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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of high concentration photovoltaics (HCPV) power plants in the US Southwest: Economic assessment and sensitivity analysis

High concentration photovoltaic (HCPV) technologies offer several advantages over typical PV systems. In this study, a detailed economic assessment and sensitivity analysis of deploying HCPV plants in the US southwest is performed. To do this, a 20 MWdc HCPV power plant is designed using the System Advisor Model (SAM) and is evaluated from both technical and financial aspects. The sensitivity analysis shows that reducing the installed cost per capacity of the HCPV system can significantly improve the economics and reduce the levelized cost of electricity (LCOE) and levelized power purchase agreement (LPPA). Also, providing proper incentives including both investment tax credits (ITC) and production tax credit (PTC), loan debt ratio, and interest rate are among the most influential parameters. The analysis shows that with the 30% ITC, the LCOE, and LPPA of the designed HCPV projects are between $65.4/MWh and $69.3/MWh and between $71.0/MWh and $75.2/MWh, respectively. The estimated LCOE and LPPA values are almost 50% higher than the utility-scale PV projects. If proper funding is provided to continue the support of the HCPV system, they would become more economical and competitive with PV plants. Federal and state government policies must also be established to advocate HCPV systems.

14 SOLAR ENERGY↗
DOE OSTI2020

Allocation and Operation of A Hydropneumatic Energy Storage with Building Microgrid

Behind-meter application for a recently invented hydropneumatic Ground-Level Integrated Diverse Energy Storage (GLIDES) is focused in this research. Considering possible uncertainties from solar radiation and electricity load in a building microgrid, two-stage stochastic programming is adopted for 15-min operation and a mixed-integer nonlinear stochastic model is built with integrated energy systems of GLIDES, solar panel and power generating unit. In order to make the model computationally tractable, we use sample average approximate method to decrease associated complexity with sample size and improve solving efficiency. The experimental results in deterministic and stochastic operation indicate that best candidate solution from SAA are superior to the solution from expected value problem in terms of optimality gap and variance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
DOE OSTI2020

High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids: Preprint

Increased deployment of Distributed Energy Resources (DERs) and microgrids are significantly changing the operation of distribution grid. Increasing penetration of power electronics based DERs also raise several operational problems such as protection coordination issues, voltage raise, recloser synchronization, stability, and frequency control. Legacy systems are designed for unidirectional power flow. Bidirectional power flows, and varying short circuit levels are some of the major challenges in implementing these legacy protection schemes for future power grid with high penetration of power electronics based DERs. Inverter based DER’s can contribute to a maximum of 2 p.u. fault current. Interconnection standards might require even lower fault current contribution. If these protection related challenges are not addressed, it will result in limiting the penetration of DERs in distribution feeders. In this paper, the feasibility of high frequency signature generated by transients is evaluated to identify and locate faults in a distribution system. The paper will discuss the need for an accurate line modeling approach to accurately capture the high frequency signatures. These high frequency signatures are naturally occurring electromagnetic transients for events such as switching events in the network, faults events, etc. This is different than that implemented in transmission systems as the proposed approach utilizes advanced signal processing techniques to retrieve valuable information from the high frequency signatures generated during faults and does not just rely on time delay measurements between reflections of the high frequency signals. In the full paper, information of a modified IEEE distribution feeder test system to accurately model the high frequency signatures will be provided. The distribution lines will be modeled using universal line modeling (ULM) technique in an electromagnetic transients program (EMTP) modeling platform. Different test cases will be simulated and these test cases will include different fault types, multiple fault locations, capacitor bank switching, etc. The feeder system modeled for these simulations will also include power electronics based DERs. Additionally, experimental results demonstrating the high frequency signatures generated due to a transient event in a medium voltage (13.8 kV) distribution line will also be presented in the full paper. Furthermore, the analysis combined with simulation and experimental results in the full paper will help demonstrate the feasibility and efficacy of the proposed protection scheme. The developed protection scheme will not just help solve problems generated by bidirectional current flow, varying short-circuit current capacity, but also help in proliferation of DERs in distribution systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2024

Evaluating Interconnection Queue Impacts Using Hosting Capacity Analysis

The interconnection queue has been identified as a bottleneck in the efforts to shift the nations generation resources towards renewable sources and meet various state and federal goals. Efforts such as the interconnection innovation e-Xchange (i2X) are therefore trying to come up with ways in which the queue could be altered to make interconnection faster, cheaper, and fairer. This paper proposes using hosting capacity analysis methods to simulate the evolution of a power system as new resources are added. Modeling the interconnection process in this way enables simulation based study of various policy decisions for queue management and cost allocation. Sample results are presented to illustrate how some queue modifications might play out both in distribution and transmission systems.

Distributed Energy Resources, Interconnection↗
DOE OSTI2022

Parametric Modeling and Economic Analysis of a 2MW th 3-Stream sCO 2 Heat Exchanger

Here, this paper presents the design and cost optimization of a novel 2MW th 3-stream sCO2 plate-fin heat exchanger. This heat exchanger design is unique in that it uses reduced metal oxide particle-to-sCO2 heat exchanger for cost-effective energy storage applications. The design uses low velocity, laminar air as the re-oxidizing reactant to transfer the heat of the re-oxidizing reaction to a sCO2 power loop. The design of the heat exchanger is based on a 2-D, 3-fluid plate/fin heat transfer model. The model parameterizes the size, shape, and number of passages of the heat exchanger to calculate the temperature profile, pressure drop, and fluid velocities of all three fluids. Global heat exchanger parameters such as the effectiveness and total heat transferred to the sCO2 are then calculated for overall performance. Due to the value and increased use of sCO2 heat exchangers in power cycles, a cost model of the system based on the unique high temperature/high pressure operating conditions was created using quotes from reference projects and market analysis. These quoted air-to-sCO2 heat exchangers are then processed using multiple weighting factors pertinent to heat exchanger design, including heat exchanger type, maximum temperature, differential pressures, fluids, duty, and more. These factors are then used in an exponential function in order to generate a parameterized cost curve. The design and cost of the heat exchanger are then optimized using the SMPSO genetic algorithm in Python. The optimization objectives for the system are to maximize the overall system effectiveness, including an air recuperator for preheating, and to minimize unit costs. Additional constraints are added to the system for the sCO2 and air pressure drops, air velocity to reduce particle entrainment, and the length and volume of the heat exchanger.

Cost Model↗
DOE OSTI2024

lanl-ansi/MG-RAVENS

The MG-RAVENS project with the DOE Office of Electricity Microgrid R&D Program is a project to develop a completely free, open-source data exchange standard (API) for the Department of Energy, targeted at software tools related to infrastructure modeling, particularly the modeling of microgrids and electric power distribution systems that are created with funding from the Microgrid R&D Program. This software produces formal definitions of an API, documentation, contains supporting functions for parsing, validating, etc., and will contain examples of workflows enabled by the developed API.

Fobes, David M↗
DOE OSTI2021

Long-duration Energy Storage Technology Analysis and Development

This project develops a new low-cost and flexible long-duration energy storage (LDES) technology based on simultaneous regeneration technology. In lieu of the technology development, the project team also conducts a complete cost analysis for LDES based on various currently available technologies, as well as grid economic and environmental benefits analysis for LDES. There are three tasks in this project: Task 1. Fe-H2 flow cell fabrication, testing, demonstration; Task 2. Dynamic modeling, simulation and control architecture of power system; Task 3. Cost and grid analysis.

25 ENERGY STORAGE↗
DOE OSTI2025

Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)

This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).

17 WIND ENERGY↗
DOE OSTI2026

Long-Term Impacts of Constrained Transmission Deployment on the Cost-Reliability Tradeoff

Traditional Resource Adequacy (RA) frameworks in the U.S. undervalue the contributions of inter-regional transmission to resource adequacy during stress periods, focusing on the availability of nameplate capacity instead. However, availability of nameplate capacity does not always translate into electricity delivery, especially during tail events. Moreover, the rapid deployment of energy-limited resources and increasing electricity demand challenge existing resource adequacy frameworks and couple regional electricity demand and availability of supply via transmission. We propose a two-stage framework that goes beyond the existing capacity-centered approaches to reveal the RA contributions of transmission. In the first stage we introduce a multi-objective optimization framework to quantify the merits of transmission expansion via Pareto Frontiers under alternative futures of no transmission investment, primary energy resources availability and demand growth. The second stage focuses on tail events and leverages the results of the first stage to characterize the risk profile of regional consumers across the U.S. under the alternative energy futures. We find that no new transmission can lead to a more expensive and less reliable national grid across scenarios, however, the impact on regional RA can vary. The probabilistic analysis reveals that transmission investments can alleviate the tail risk of consumers, however, the availability of fuel resources does not always alleviate regional tail risks. Our findings inform policymakers and utilities on the prioritization of transmission investments to mitigate the risk of widespread outages, also for tail events, and ensure reliable and affordable electricity delivery to all.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

From Ensemble Climate to Ensemble Impacts

Many climate-risk tools rely on ensemble mean projections or endpoint climate snapshots to characterize future hazards. Although convenient for communication, these representations remove the statistical, temporal, and physical information that real infrastructure systems respond to. Infrastructure degradation and failure arise from extremes, sequences, cumulative stress, compound hazards, and nonlinear fragility relationships, none of which survive ensemble averaging or temporal compression. Power-system failure statistics and cascading failure models further show that infrastructure risk is dominated by tail events and path-dependent dynamics rather than by mean conditions. This paper demonstrates why ensemble mean or endpoint-only climate representations are mathematically and physically inconsistent with engineering-grade risk analysis. We outline a model-resolved, time-series-based workflow that preserves extremes, variability, and sequencing by propagating each climate-model realization independently through hazard formation, exposure, fragility, and cascading failure mechanisms. Taking the ensemble of impacts—rather than the ensemble of climate—provides a defensible, physically coherent foundation for infrastructure resilience planning, regulatory compliance, and long-term investment decisions.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2023

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

A variety of US electric power sector capacity expansion models are used by decision makers and analysts to evaluate competition among generation, transmission, and storage technologies to meeting the demands of the system. CEMs use least-cost optimization to identify optimal portfolios of investments capable of satisfying all specified requirements. While CEMs are a useful tool to inform pathways to meet future needs, projections can differ significantly between tools for apparently similar scenario assumptions. Differences in model structure, scope, and input assumption contribute to this issue. This work compares model response with harmonization between four modeling teams on issues significant to the representation and development of nuclear energy.

capacity expansion model↗
DOE OSTI2022

Using a Grid-Forming Inverter to Stabilize a Low-Inertia Power System - Maui Hawaiian Island

As power systems around the world integrate greater amounts of wind and solar photovoltaic power, periods of very high instantaneous power shares of inverters, the primary interfacing technology for these generation sources, are complicating system stability and control. The contemporary, primary mode of inverter operation, grid-following, which explicitly assumes the presence of a local, stable voltage waveform, yields operational inadequacy at high instantaneous power shares potentially leading to instability due to the low-inertia conditions, as well as the correlated reduced voltage forming capacity on the respective system. Parallel connected grid-forming inverters, which directly regulate the local voltage, are a solution that is expected to bolster system stability and mitigate the shortcomings of the grid-following technology. In this paper, grid-following and two types of grid-forming inverter control, the traditional linear droop and the recently introduced nonlinear exponential droop (Droop-e), are simulated on a low-inertia, H = 0.48s, high inverter-based resource scenario, 97%, with a validated electromagentic transient domain model of the Hawaiian island of Maui power system. The benefit of a single grid-forming device over its grid-following counterpart is significant, both in terms frequency deviation and voltage stability. Further, the superiority of the Droop-e and the associated secondary power sharing control over both grid-following and linear droop grid-forming technologies is displayed, with improved nadir and rate of change of frequency over the linear droop control.

droop control↗