SEARCH · Engineering Papers

Results for “variable load”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Stable and Recyclable Photocatalysts of CsPbBr 3 @MSNs Nanocomposites for Photoinduced Electron Transfer RAFT Polymerization

All-inorganic metal halide perovskite CsPbX 3 (X = Cl, Br, I) nanocrystals (NCs) have demonstrated attractive optoelectronic characteristics. However, their photocatalytic properties are limited by their poor stability and easy recombination of photogenerated carriers. Herein, we introduced a CsPbBr 3 @MSNs nanocomposite (CsPbBr 3 NCs embedded in dendritic mesoporous silica nanospheres (MSNs)) as photocatalysts for photoinduced electron transfer reversible addition–fragmentation chain transfer (PET-RAFT) polymerization. In this study, the CsPbBr 3 nanocrystals (~8.1 nm; PLQY of 62 ± 2.1%) were embedded in dendritic MSNs using a nanoconfinement strategy. PET-RAFT polymerization was successfully initiated using the CsPbBr 3 @MSNs nanocomposite as the photocatalyst. Reaction variables, such as catalyst loading, monomer composition, and excitation light wavelengths, were varied to yield polymers with the desired control of molecular weight and dispersity as well as block copolymers with high chain-end fidelity. In addition, the perovskite-based photocatalysts could be readily separated and purified, which allowed effective and rapid recycling of the nanocomposites for multiple polymerization cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

GRAF-Plan for Vietnam

Evaluates the reserve requirements for power system balancing areas based on variability and uncertainty of load as well as wind and solar generation scenarios. The tool uses minute‐by‐minute site‐specific generation and load information, as well as information from generation and load forecasting algorithms used in the balancing areas. The Balancing‐Plan Tool can be used directly by utility planners and operators to aid in the integration of intermittent renewable resources. The tool provides reserve requirements of various kinds, such as day‐ahead, load following and regulation, as well as estimates the capacity of the generation fleet to provide the require reserves.

Campbell, Allison↗

DOE OSTI2022

TRIPOLI-4 neutronics calculations for IAEA-CRP benchmark of CEFR start-up tests using new libraries JEFF-3.3 and ENDF/B-VIII

Sodium-cooled Fast Reactors (SFR) are one of the promising Generation IV fast reactors. The China Experimental Fast Reactor (CEFR) is a 65 MWth pool-type SFR with a high neutron leakage core using high enriched uranium oxide fuel. The CEFR start-up tests in 2010 consist of series of neutronics experiments. Essential experimental data are available from the 2018 IAEA-CEFR-CRP benchmark document and useful for the validation of neutron transport codes and nuclear data libraries. The TRIPOLI-4 Monte Carlo transport code is a general-purpose neutronics code using continuous-energy nuclear data libraries. It has a rich validation database covering different computational and experimental benchmark data sets to assure the accuracy and credibility of numerical studies. Previous TRIPOLI-4 SFR core physics calculations used mainly MOX fuels. SFR control rod worth studies with TRIPOLI-4 were mainly on big-size cores. To test the modeling capability of TRIPOLI-4 on fuel loading patterns and on variable control rods positions for SFRs and to check recent nuclear data libraries, it is interesting to investigate the CEFR start-up tests in this work, including core states from subcritical to supercritical, control rods and rod group worth, point kinetics parameters, and radial foil activation measurements. TRIPOLI-4 calculation results using new data libraries JEFF-3.3, ENDF/B-VIII, and those of ENDF/B-VII.1 for different CEFR core states are reported here. By means of different options of TRIPOLI-4, control-rod SAs reactivity worth, point kinetics parameters, and radial {sup 237}Np(n,f) fission rate distribution were successfully evaluated. Both ENDF/B-VIII and JEFF-3.3 nuclear data libraries provided reliable results with a difference of 170 +/- 11 pcm in k{sub eff}.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Simulink Model of a New Energy EVG-005 5kW Hydropower Turbine

Simulink model for a New Energy 5kW hydropower turbine. ADCP data ("ds_streamwise_7_13.nc") and DC voltage, DC current, and rotor rotation observed from the New Energy EVG-005 Current Energy Converter (CEC) ("electrical_7_13_10ohms.nc") were collected at the Tanana River Test Site (TRTS) near Nenana Alaska. - Data was collected on July 13th, 2023 with a constant 10 ohms resistance applied with a DC load bank. - Simulink model is meant to resemble the electrical setup at the TRTS. - Model is initialized by running the "NewEnergy_2023_10hz.m" Matlab script. Then the Simulink model ("New_Energy_Model_PMSM.slx") can be run. - Results are processed with the "NewEnergy_2023_processing.m" Matlab code. The Matlab results are also saved in the "New_Energy_7_13_model_validation_results.mat" Matlab file. This can be directly loaded into the Matlab Workspace using the "Load()" command. - The timetable variables "electrical_model_downsampled" is the model results and the "electrical_limited" is the experimental data from the TRTS.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Net-Zero Carbon Microgrids

The microgrid concept has been effective in creating aggregations of distributed energy resources—generation, storage and loads—for resiliency, in the form of energy security. The success of microgrids in bringing energy security to a wide range of customers—from individual residences to commercial and industrial installations to military bases—has been exemplified during power disruptions and extended outages due to extreme weather events, cybersecurity attacks, and equipment failures. Now microgrids have an opportunity to meet the challenges of climate change and contribute to a carbon-free power delivery system. The transition to net-zero starts within microgrids themselves. In fact, today’s microgrids are largely dominated by generators using fossil fuels, natural gas and diesel, with high greenhouse gas emissions. In short, the transition to net-zero means replacing fossil fueled generators with renewable generation in microgrids. This transition is extended by including new dispatchable generation technologies that are 100% carbon-free and that offer additional advantage of a more-dependable and sustainable source of energy and power. Basically, the decarbonization of microgrids requires three elements: 1) maximizing generation from renewable energy resources, 2) management of storage and flexible loads to balance the variability and intermittency of renewable energy resources, and 3) introducing new clean power sources, including hydrogen-based generation and small modular reactors. This report affirms a need for specific focus by governmental agencies at national, regional, and local levels to establish technology, policy, and investment in this area. The intention of the Net-Zero Microgrid (NZM) Program is to inform these constituencies with cross-cutting research and tools for the reduction of GHG in microgrids – to net-zero in the near term eventually to zero in the longer term. The NZM Program is committed to achieving decarbonization for resiliency and for providing clean energy at the local or distribution level, from remote communities to underserved communities, and large industrial and military facilities. The NZM Planning and Design Platform is a core tool to be developed as an early deliverable of the NZM Program because only a fully integrated microgrid-design approach will ensure maximum carbon reduction in energy production.

13 HYDRO ENERGY↗

DOE OSTI2022

Energy Performance of Awnings in Residential Buildings

Residential buildings consume approximately 20% of the total primary energy in the United States. More than 50% of this energy is spent in heating, cooling, and lighting these buildings. Solar heat gain is one of the largest and most variable sources of cooling load in these buildings, while it can also provide passive heating during the heating season. Shading devices can be used to control the amount of solar heat gain in buildings. Various studies have considered how different shading devices and their applications affect energy and occupant comfort in buildings. However, most of these studies were limited to planar shading devices such as roller shades, cellular shades, and blinds. Although some theoretical studies have been performed for awnings, the energy performance of awnings has rarely been studied via either energy simulation or field measurement. In this study, the authors evaluated the energy performance of typical operable awnings by using field data, aided by simulation. Awnings were installed on a real house, and measurements were performed to evaluate the thermal performance of the awning. The measured data were then used to develop a calibrated energy model and evaluate the awning’s energy performance. The annual simulation of the building model used showed that awnings left in the closed position from April to September can reduce annual HVAC energy consumption by 15% compared with a building without any shades. The validated model was used in US Department of Energy prototype buildings to evaluate awning energy performance in climate zones 1A through 4B via energy simulation. For these prototype buildings, energy savings of up to 1,034 kWh were achieved for a building with a conditioned floor area of 2,377 ft 2 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when coordinated with detailed operational strategies; (ii) multi-year data is critical to capture a wide variety of system conditions; (iii) not incorporating transmission limits into RA assessment could lead to substantial underestimation of traditional “expected value” RA metrics; and (iv) new RA metrics that capture event-specific shortfall characteristics should be used as supplements to traditional metrics. Finally, we examine RA assessments and use this information to propose a guide of evolving industry standards for resource adequacy assessments in resource planning and transmission planning. We report minimum, best, and frontier practices for temporal resolution of assessments, metrics and targets, weather data, load forecasting, characterization of variable renewable resources, characterization of transmission and market transactions, RA modeling and integration with planning processes, and capacity accreditation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Dynamic Modeling and Simulation of a Subcritical Coal-Fired Power Plant under Load-Following Conditions

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long-time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Load Control for Frequency Response - A Literature Review

As electricity grids employ greater fractions of renewable energy, which introduce additional variability and uncertainty in the net load, balancing electrical load and generation becomes more challenging. This paper reviews the literature documenting physical simulations and real systems that employ load control (LC) for frequency response and other grid services, which balance net load on the grid and prevent unwanted frequency excursions. Apart from academic and simulation studies, few sources exist on large-scale laboratory hardware testing or actual real-world systems that employ LC for frequency response, and we review them here. Four types of systems that we consider are: 1) Laboratory-based LC experiments, 2) Isolated microgrids that employ LC, 3) Larger grids that employ LC and 4) vehicle-to-grid (V2G) technology, using electric vehicles (EVs). In general, these systems have successfully used LC to meet their objectives, which are often keeping grid frequency within a required band. However, LC struggled to balance grid frequency in an isolated system powered by a single wind turbine, and V2G technology requires refinement in communication and control to provide optimal regulation that adheres to industry standards. As LC grows in the energy industry, we have three main recommendations: 1) encouraging system operators who use LC to publish system characteristics and lessons learned; 2) transitioning more LC theoretical/simulated systems to physical experiments, and physical experiments to real-world pilot systems; 3) demonstrating load control to support isolated, high-wind-contribution systems.

17 WIND ENERGY↗

DOE OSTI2022

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting demand response using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings (GEBs) to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Keskar, Aditya (ORCID:0000000244617980)↗

DOE OSTI2025

Load Matching Potential of Urban Renewables during Extreme Heat in New York City

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on the matter by assessing the feasibility of using offshore wind and rooftop photovoltaic systems to meet electricity demand during a period of thermal stress in New York City—August 2019. A unified modelling framework, based on the Urbanized Weather Research and Forecasting model, is used to simulate climate, renewable resources and energy demand variables. Findings show significant energy load miss-match, ranging from 3 to 5 GW, between the demand and the composite renewable generation outcome. This study provides a transferable framework for evaluating renewable integration in dense urban regions and highlights the need for additional strategies to support grid resilience during extreme heat events, while clarifying the role of local resources in fully managing NYC’s load.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

alternating direction method of multipliers↗

DOE OSTI2026

Unified Modeling Architecture for Load Management in Extreme Heat: The New York City Case

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).

14 SOLAR ENERGY↗

DOE OSTI2024

Distributed Wind for Commercial Loads

Distributed wind can help meet on site energy and resilience needs for a wide variety of commercial loads .While it is a variable resource, it has predictable daily and annual production trends. The presence of on-site renewable generation can enhance resilience, supplying power for long periods of time without worrying about conserving fuel supply. The addition of properly sized storage or complementary solar resources can further reduce the challenges of intermittency and reduce the need for fuel-limited backup power during outage situations. This fact sheet explores further the energy and resilience requirements of commercial loads and how distributed wind may be a good match to meet their load needs.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Distributed Wind for Commercial Loads

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗