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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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This paper presents a methodology for the reliability evaluation of Space Station power system. The two options considered are the photovoltaic system and the solar dynamic system. Reliability models for both of these options are described along with the methodology for calculating the reliability indices.
Several cost estimation and financial cash flow models have been applied to a series of geothermal case studies. In order to draw conclusions about relative performance and applicability of these models to geothermal projects, the consistency of results was assessed. The model outputs of principal interest in this study were net present value, internal rate of return, or levelized breakeven price. The models used were VENVAL, a venture analysis model; the Geothermal Probabilistic Cost Model (GPC Model); the Alternative Power Systems Economic Analysis Model (APSEAM); the Geothermal Loan Guarantee Cash Flow Model (GCFM); and the GEOCOST and GEOCITY geothermal models. The case studies to which the models were applied include a geothermal reservoir at Heber, CA; a geothermal eletric power plant to be located at the Heber site; an alcohol fuels production facility to be built at Raft River, ID; and a direct-use, district heating system in Susanville, CA.
The approach used is the Multi-Agent System (MAS) paradigms, where systems components are represented as agents, interacting both with each other, and with the environment in which they evolved. Agents behaviors correspond to components in the real Integrated Water-Power System. The model simulate actions and interactions of these (autonomous) agents to analyze their effects on the overall system. Agents in the water system capture components of water collection, treatment, transportation, distribution and use (e.g. pipe, canal, pump, water demands for agriculture, etc.). Agents in the power system capture components of power generation, transportation, distribution and use (electricity demands, sources, etc.).
The Electricity Markets Investment Suite - Competitive Equilibrium (EMIS-CE) model is an optimization-based power system investment model developed at NREL. It attempts to approximate a market-driven investment equilibrium outcome under the assumption of idealized or perfect competition (e.g. no participants are able to exert market power or leverage exclusive cost advantages) and uncertain future conditions.
This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM ) capacity expansion model using two resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.
Electrified aircraft propulsion concepts show potential in using propulsion airframe inte-gration in order to increase efficiency in flight and therefore decrease fuel burn and emissions.Electrification offers component efficiency values greater than 90 percent, but the loss is in the form of low grade waste heat. A major challenge of electrified aircraft propulsion is managing that heat while minimizing any penalties associated with a thermal management system. This paper explores the effect of two innovations in the management of waste heat at the aircraft system level for a turboelectric single aisle concept. The first innovation is achieving a 3 times reduction in heat by developing high-efficiency components rather than managing the high levels of heat. The second takes advantage of the outer mold line of the aircraft to reject heat directly to the environment passively instead of adding active cooling loops that negatively impact the weight, power, and drag of the aircraft. In order to fully grasp the impact of the advanced power system, we develop methods of modeling the power and thermal management systems to be integrated in the full aircraft conceptual model. We then model the aircraft with a state of the art DC transmission system and active cooling loops as a baseline for our study. Our second model includes the advanced power system with active cooling, which results in a fuel burn reduction of 2.5 percent. Finally, in our third model we assess the benefit of an outer mold line cooling scheme with the advanced power system. The outer mold line cooling scheme with an advanced power system yields an additional 0.8 percent reduction in fuel burn,for an overall fuel burn reduction potential of 3.3 percent in addition to aerodynamic benefits of electrified aircraft propulsion.
This study explores the air quality and health impacts of power system dispatch decisions by integrating a detailed atmospheric dispersion model and a health impact model with power system simulations.
NASA’s Artemis exploration campaign includes multiple elements that utilize photovoltaic power generation. Artemis mission profiles feature a variety of events that can limit solar array generation and stress electrical power system (EPS) performance, including eclipses, propulsive or navigational maneuvers which constrain the positioning of the solar arrays, and spiral trajectories with significant radiation degradation. These features make predicting the performance of the power system particularly important and requires accurate modeling of power generation by the solar arrays under a variety of conditions. One of the tools used to predict spacecraft EPS performance is the System Power Analysis for Capability Evaluation (SPACE) model developed at NASA Glenn Research Center. SPACE is used by NASA to model EPS performance for the Orion crew transport vehicle and the Power and Propulsion Element (PPE) of the Gateway space station. This presentation will provide an overview of how SPACE calculates solar array performance, including degradation factors considered and environmental conditions that drive the solar array designs. Additionally, Orion array performance predictions generated by SPACE will be compared to flight data collected during the Artemis I mission.
Georgia Institute of Technology (GIT), University of Texas at El Paso (UTEP), and the National Energy Technology Laboratory (NETL) will collaborate and develop cyber-physical systems for advanced power systems. GIT will test virtual models on hybrid power system hardware and cyber physical components. UTEP will test novel actuators on hybrid power system hardware. NETL will access GIT’s expertise in the engineering and real time simulation of power system processes for cyber-physical systems and simulations to control hardware. NETL will establish a relationship to promote UTEP’s capabilities in additive manufacturing and high-temperature piezo-electric devices with the potential for controlling high-temperature flows with a light and inexpensive device at high response rates. This project is the result of an EERE Advanced Manufacturing Office open Multi-Topic awarded proposal led by GIT titled, “Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration”.
Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.
A potassium-Rankine power conversion system model was developed under Contract No. NAS3-25808 for the NASA-LeRC. This model predicts potassium-Rankine performance for turbine inlet temperatures (TIT) from 1200 - 1600 K, TIT to condenser temperature ratios from 1.25-1.6, power levels from 100 to 10,000 kWe, and lifetimes from 2-10 years. The model is for a Rankine cycle with reheat for turbine stage moisture control. The model assumes heat is supplied from a lithium heat transport loop. The model does not include a heat source or a condenser/heat rejection system model. These must be supplied by the user.
Test systems play an important role in power systems analysis and are used extensively for a wide range of purposes like reliability analysis, production cost modeling, studying the impacts of load shifting, and many more. These test systems have continued to evolve over the years to keep pace with changing system compositions due to technology and policy changes. In this work, we apply the Regional Energy Deployment System (ReEDS) model, a large-scale capacity expansion model (CEM), on the nodal RTS-GMLC system to perform capacity expansion of the bus-level test system. This work both demonstrates that a large-scale CEM can successfully be applied to nodal systems, and that the CEM can allow a test system to be evolved to meet desired criteria. The process allowed the coupling of CEM data not available in the test system (such as capital costs) with test system data to provide plausible system evolutions in line with scenario specifications.
For the U.S. Department of Energy’s (DOE) 2016 Small Business Voucher for Marine and Hydrokinetic (MHK) System, Second Round 2016, ORPC intends to work with the National Renewable Energy Laboratory (NREL) to perform dynamometer testing of the MHK generator systems and its associated controls and inverters. ORPC will provide the generator, variable frequency drives (VFD), controls, and inverter for this testing. NREL will utilize the NREL Energy Systems Integration Facility (ESIF) and dynamometer facilities at the National Wind Technology Center (NWTC) for this work. Modification 6: Additionally, NREL will conduct a feasibility study for implementing passive DC rectification at the turbine.
A small radioisotope powered TPV system was assumed. The system was simplified into a one-dimensional model with the emitter and heat rejection system as boundaries. Design equations were then developed which would allow parametric modeling of system efficiency, emitter radiation incident upon the TPV cell, energy converted in the TPV cell, and energy absorbed in the filter and cell-to-cell gaps. A figure of merit was then developed for the emitter, filter, and TPV cell.
We present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages respect to the popular Vector Fitting (VF) algorithm using examples of approximations of admittance matrices discussed in a very recent paper and by the creators of VF. We show that not only MIAAA outperforms VF in terms of achieving better accuracy using a smaller number of poles, but also has no numerical issues to achieve convergence. In contrast to VF, MIAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles.
The South Asia Group for Energy (SAGE) is working with Grid India and the Central Electricity Authority (CEA) to determine the best methods to deploy more inverter-based resources (IBR) such as solar photovoltaic generators and utility-scale battery. To address grid strength and stability concerns, planners and operators are working with the National Renewable Energy Laboratory's (NREL) team, through SAGE, to identify potential solutions for strengthening the grid and fostering grid reliability.
As part of continuing efforts to develop support, understanding, and infrastructure of data center integration onto the electric grid, our work aims to model and analyze the behavior of data center loads in a microgrid power system. Our model consists of renewable energy sources, batteries, and a nuclear reactor-steam Rankine cycle to power data center loads. The simulation studies investigate the electrical behavior of the microgrid system to assess its ability in supporting large data center electrical demands.
Duke Energy has partnered with the National Renewable Energy Laboratory (NREL) to evaluate pathways to achieving their carbon-free targets and to assess the operational impacts of the resulting system. This report details findings from Phase II of the Duke Low Carbon Resource Integration study, which consisted of three separate but interrelated analyses: (1) a resource assessment exploring the technical and economic potential and characteristics of wind and solar resources in the Carolinas; (2) capacity expansion modeling identifying the least-cost investment pathways for achieving 70% CO 2 emissions reductions in North Carolina by 2030 and a net-zero electricity system by 2050; and (3) detailed production cost modeling of power system operations at the higher shares of low- and zero-carbon emitting generation sources, informed by the capacity expansion modeling portion of the analysis. The analysis finds that Duke Energy can approach the 2030 and 2050 emissions target in North Carolina through investment in a combination of solar, wind, and storage along with maintaining its existing nuclear fleet. The average cost of CO 2 abatement in the Carolinas through 2021-2050 is on the order of $\$27-33$ per metric ton (range of $\$9-34$ per metric ton across key sensitivities).Duke Energy can expected increased interchange with neighbors to help balance higher levels of solar, although the ability to do this will depend on whether neighboring regions also move to integrate more carbon-free resources. As Duke Energy moves toward both the 2030 and 2050 targets, addressing energy needs during the winter peak period becomes particularly important, and the system relies on the availability of resources such as renewable or hydrogen combustion turbines, seasonal storage, or other similar technologies that are dispatchable but able to operate at low capacity factors.