a key process concern with the direct feed processing of the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The reduction of nitrates and organic compounds in the waste feed will directly reduce hazardous NOx and ammonia gases generated during the vitrification process, which in turn will aid in addressing potential regulatory and safety challenges associated with processing large volumes of tank waste. This paper highlights the past legacy work, project layout, accomplishments from Phase 1 and research and development envisioned for Phase 2. An innovative electrochemical denitration and caustic generation (EDCGe) process was demonstrated for the pretreatment of tank waste at the Savannah River Site (SRS) in the early 2000s. The denitration electrolyzer, off-gas abatement system, and caustic generator electrolyzer are being developed with the intent that the denitration electrolyzer will convert nitrate and nitrite anions to nitrogen gas while also yielding other gaseous byproducts, which may include N2O, NH3, VOCs, and H2. The gaseous byproducts will be managed via a tandem off-gas catalyst-bed treatment system. The caustic generation electrolyzer will recycle NaOH from the feed to produce a clean caustic stream for use within the batching tanks at Hanford, aiding in the preparation of waste for WTP. The reduction in hazardous emissions and improved waste treatment processes provides a robust solution for nuclear waste management, contributing to environmental safety and regulatory compliance. The EDCGe technology is being adapted, modified, and updated for the preparation of the Direct Feed-High Level Waste (DF-HLW) flowsheet at Hanford. Phase 1 demonstrated a bench-scale proof-of-concept for reactions involving the denitration electrolyzer and gas phase abatement of ammonia. The electrochemical technology is drawing on the scientific outcomes that were reported in the legacy work. The results from Phase 1 demonstrated the viability of the EDCGe system in reducing the nitrogen species of simple non-radioactive waste simulants. Commercially available alloys used as electrode materials and membranes are being studied for the denitration and caustic generation electrolyzers. The continuation of this project holds promise for broader applications, such as energy-efficient ammonia production, and contributes significant advancements in nuclear waste management. Additional material discovery has been investigated into ceramic Na super ion conductive (NaSICON) materials and off-gas abatement catalyst discovery. NaSICON is of interest for selective transport of Na within the electrolyzers to make a clean caustic stream. Future integration and optimization efforts, informed by Phase 1 results and ongoing research, will continue to drive advancements in nuclear waste management technology. The technology developed for the EDCGe treatment of tank waste will also have broader potential to inform other fields, such as energy-efficient ammonia production, as well as ammonia abatement catalysis through the lessons learned in electrochemical nitrate reduction. The applications and benefits of this research extend beyond Hanford and the Savannah River Site, supported by a collaborative team of scientists and engineers from national labs, academia, and industry, ensuring a comprehensive approach to solving complex waste treatment challenges. The team is leveraging advanced electrochemical technologies, machine learning, novel catalysts tailored for gaseous nitrogen species, and cutting-edge reactor systems to enhance the process efficiency and effectiveness of the denitration process.
In this work, we devised a new WEC (wave energy converter) called SR-WEC (Surface Riding WEC). The SR-WEC consists of two bodies: the outer cylinder with an armature assembly (body #1) and a magnet assembly (body #2) sliding inside the armature. For the SR-WEC, the relative sliding displacement and velocity are caused by gravity acceleration and the outer cylinder's motions, and they lead to electrical power generation. To evaluate its performance, a numerical simulation tool was developed, which solves the fully-coupled floater-mooring-generator dynamics. During the developing stage, the appropriate hydrodynamics model, sliding mechanics model, mooring dynamics model, and LEG (linear electric generator) electro-magnetic model were independently developed and then fully coupled in time domain to account for the cross-coupling interactions among them. Then, the developed simulation tool was verified component by component against various laboratory tests. Subsequently, systematic parametric studies were conducted with several important design parameters under various wave conditions to enhance power generation. After that, the average output power was evaluated in enlarged operational wave conditions. The present SR-WEC is particularly designed to be efficient at low sea states, which is good since they cover the majority of typical annual sea states.
Generating multiple excitons by a single high-energy photon is a promising third generation solar energy conversion strategy. We demonstrate that multiple exciton generation (MEG) in PbS|CdS Janus-like hetero-nanostructures is enhanced over that of single-component and core/shell nanocrystal architectures, with an onset close to two times the PbS band gap. We attribute the enhanced MEG to the asymmetric nature of the hetero-nanostructure that results in an increase in the effective Coulomb interaction that drives MEG and a reduction of the competing hot exciton cooling rate. Slowed cooling occurs through effective trapping of hot-holes by a manifold of valence band interfacial states having both PbS and CdS character, as evidenced by photoluminescence studies and by ab initio calculations. Using transient photocurrent spectroscopy, we find that the MEG characteristics of the individual nanostructures are maintained in conductive arrays and demonstrate that these quasi-spherical PbS|CdS nanocrystals can be incorporated as the main absorber layer in functional solid-state solar cell architectures. Finally, based upon our analysis, we provide design rules for the next generation of engineered nanocrystals to further improve the MEG characteristics.
A large fraction of the world's population lacks access to the electric grid. Standard photovoltaic (PV) cells can provide a renewable off-grid source of electricity but only produce power from daytime solar irradiance and do not produce power at night. While there have been several theoretical proposals and experimental demonstrations of energy harvesting from the radiative cooling of a PV cell at night, the achieved power density is very low. Here, we construct a device, which incorporates a thermoelectric generator that harvests electricity from the temperature difference between the PV cell and the ambient surrounding. We achieve 50 mW/m 2 nighttime power generation with a clear night sky, with an open-circuit voltage of 100 mV, which is orders of magnitude higher as compared with previous demonstrations. During the daytime, the thermoelectric generator also provides additional power on top of the electric power generated directly from the PV cells. Our system can be used as a continuous renewable power source for both day- and nighttime in off-grid locations.
In this paper, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding method. Our study focuses on the computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems is crucial for second-harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.
Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.
Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.
A nuclear technological complex for producing liquid fuel (methanol) from synthesis gas, obtained in a direct-flow gas generator from coal obtained from the Borodinskoe deposit, is presented. The thermal energy required for the process is delivered from a nuclear reactor with liquid-metal coolant at temperature 700°C. A water-coal suspension with coal-to-water mass ratio 1:4 is delivered into the gas generator along a spiral coil placed in a ring casing into which the warming coolant from the nuclear reactor is delivered. Material and thermal balance of the gas generator is obtained. The synthesis gas at the exit from the gas generator passes through a purification apparatus. Methanol is obtained on a copper catalyst in a circulation reactor at pressure 3 MPa and temperature 270°C. The power of the nuclear reactor is 100 MW. The capacity of the complex is 248 tons/day of methanol.
We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid method can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.
An experimental technology for the removal of insoluble impurities from a horizontal steam generator with purge water during planned shutdowns of the power generating unit is improved through a more representative determination of the concentration of impurities in the purge water ahead of the water cleanup facility and a more precise effective time for the duration of the purge process. Tests with the improved technique at power generating unit No. 1 of the Rostov Nuclear Power Plant show that the efficiency with which insoluble impurities are removed from the steam generator volume was more than two orders of magnitude greater than under the standard regulations.
We theoretically investigate high-order harmonic generation (HHG) from a coherent superposition state of hydrogen atom in a circularly polarized (CP) laser field. The results show that, driven by the CP field, HHG from the coherent superposition state is enhanced by 4 to 5 orders of magnitude compared to that from the ground state. The enhanced intensity is comparable to that of the ground state in a linearly polarized laser field. Based on the classical and quantum analyses, we demonstrate that HHG in the CP laser field mainly arises from the recombination of electrons ionized at non-zero initial positions. The enhancement of HHG from the coherent superposition state is due to the higher ionization rate and also the larger probability density of electrons located far from the nucleus of the involved excited state. The obtained harmonic spectra support the generation of intense isolated attosecond pulses (IAPs) with the ellipticity as high as 0.93. Moreover, we show that the generation of IAPs with large ellipticity is robust against the variation of the carrier envelope phase (CEP) of the laser field. Our results provide a new route toward generating near-CP IAPs, which will be beneficial for ultrafast detection of chiral-sensitive light-matter interactions.
The paper deals with the soft X-ray backlighting based on the X-pinch as a powerful tool for physical studies of fast processes. Proposed are the unique small-size pulsed power generators operating as a low-inductance capacitor bank. These pulse generators provide the X-pinch-based soft X-ray source (hν = 1–10 keV) of micron size at 2–3 ns pulse duration. The small size and weight of pulse generators allow them to be transported to any laboratory for conducting X-ray backlighting of test objects with micron space resolution and nanosecond exposure time. These generators also allow creating synchronized multi-frame radiographic complexes with frame delay variation in a broad range.
In this work, we propose a stable, parallel approach to train Wasserstein conditional generative adversarial neural networks (W-CGANs) under the constraint of a fixed computational budget. Differently from previous distributed GANs training techniques, our approach avoids inter-process communications, reduces the risk of mode collapse and enhances scalability by using multiple generators, each one of them concurrently trained on a single data label. The use of the Wasserstein metric also reduces the risk of cycling by stabilizing the training of each generator. We illustrate the approach on the CIFAR10, CIFAR100, and ImageNet1k datasets, three standard benchmark image datasets, maintaining the original resolution of the images for each dataset. Performance is assessed in terms of scalability and final accuracy within a limited fixed computational time and computational resources. To measure accuracy, we use the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks as well as an improvement of image quality of the new images created by the GANs approach. Weak scaling is attained on both datasets using up to 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.
Here we integrate a high-pressure electroosmotic pump (EOP), a nanoflow gradient generator, and a capillary column into a miniaturized liquid chromatographic system that can be directly coupled with a mass spectrometer for proteomic analysis. We have recently developed a low-cost high-pressure EOP capable of generating pressure of tens of thousands psi, ideal for uses in miniaturized HPLC. The pump worked smoothly when it was used for isocratic elutions. When it was used for gradient elutions, generating reproducible gradient profiles was challenging; because the pump rate fluctuated when the pump was used to pump high-content organic solvents. This presents an issue for separating proteins/peptides since high-content organic solvents are often utilized. In this work, we solve this problem by incorporating our high-pressure EOP with a nano-flow gradient generator so that the EOP needs only to pump an aqueous solution. With this combination, we develop a capillary-based nano-HPLC system capable of performing nano-flow gradient elution; the pump rate is stable, and the gradient profiles are reproducible and can be conveniently tuned. Finally, to demonstrate its utility, we couple it with either a UV absorbance detector or a mass spectrometer for peptide separations.
The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.
Decision makers rely on models to make important regulatory, policy, and investment decisions. For power systems, these models must capture (i) the future challenges introduced by the integration of large quantities of variable renewable energy sources and (ii) the role that energy storage technologies should play. In this paper, we explore several different approaches to selecting representative days for generation expansion planning models, focusing on capturing these dynamics. Further, we propose a new methodology for adjusting the outputs of clustering algorithms that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peak demand is captured. This weighting methodology reduces the magnitude of the error in the representative day based generation expansion planning models estimation of costs by 61% on average. The results also demonstrate the importance of carefully performing the clustering of representative days for both system costs and technology mix. In most cases improvements to the total cost of different representative day based expansion plans are realised where conventional generation capacity is substituted for energy storage. Based on the energy storage technology selected we conclude this capacity is being used to address ramping challenges as opposed to shifting renewable generation from off to on peak periods, reinforcing the importance of capturing detailed intraday dynamics in the representative day selection process.