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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Evaluation of solar thermal power plants using economic and performance simulations

An energy cost analysis is presented for central receiver power plants with thermal storage and point focusing power plants with electrical storage. The present approach is based on optimizing the size of the plant to give the minimum energy cost (in mills/kWe hr) of an annual plant energy production. The optimization is done by considering the trade-off between the collector field size and the storage capacity for a given engine size. The energy cost is determined by the plant cost and performance. The performance is estimated by simulating the behavior of the plant under typical weather conditions. Plant capital and operational costs are estimated based on the size and performance of different components. This methodology is translated into computer programs for automatic and consistent evaluation.

El-Gabawali, N.↗
DOE OSTI2022

Evaluation of Heat Exchanger Technology for Integrated Energy System: Methodology and Preliminary Application

This report proposes a novel heat exchanger evaluation method, based on integrated QFD-AHP technique, to support strategic optimal selection of heat exchanger (HX) technology for integrated energy systems. The term “optimal selection” is defined as selecting a product (i.e., HX) that best meets various aspects of end user demand, including both technical and economic requirements. Compared to previous HX evaluation methods, the proposed approach allows decision makers to explicitly take into account the practical demand of customers (i.e., HX end users) which may vary depending on the HX application target. In addition, the proposed evaluation method can be used not only for high-level decisions to select HX type, but also for low-level decisions such as the selection of the key components including HX material or heat transfer fluid. The focus of this study is to describe the new HX evaluation method and to illustrate the overall HX evaluation and selection processes using the three nuclear heat application cases that represent the thermal integration between advanced nuclear reactors and industrial processes. As a preliminary study, three different types of HX, i.e., shell-and-tube heat exchanger, printed circuit heat exchanger, and heat pipe heat exchanger, were compared.

42 ENGINEERING↗
DOE OSTI2022

Determining spatially varying profit‐maximizing management practices for miscanthus and switchgrass production in the rainfed United States

Abstract Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit‐maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha −1 for switchgrass production at age 2. The mean profit‐maximizing N rate increases with age for miscanthus, peaking at 151 kg ha −1 at age 11 before declining to 114 kg ha −1 at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha −1 for middle‐aged stands and declines to 114 kg ha −1 at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Salt matters: How ionic strength and electrolytes impact redox polymer reactivity and dynamics for energy storage

As the global demand for sustainable energy grows, redox-active polymers (RAPs) have emerged as promising materials for batteries due to their advantages in stability, ease of preparation, and low-cost processability. Despite factors traditionally known to impact polymer dynamics (e.g., temperature, viscosity, and structure), we posit that investigating the effect of ionic strength and/or supporting electrolyte types on the electrochemical performance of RAP systems is crucial, both in aqueous and nonaqueous systems. Here, we first highlight recent findings on RAP-electrolyte interactions, elucidating how their polyelectrolyte nature determines their redox activity. Then, we focus on strategies to enhance RAP performance for energy storage through ionic strength optimization and tailored electrolyte composition. These insights into the modulation of RAP reactivity provide a foundation for improving battery performance in both flow and stationary configurations, thus facilitating progress toward next-generation energy storage solutions.

25 ENERGY STORAGE↗
DOE OSTI2020

Piecewise Linear Optimization for Public EV Charging Depots in Different Utility Environments

With the proliferation of electric vehicles (EVs), EV supply equipment technology is being pushed to increasingly higher power levels, such as extreme fast charging (XFC) at 300kW or more. As more public XFC depots are connected to electric distribution systems, utility operators will mitigate their impact with pricing schemes and programs such as demand response, demand charges, and time-varying prices. In this paper we describe an XFC management system that combines an on-site battery energy storage system and an optimization application that minimizes the cost to operate the depot in light of the utility pricing programs. We have designed the optimizer specially to use fast and simple linear programming so that it can be deployed to low-cost computing platforms. We also describe how several utility pricing schemes are integrated into a single piecewise model, reducing the engineering effort to implement the optimizer in different utility environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Hydroboost

HydroBoost is the most realistic revenue optimization tool for the hybridization of hydropower and battery energy storage systems to date. The innovative representation of how operators actually schedule hydropower in practice results in more realistic predictions of revenue and operations. Unlike other optimization tools, HydroBoost generates forecast energy prices with uncertainty to use in the optimization. This allows HydroBoost to give users a range of potential revenue with an upper bound using the perfect foresight pricing and a lower bound using a naive persistence forecast model. Additional forecast can be generated and used in the optimization, such as additive models, random forest, and neural networks to give further insight into potential revenue. HydroBoost has been designed to be applicable for both run-of-river and reservoir storage sites. The primary focus is on the day-ahead market and requires year-long data with an hour time-step. All time-series input and constraints are contained in an Excel worksheet for convince. The user will run the forecasting generation first with a Python script to give the optimization model the necessary requirements. Next the optimization is ran using Julia and results are generated and stored into a directory as csv files. HydroBoost includes an additional module to generate figures based on the results of the optimization simulation. The results help analyze the results and users to draw insights into how the hydro and battery systems are operated and the revenue each is producing. Additionally, the difference between the perfect foresight model and models that include forecast can easily be inspected.

Phillips, TylerB. [Idaho National Laboratory (INL)↗
DOE OSTI2024

Electrification of residential and commercial buildings integrated with hybrid renewable energy systems: A techno-economic analysis

Here, this study assesses the techno-economic viability of city-scale building electrification, comparing a base case scenario with conventional HVAC systems to an electrified scenario with heat pump systems. EnergyPlus simulations reveal a 28% increase in annual electricity consumption in the electrified scenario, shifting the peak consumption from summer to winter with a 49% surge. Hybrid renewable energy systems (HRESs) are then evaluated using HOMER Pro, considering renewable penetration from 0% to 100%. The net present cost (NPC) of the base case and electrified scenarios range from $\$10.34$ to $\$24.38$ billion and $\$8.79$ to $\$50.31$ billion, respectively. Sensitivity analysis explores the impact of carbon tax, fuel price, and renewable subsidies on HRESs in the electrified scenario. Results indicate that applying a carbon tax up to $\$120$/tonne of CO 2 increases the optimum renewable fraction from 5% to 26%, while higher fuel prices and renewable subsidies further enhance the feasibility by shifting the optimum renewable fraction to 60% and 54%, respectively, demonstrating the interplay between economic factors and renewable integration in electrified urban contexts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. Additionally, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluation of AddUp Precision L-PBF Technology for Tooling and Other Industrial Applications

ORNL (Contractor) and AddUp Inc. (Participant) collaborated to enable the adoption of laser powder bed fusion for tooling applications, critical for reshoring the manufacturing sector in the U.S. Most of the work on laser powder bed fusion has focused on high value materials such as Ti-6Al-4V and Inconel 718 for niche applications in aerospace, biomedical and energy sectors. In this collaboration, ORNL and AddUp worked laser powder bed fusion of maraging steels for tooling applications while leveraging the unique ability of the AddUp laser powder bed fusion system to deposit fine powders. We demonstrated that the AddUp technology is capable of depositing complex artifacts and features such as overhangs within desired tolerances. We also showed that direct aging of the deposited parts result in simultaneously higher strength and elongation compared to the conventional two step heat treatments, which could result in significant energy savings. Finally, we developed thermos-kinetic models to enable design of heat treatments for optimal material properties compared to the energy intensive empirical methods currently used. The report summarizes the detailed findings of this collaboration.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Quantum chemical studies of a model for peptide bond formation. 3. Role of magnesium cation in formation of amide and water from ammonia and glycine

The SN2 reaction between glycine and ammonia molecules with magnesium cation Mg2+ as a catalyst has been studied as a model reaction for Mg(2+)-catalyzed peptide bond formation using the ab initio Hartree-Fock molecular orbital method. As in previous studies of the uncatalyzed and amine-catalyzed reactions between glycine and ammonia, two reaction mechanisms have been examined, i.e., a two-step and a concerted reaction. The stationary points of each reaction including intermediate and transition states have been identified and free energies calculated for all geometry-optimized reaction species to determine the thermodynamics and kinetics of each reaction. Substantial decreases in free energies of activation were found for both reaction mechanisms in the Mg(2+)-catalyzed amide bond formation compared with those in the uncatalyzed and amine-catalyzed amide bond formation. The catalytic effect of the Mg2+ cation is to stabilize both the transition states and intermediate, and it is attributed to the neutralization of the developing negative charge on the electrophile and formation of a conformationally flexible nonplanar five-membered chelate ring structure.

Magnesium/chemistry↗
DOE OSTI2020

An Integrated Platform for Wind Plant Operations: From Atmosphere to Electrons to the Grid

Under the Atmosphere to Electrons to Grid project (A2e2g), the National Renewable Energy Laboratory (NREL) is developing an optimization platform for wind plant operations. The platform merges forecasting tools with aerodynamic and economic models in order to identify optimal operating schedules and controller functions that will maximize a wind plant's value streams for energy and other grid services. In this presentation, we will describe functionalities of the platform. We will also discuss the implications the use of the platform might have for understanding and valuing the capabilities of wind resources for power system operations.

A2E2G↗
DOE OSTI2021

Rubik’s Cube Topology Based Particle Swarm Algorithm for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g. renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as, reducing power loss, stabilizing the main power grid, etc. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubiks Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubiks Cube topology based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology based PSO and its scalability on larger scale applications.

Feng, Xiaochun↗
DOE OSTI2023

Justice 40 Tool (J40 Tool) v1.0

The Justice 40 tool provides a quantitative framework to support decision-making around equitable energy interventions at the community level. The tool calculates the optimal portfolio of policy interventions that explicitly mitigates energy insecurity of an eligible population, by reducing its disproportionate energy burden. The place-based analysis assumes a spatial census tract-level resolution and distinguishes different sociodemographic groups within each tract. Instead of focusing on a specific technology, the underlying J40 model captures the combined effect of a set of policy interventions, currently including weatherization, rooftop solar, community solar and community wind. For each tract, the model chooses the optimal (least cost) combination of interventions to address the disproportionate burden, considering the specific population demographics and techno-economic potentials of technologies. Mathematically, this problem is formalized as an optimization model and formulated as a linear program.

Heleno, Miguel↗
DOE OSTI2021

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS-based on real-time optimal power flow-controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimal Efficiency and Operational Cost Savings: A Framework for Automated Rooftop PV Placement

Residential energy consumers are charged based on a utility rate structure, such as net metering or feed-in tariff. To lower consumers' electricity bills, expensive batteries are deployed to reduce the electricity fed from the grid during peak hours. However, strategic photovoltaic (PV) panel placement enables the reduction of operational energy cost while considering the spatial feasibility and efficiency for hosting rooftop PV. In this paper, we present a framework to automatically identify the optimal location of rooftop PV panels on residential buildings. Our framework integrates multiple workflows, including energy and environmental simulation, parametric modeling, and optimization to identify the ideal location of PV panels to balance the demand and supply at a building and community scale. These workflows are linked using the Grasshopper plug-in for Rhinoceros CAD software. The framework includes two different workflows, each satisfying a target for optimal PV placement: (a) maximizing PV panel efficiency, where users aim to maximize energy generation, and (b) minimizing operational energy cost, where "best'' panels are selected considering utility rates for operational energy cost. Our framework is demonstrated in a residential community in Fort Collins, Colorado, to generate the optimal PV placement for each of the two aforementioned targets. Results from the two workflows are compared to illustrate the effect of PV location and orientation on solar energy production efficiency and operational energy cost. The developed workflows are introduced as tools within the Grasshopper plug-in to investigate the solar potential of rooftop PV panels while taking into account factors such as contextual shading, utility rate structures, and buildings' energy demand profiles.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Optimizing spectral phase transfer in four-wave mixing with gas-filled capillaries

Four-wave mixing (FWM) in gas-filled hollow-core capillaries, a nonlinear optical process that mixes signal and pump photon frequencies to generate idler frequency photons, offers a method for precise spectral phase transfer from signal to idler at ultrashort timescales and extreme powers. However, this regime is challenged by competing linear and nonlinear dynamics, leading to significant trade-offs between spectral phase transfer and conversion efficiency. Our computational investigation focuses on the upconversion of femtosecond pulses from the infrared (IR) to the ultraviolet (UV), a range notoriously difficult to manipulate. We explore an intermediate energy regime that strikes an optimal balance between FWM-mediated phase-transfer fidelity and nonlinear conversion efficiency. By adjusting the energy ratios and spectral phase profiles of the input signal, we achieve conversion efficiencies of approximately 5-15% while maintaining an effective quasi-linear spectral phase transfer. These findings will contribute to establishing first-principles and scaling laws essential for applications such as high-precision imaging, spectroscopy, quantum transduction, and distributed entangled interconnects, facilitating advanced control of ultrafast photonic and electronic wavepackets in quantum materials with unprecedented spatial and temporal precision.

Zhang, Hao↗
DOE OSTI2022

Optimizing molecular light absorption in the strong coupling regime for solar energy harvesting

The strong coupling of optical absorbers (e.g., molecules or semiconductors) to confined photonic modes fundamentally alters the physical properties of the coupled system via the formation of hybrid light-matter states. One potential application of strong light-matter coupling relies on exploiting it to localize light-induced charge excitation processes to small volumes of material. Applications that would benefit from this localization include thin-film photovoltaics, photodetection, photocatalysis, and others, where the overall performance depends on the ability of a material to amplify light absorption (i.e., the formation of electron-hole pairs) within specific locations in space. This contribution investigates how strong light-matter coupling affects light absorption rates in molecular absorbers coupled to photonic nanostructures. Here, our results show that the molecular light absorption efficiencies are highest in configurations where the strongly coupled molecules interact directly with the incoming photon flux. We also identify a nonlinear dependence in the molecular absorption as a function of concentration, unique to the strongly coupled systems. Based on these results, we propose design principles for engineering nanostructured systems that allow for high efficiencies of charge carrier localization into strongly coupled absorbers.

14 SOLAR ENERGY↗