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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, here we present a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Resilient Operating Constraints for Power Distribution Systems under Setpoint Attacks

Integration and operation of distributed generation (DG) and energy storage (ES) in power distribution systems are enabled by communication networks and embedded sensor and control devices that increase the vulnerability of the systems to cyber-threats, broadening the attack surface and making adversary actions more unpredictable. This paper proposes a methodology that uses ellipsoidal approximations to quantify the potential damage caused by successful attacks that affect, directly or indirectly, the desired operation setpoints and may drive the power distribution operation to unsafe states by violating the limits of voltage or line flows. More specifically, a new methodology is introduced to find the optimal non-symmetric operating constraints that can be imposed to each DG and ES in order to guarantee that the power distribution system is resilient to any malicious setpoints. The proposed method takes as inputs the system topology, DG and ES capabilities, and load limits to solve a convex optimization problem formulated using linear matrix inequalities (LMIs) and the power flow equations. The proposed solution is agnostic to the attacker's action or load profile and it does not require any assumption about the location or means of the attack. The numerical results on a test distribution feeder with several DG and ES illustrate how the proposed resilient operating constraints guarantee the security of the power distribution system under setpoint attacks.

Giraldo, Jairo↗
DOE OSTI2024

Power System Operational Impacts of Electric Vehicle Dynamic Wireless Charging

The electrification of the transportation sector poses an opportunity for reducing greenhouse gas (GHG) emissions from passenger vehicles. Electric vehicle (EV) charging through dynamic wireless power transfer (DWPT), known as roadway electrification, could shift EV demand profiles to better coincide with renewable electricity generation. However, this would be a very large new load and few studies evaluate the regional impacts of DWPT charging in a power transmission system. This paper defines methods that address dataset generation for passenger vehicle trips and models to evaluate regional impacts for this emerging technology. Household vehicle miles traveled (VMT) data form localized EV demand profiles through discrete-event simulation. This data serves as exogenous inputs for a Production Cost Model (PCM) of a synthetic transmission system based on the Electric Reliability Council of Texas's (ERCOT) network. EV charging methods are compared for both a 2018 baseline generation mixture and a high-renewable generation case incorporating 20 GW of installed solar photovoltaic (PV) capacity. The PCM employs unit commitment and economic dispatch (UC&ED) models to compare financial, environmental, and grid reliability impacts from EV charging across passenger EV adoption levels. In-transit charging could reduce grid operational costs by as much as 1.49%, with up to $13.7B saved in annual vehicle operational costs for consumers compared to gas-powered vehicles. Health impacts analysis from power plant and vehicle tailpipe emissions from this study show net health benefits increase by 40% for in-transit charging coupled with high renewable generation. Renewable resources provide an avenue for cost-effective in-transit charging with reduced emissions. The combination of dataset generation and open-source power system modeling establish a foundation for the holistic evaluation of regional DWPT impacts.

dynamic wireless power transfer↗
DOE OSTI2020

An Assessment of Impacts on Electric End-Use Load Profile of a Typical Residential Building from a Ground Source Heat Pump Systems Integrated with Underground Thermal Energy Storage

Buildings are the primary users of electricity, and a considerable amount of electric energy consumed in residential and commercial buildings is for meeting thermal demands (e.g., space heating/cooling, water heating, etc.). Therefore, integrating thermal energy storage with a building’s energy system has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable power supply and the thermal demands of buildings. A novel ground-source heat pump system (GSHP) integrated with underground thermal energy storage (UTES) has been developed to enable flexible electric demand at buildings while meeting their thermal demands with an energy-efficient GSHP system. This paper assessed the potential impacts of the proposed system on the peak electrical demand and annual electricity consumption in a typical residential building at various climate zones in the United States. The results show that by replacing the conventional HVAC system (air conditioner and natural gas furnace) with the UTES integrated GSHP system, summer peak electric demand can be reduced by 27% to 50% depending on the weather. Although the winter peak electrical demand could be increased by up to 70% if the proposed system replaces natural gas furnace, the existing electricity supply system for single-family homes in most regions of the United States have adequate capacity for the replacement. While eliminating the natural gas consumption for space heating, the proposed system consumes more electricity on an annual basis in heating-dominated regions. However, it consumes less electricity in cooling dominated regions. The resulting variance in annual electricity consumption in various climate zones is within 15% compared with the baseline system.

Shi, Liang↗
DOE OSTI2026

Techno-Economic Assessment of Data Center Load Demand Powered by Small Modular Reactors and Distributed Energy Resources

The rapid increase in data center energy demand, driven by AI and large-scale data processing, poses significant challenges to global energy infrastructure. Data centers require substantial and reliable energy for continuous operations and high-performance computing. Current electrical grids face issues such as transmission bottlenecks and aging infrastructure, making it difficult to meet these demands. Integrating inverter-based-resources (IBRs) like solar and wind presents both opportunities and challenges due to their intermittent nature. Small Modular Reactors (SMRs) offer a promising solution with their enhanced safety, modularity, reliability, and scalability, providing consistent base load power ideal for data center operations. This study presents a comprehensive techno-economic assessment of powering data center load demand using a combination of SMRs and IBRs with grid-connected and islanded mode. This study utilized Idaho National Laboratory’s (INL) HPC data center hourly load profiles and Xendee microgrid optimization platform to conduct the analysis. In this configuration, SMRs serves as the primary base load power source, consistently providing a steady supply of electricity necessary to meet the minimum load demand of the data center with support from the IBRs. Key performance indicators such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV) has been calculated to assess the economic feasibility. The findings from this research will underscore the strategic benefits of integrating SMR plant with DERs – particularly for critical infrastructure load such as data centers.

14 - SOLAR ENERGY↗
DOE OSTI2026

AI4MG-networked-microgrid-models

SF-26-123 24-hour scaled power-flow analysis of a modified IEEE 123-bus distribution feeder using OpenDSS. The simulation runs 24 snapshot power-flow cases (one per hour) with independent hourly scaling profiles for base loads, added loads, generators, and battery storage, and writes per-hour CSV/Excel reports plus a 24-panel voltage-profile plot.

Kumar, Kiran [Argonne National Laboratory (ANL), A↗
DOE OSTI2020

Quantifying Solar PV Variability at Multiple Timescales for Power Systems Studies: Preprint

Increasing penetration of solar photovoltaics (PV) has concerned power system operators about their ability to balance load and generation over multiple timescales and maintain acceptable frequency profile because of the variability and uncertainty associated with solar PV. This paper focuses on developing metrics to quantify solar PV variability at multiple temporal scales ranging from seconds to an hour to help power system operators and planners better understand and account for solar PV variability under high solar PV penetration. Fourier coefficients of solar irradiation spectrum and average size of solar irradiation ramps over a day are the two metrics that are used to quantity the variability. It is shown that by using the two metrics the days with highest and lowest solar irradiation variability can be identified, which is useful information for operations planning or long-term power system planning studies. The key findings of the paper are (i) single sensor-based variability quantification can significantly exaggerate the variability of a solar PV plant, particularly at seconds to 1-min temporal resolution, (ii) at 1-minute or lower temporal resolutions, single sensor-based variability quantification of an entire plant can be fairly accurate, with the accuracy increasing as the temporal resolution reduces from 1-minute to 1-hour, (iii) at each 1-minute and lower temporal resolutions, maximum solar irradiation ramps appear to be independent of the geographical location of the sensor, and (iv) significant reduction in solar PV plant variability over a region with multiple plants is possible at all temporal scales, but more high quality, high temporal resolution data is needed to quantify the reduction with high confidence.

photovoltaics↗
DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis

The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.

building loads↗
DOE OSTI2021

Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems

Marine renewable energy (MRE) resources are highly predictable and persistent sources of energy, when compared to other renewable sources like wind and solar. These lend them favorably for potential grid applications, particularly for coastal/island power systems where their generation potential is high. Island power systems, on the other hand, are either supported by onsite generation or by transported energy from the mainland grid. Therefore, robustness of grid operations depend heavily on the diversity of onsite generation resources and the reliability of the power transportation medium. Issues relating to either of these two factors may lead to impediments in smooth and reliable operation of the power system. Analyzing and quantifying operational risks for such island power systems with diverse non-conventional generation portfolios through conventional techniques can also prove to be cumbersome, often requiring multiple different inputs. Therefore, in this paper, we firstly present a novel, purely data-driven formulation which quantifies the operational reliability of such island power systems through minimal input data. Specifically, our proposed methodology only relies on historical knowledge of typical hourly load and generation profiles to quantify associated operational risks. Subsequently, we use our proposed formulation to evaluate the effectiveness of MRE resources (over other renewable resources like wind and solar) in providing resilience benefits to island power systems. The proposed formulation is demonstrated with a case study for an island power system in Nantucket, MA.

Chalishazar, Vishvas H.↗
DOE OSTI2023

Techno-Economic and Deployment Analysis of Fossil Fuel-Based Power Generation with Integrated Energy Storage

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenues. Depending on plant type and design, these plants can adjust output within a fixed range in response to plant or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation such as wind and solar is shifting traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. The recent studies have shown that flexibility of a coal-fired power plant can be improved by the energy storage. The objective of this project was to analyze a set of energy storage options (technologies) and determine their impact on flexibility and economics of a representative coal-fired power plant. The effect of five Energy Storage Systems (ESSs) integrated with a coal power plant on plant flexibility and economics was investigated in this study. The results obtained in this project and presented in this report showed that ESS integrated with a thermal power plant improves plant flexibility and participation in the Energy and Ancillary Services markets and also improves plant financial performance.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2022

Economic Dispatch Optimization of Multi-Unit SMR Site

With the increase of integration of variable renewable into the grid, grid resilience can be compromised without flexible electricity suppliers. Flexible operation of NPP can expand the scope of integration of new nuclear power plants in regions with high share of renewables. It also provides opportunity to increase profits through participation in ancillary services. In this research, NuScale modules are used as the case study. NuScale is an integrated pressurized water reactor with thermal power of 250 MWth. Having multiple units can provide higher flexibility in changing power levels and supplying the grid with electricity all year long. Additionally, load-following might perturbate the ordered sequence of refueling (every 24 months for NuScale). To maintain the 2 year refueling cycles, the loading patterns will need to be adjusted in a year-to-year basis, resulting in additional unnecessary costs. Another solution is to find the optimal outage schedule. This will also shift the focus of the planned outage to a more optimized predictive outages and maintenance. Results from short-term optimization (i.e., daily participation in energy and ancillary services markets) shows that average commitment to regulation down ancillary service markets during late night and early morning hours can increase revenues. Participating in upward reserves during pre-work commuting hours and early evening hours can also generate additional revenues due to high ramp up of electricity demand. Having multiple units at a single site can also help operate flexibly. Results from long-term optimization show that daily fluctuation in electric power is usually handled by few units while other units operate on base-load profile. It is possible to combine base-load operation with flexible operation to extend component remaining useful life in some units and find optimal outage schedule for the units.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Energy Storage Improves Power Plant Flexibility and Economic Performance

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation, such as wind and solar, is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and a lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that the flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on the flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that energy storage systems integrated with a thermal power plant improve plant flexibility and participation in the energy and ancillary services markets, which improves plant financial performance.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning↗