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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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NEUP Final Technical Report outlines experimental and modeling efforts to characterize performance of thermal energy storage systems with molten salt as the phase change material.
This is the modeling data (input/output files of TOUGHREACT 4.10) used to simulate the reactive transport processes of the Aquifer Thermal Energy Storage (ATES) operations at Stockton University, NJ. Readme.txt lists all the files. TOUGHREACT 4.10 requires to reproduce the modeling output. The modeling data in this submission is related to the Aquifer Injection for Energy Storage purposes outlined in "Reactive Transport Modeling of Aquifer Thermal Energy Storage System at Stockton, NJ During Seasonal Operations".
Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.
This fact sheet provides an overview of district energy systems, which are characterized by one or more central plants producing hot water, steam, and/or chilled water, which then flows through a network of insulated pipes to provide hot water, space heating, and/or air conditioning for nearby buildings. District energy systems serve a variety of end-use markets, including downtowns (central business districts), college and university campuses, hospitals and healthcare facilities, airports, military bases, and industrial complexes. By combining loads for multiple buildings, district energy systems create economies of scale that help reduce energy costs and enable the use of high-efficiency technologies such as combined heat and power (CHP).
The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.
The work aims to achieve optimal tidal energy conversion through a comprehensive approach of modeling, optimization, and control hardware-in-the-loop (CHIL) validation. By developing accurate models and employing optimization techniques, it seeks to identify efficient system configurations and control strategies. HIL validation will ensure the performance and reliability of the optimized tidal energy conversion system. The preparation of the present manual has been supported by the U.S. Department of Energy.
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PV systems, like all energy systems, must chart a difficult course in order to improve resilience. They need to simultaneously withstand extreme conditions that cause grid power disruptions, be configured in a way that allows electricity to be delivered to meet the PV user's needs, and overcome disruptions arising from solar resource intermittency. Certain considerations reveal themselves as key to a PV system's resilience benefits. Choice of hardware is critical, as is site selection for the PV array. Voluminous evidence in a hurricane context demonstrates that durable hardware, properly maintained and sited, leads to PV systems surviving extreme weather with their functionality intact. Structurally weak or poorly maintained PV equipment, on the other hand, has demonstrated failures during extreme weather events. PV system stakeholders should ensure that best practices are followed for hardware procurement and siting. It is also important to understand that PV systems are not one-size-fits-all. While incorporating islanding capabilities and energy storage into the system's electrical topology will typically enhance resilience, the user's needs will determine the optimally resilient conformation of any given system. These factors - appropriate hardware, siting, maintenance, and system design - are the primary drivers of PV resilience. PV systems possess high potential for resilient energy generation due to their distributed, abundant, and free fuel supply, but the technology itself does not inherently lead to resilient outcomes. Realizing PV's potential for resilience requires consideration of these determinative factors.
This report documents the work performed by Pacific Northwest National Laboratory (PNNL) and Washington State University (WSU) supporting an effort to develop control strategies for a 1 MW/3.2 MWh energy storage system (ESS) at Avista Utilities (Avista). The ESS project is funded though the Washington Clean Energy Fund (CEF) program administered by the Washington State Department of Commerce (DOC). A tri-party Cooperative Research and Development Agreement (CRADA), namely the CRADA 360, was created in 2015 to facilitate this work.
Phase change materials (PCMs), especially salt hydrates possess high volumetric energy storage capacity in their transition temperature range. These materials are used in applications where it is necessary to store thermal energy due to temporary load shift between demand and availability. Thus, possible applications are HVAC, recovery of waste heat, and defense thermal management. Despite salt hydrates potential, the practical feasibility of latent heat storage with salt hydrates is limited due to low power rating, supercooling, phase segregation, and long-term stability. Its low power rating and long-term stability limits its application in most applications. This work experimentally validates the stability and thermal performance of a compact heat exchanger charged with salt hydrate during melting and freezing. The compact heat exchanger was designed with fins on both the heat transfer fluid (HTF) and salt hydrate PCM side. The thermal performance of the latent heat thermal energy storage system (LHTESS) was evaluated for various operating conditions. The results show that LHTESS could achieve an average heat transfer coefficient of 124 and 87 W/(m2 K) during melting and solidification, respectively. The stability of the system in suppressing supercooling was validated over 800 cycles with nucleating agent and active homogenous nucleation techniques. The supercooling was reduced to 3 °C with zinc hydroxyl nitrate as nucleating agent and less than 1 °C with the active homogenous nucleation technique. The LHTESS showed less than 6% degradation in energy storage capacity over 800 cycles.
Safety and reliability are primary concerns for the deployment of lithium-ion batteries, especially in electric vehicles (EV) and larger-scale energy storage systems (ESS). Current technology in battery management systems (BMS) includes cell voltage monitoring and positioning temperature sensors in selected locations. For a system with hundreds to thousands of individual batteries, single-point temperature monitoring is inadequate to detect hot spots and cell overheating, which could lead to thermal runaway. Here, we have developed a temperature-sensitive copper-thiol compound that can be directly coated onto battery pouch foils to enable early detection of thermal runaway. Upon reaching specific temperatures, this compound releases a sulfur-containing detectable gas, which can be identified using chemically specific gas sensors to trigger an early warning signal. Such a signal propagate through air offers broad signal coverage and enables a more comprehensive approach to large-area temperature monitoring. The Cu-ethanethiol coating is designed to release volatile gases when the substrate surface temperature exceeds 70 °C, with continuous outgassing as the temperature increases. The compound is composed of Cu, S, Cl, hydrocarbons and trace amounts of oxygen. Upon heating, the oxidation state of Cu(I) transitions to Cu (II), accompanied by gas release. Thermogravimetric analysis coupled with mass spectrometry correlated well with the onset of gas release temperature and emission of sulfur-containing volatile gases. Additionally, an acrylic overcoat is applied to enhance the adhesion of the thermally sensitive compound film to the battery pouch foil. This coating is expected to offer an additional safety layer for ESS, alerting possible thermal runaway events before a failure occurs, thereby allowing sufficient time to implement a mitigation plan.
This presentation provides a comprehensive overview of end-of-performance and decommissioning considerations for large-scale Battery Energy Storage Systems (BESS). It outlines expected system lifespans, midterm assessment needs, and pathways for extending operational life through augmentation or repowering. The presentation details regulatory requirements that govern decommissioning plans, cost estimates, financial assurance mechanisms, and performance obligations across multiple jurisdictions. It further examines end-of-life equipment management, including recycling, waste handling, transportation, and environmental compliance. Designed to support Malawi's electricity-sector institutions, the presentation highlights how planning for decommissioning and environmental stewardship can be integrated early in project development to ensure safe, financially accountable, and environmentally responsible BESS system retirement.
The purpose of this study is to experimentally investigate the thermal performance of an innovative thermal energy storage (TES) system that combines the advantages of the phase-change material (PCM)/graphite foam latent heat TES medium developed at Argonne National Laboratory (Argonne) and the internally supported plate-fin (ISPE) cell architecture heat transfer fluid (HTF) flow channels developed at Brayton Energy (Brayton). Several essential tasks were accomplished: (1) Thermal property characterization. Thermal properties of the graphite foam were characterized, providing necessary data for experimental result analysis and numerical simulation. (2) Design and optimization of lab-scale test module. Based on Brayton’s full-scale heat exchanger (HX)-TES system, the experimental test module was designed, optimized, and fabricated. (3) Thermal performance testing and data analysis. Five cycle tests were successfully conducted—including one with approximately 3.5 psig of pressure applied to the diaphragms—to investigate the thermal performance of the experimental test module for charging and discharging. Temperature profiles were generated for each charging test and discharging test as a function of time. The temperature profiles clearly show three TES stages: sensible heat (temperature increase), latent heat (melting), and sensible heat (temperature increase) for the charging process. Similarly, the temperature profiles clearly show three thermal energy release stages: sensible heat (temperature decrease), latent heat (solidification), and sensible heat (temperature decrease). Melting and solidification of the PCM generally occurred in relatively narrow temperature ranges, indicated by the flattened temperature regions in the temperature profiles. These phase changes ranged approximately 3°C for melting and 3.5°C for solidification. The charging and discharging temperature profiles were similar for similar experimental parameter tests whether or not pressure was applied to the diaphragm to eliminate the gap between the HX surface and the TES subsystem. This indicates that the effect of a small gap between the HX surface and the TES subsystem is insignificant for charging and discharging. (4) Comparison of experimental data and simulation results. We compared the experimental data to the numerical simulation results. Numerical simulations were conducted by using the ANSYS FLUENT 2019 R3 commercial computational fluid dynamics software. The predicted phase-change times agreed reasonably well with those from the experimental data. In most cases, the estimated time differences between the relative phase changes were within 16%. The predicted start and end times for the charging process agreed well with those from the experimental data. However, the simulation results showed earlier start and end times than the experimental data for the discharging process. Overall, the experimental data and its comparison with the simulation predictions verified the technical viability of the integrated ISPF HX-PCM/graphite foam latent-heat TES system.
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.
Thermal energy storage (TES) is a promising solution to store and dispatch energy and shave peak electric load, reducing the operational cost of HVAC systems. We present results of a TES system using phase-change materials (PCM) integrated with an air conditioner. The proposed system uses an organic PCM (tetradecane) encapsulated within compressed expanded natural graphite foams referred to as phase-change composite. The graphite foam encapsulates the PCM eliminating the need for expensive storage vessels, reduces installation costs, and provides higher thermal conductivity that can lead to faster charge/discharge rates. Two serpentines, multi-pass circuits, operating as a heat source and a sink, exchange heat to and from the phase-change composite. These two circuits are embedded in multiple slabs of this material. The "charge" circuit contains the refrigerant that is directly coupled to a vapor compression system, and the "discharge" circuit removes heat from an airstream and releases it into the PCM composite through a water-glycol liquid coupling. This configuration allows for multiple modes of operation depending on the state of charge of the thermal energy storage module, the building air-conditioning load, and the current electricity and demand charges. This flexible operation allows variable air volume capacity control without the need to have a variable capacity refrigeration system. We developed a 21 kW-hr (6 RT-hr) prototype TES system coupled with a commercial air-conditioner to characterize the component-and system-level performance.
<span style="font-size: 12pt;">Presentation for the 2020 International Pittsburgh Coal Conference held virtually from September 8-11, 2020.</span><span style="font-size: 12pt;">Abstract: As electricity from various renewable energy sources is increasingly supplied to the power grid, the grid’s stability will be challenged. Proposed grid-stability solutions will need to address variability of sources, such as solar and wind energy, in order to alleviate problems such as overgeneration and ramping. In regions where increases in solar energy are prominent, such as California, the net power demand curve often referred to as “the Duck Curve,” has caused problems for energy dispatchers, due to over-generation and fast ramping demands. In this project, we investigated a conceptual design for a Thermal Energy Storage System (TESS) integrated into a coal-fired power plant to support grid-scale storage. Implementing a TESS into power plants can allow for an increase in renewable sources, increase grid resilience, and allow conventional coal-fired plants to operate more efficiently. For this project, a TESS using granular solids as the energy-storage media is integrated into a conventional coal-fired power plant. The purpose of the TESS is to store energy from the flue gas during low demand periods and transfer the energy to steam during peak demand periods. The storage allows the stabilization of combustion and related boiler components at low loads and provides the opportunity to avoid some shutdowns. This study uses thermodynamic properties of several candidate thermal storage materials to estimate the size of storage system required for several operating scenarios. </span>
To decarbonize their industrial facilities, The Dow Chemical Company has collaborated with Idaho National Laboratory (INL) to study the integration of nuclear power with an industrial chemical facility. Using Holistic Energy and Resource Optimization Network developed at INL for optimizing and analyzing integrated energy systems, a nuclear microreactor system was sized and evaluated for dynamic dispatch to Dow Silicones Corporation’s Carrollton, KY (USA) site for iloxane production. It was found that a 180 MW th system (12 × 15MW th ) with 75.1 MWh th of thermal energy storage could provide heat and power to the chemical facilities. In the process, this would reduce electricity imports by 99.9 % and reduce the Scope 1 and 2 emissions of the site by 292,100 tonnes CO 2 /yr (98.8 %). The primary novelty of this work is a first of a kind design and optimization of a microreactor powered integrated energy system to provide heat and power to a chemical plant using real plant data. This analysis will pave the way for future studies using dispatchable clean energy sources to reduce carbon emissions and commodity industries’ reliance on fossil fuels.