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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Virtual Trajectories for I–24 MOTION: Data and Tools

This paper introduces a toolbox for generating virtual trajectories, which we call VT-tools v1.0, to address challenges in analyzing large but imperfect trajectory datasets. VT-tools v1.0 is able to generate virtual trajectories from large raw datasets that are typically challenging to process due to their size. We also provide a set of these virtual trajectories resulting from I–24 MOTION INCEPTION v1.0 data and demonstrate the practical utility of these trajectories in as-sessing speed variability and travel times across different lanes within the INCEPTION dataset. The virtual trajectory toolbox opens future research on traffic waves and their impact on energy. The VT-tools v1.0 Python implementation and sample data are publicly available at https://i24motion.org.

42 ENGINEERING↗
DOE OSTI2025

A Hardware-in-the-Loop Experimental Testbed Using Air Conditioners for Grid Balancing

Driven by the need to offset the variability of renewable generation on the grid, development of load control is a highly active field of research. However, practical use of residential loads for grid balancing remains rare, in part due to the cost of communicating with large numbers of small loads and also the limited experimentation done so far to demonstrate reliable operation. To establish a basis for the safe and reliable use of fleets of compressor loads as distributed energy resources, we constructed an experimental testbed in a laboratory, so that load coordination schemes could be tested at extreme conditions. Here, this experimental testbed was used to tune a simulation testbed to which it was then linked, thereby augmenting the effective size of the fleet. Modeling of the system was done both to demonstrate the experimental testbed's behavior and also to understand how to tune the behavior of each load. Implementing this testbed has enabled rapid turnaround of experiments on various load control algorithms, and year-round testing without the constraints and limitations arising in seasonal field tests with real houses. Experimental results show the practical feasibility of an ensemble of small loads contributing to grid balancing.

Air Conditioners↗
DOE OSTI2022

Geographical variability of summer- and winter-dominant onshore wind

For each geographical region, one of the biggest challenges in reaching a zero-carbon grid is identifying sources of electricity that match the seasonal profile of the load. Summer-dominant solar electricity generation can often be balanced by winter-dominant wind electricity generation. Together with long-duration storage, balanced solar and wind generation are well positioned to provide reliable renewable electricity. However, in some locations the wind may not complement solar energy so well. For example, currently California's wind turbines produce more electricity during summer than winter, raising the question of whether all future wind plants in California will exhibit the same seasonality. As a response to this question, in this paper, we analyzed the generation from existing California wind plants and simulated potential onshore wind resource for the whole state using a metric that reflects the relative wind resource in winter. Our results indicate that the seasonality of the wind can vary for very small spatial difference with more than half of California showing stronger wind resource in the winter compared with the summer despite the current observation of the opposite trend. This study differentiates the seasonality of potential wind resources to inform the creation of a reliable, 100%-renewable-driven grid.

17 WIND ENERGY↗
DOE OSTI2020

Numerical Model Development of a Variable-Geometry Attenuator Wave Energy Converter

Because the wave energy industry is still in its infancy, an optimal design for wave energy converters (WECs) has yet to be established; more work is needed to explore various cost-reduction pathways. The primary cost-reduction pathway considered for this work is the optimization of the geometric profile on an attenuator WEC to maximize power production while, at the same time, minimizing capital expenditures through the use of variable-geometry modules. In this investigation, the variable-geometry modules consist of inflatable bags placed on either side of a base central steel cylinder that would be inflated in low-moderate sea states to maximize power capture and then deflated in moderate-extreme sea states to minimize wave loading. The numerical model and simulation of the attenuator WEC were developed and completed using WEC-Sim, which is an open-source code that is appropriate for use in evaluating the dynamic response of the different WEC models in operational seas. The power production estimates were obtained from the Wave Energy Prize (WEP) sea states, which are representative of U. S. deployment sites, to calculate the average climate capture width that is used in the WEP ACE calculation. Preliminary capital expenditure costs were obtained assuming the base central steel cylinder mass was equal to the fluid displaced mass, minus the mass of the variable-geometry bags. The additional weight required to offset the additional buoyancy from the variable-geometry bags was assumed to come from the addition of seawater ballast. The variable-geometry attenuator model was found to have a similar power capture efficiency as a fixed-body model, but is expected to have a lower characteristic capital expenditure given its more streamlined profile, which demonstrates that variable-geometry modules may provide a realistic cost-reduction pathway to help design a more cost-competitive WEC.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Turbulent exchanges between near-inertial waves and balanced flows

Wind generated near-inertial waves are ubiquitous in the upper ocean. An improved understanding of near-inertial wave dynamics following their excitation in the ocean and their subsequent interaction with mesoscale geostrophic balanced flows is key to decoding oceanic energy flow pathways. In this regard, multiple oceanic data sets accumulated over the past few decades reveal that the relative strength of near-inertial waves and geostrophic balanced eddy fields is highly variable, both geographically and seasonally. Inspired by these observations, we investigate turbulent interactions and energy exchanges between near-inertial waves and balanced flows using freely evolving numerical simulations of the non-hydrostatic Boussinesq equations. Here, we find accelerated vertical propagation and dissipation of the waves in regimes where balanced and wave fields have comparable strengths. In such regimes we also find that near-inertial waves directly extract energy from balanced flows, with O(10%) being the amount of balanced energy loss. In contrast, we find that near-inertial waves transfer energy to balanced flows in regimes where balance-to-wave energy ratio is small, with the gain in balanced energy being dependent on the relative strength of waves. Furthermore, these regimes are characterized by relatively weaker vertical propagation and dissipation of the near-inertial wave field. One of the key outcomes of this study is the demonstration of the lack of a unique direction for near-inertial wave-balanced flow energy transfers. Depending on the balance-to-wave energy ratio, near-inertial waves can act as an energy sink or energy source for the geostrophic balanced flow.

58 GEOSCIENCES↗
DOE OSTI2025

Tuning water dissociation at oxide–electrolyte interfaces with electric fields

Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio–based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO 2 –electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 eÅ, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, we find that small electric field changes exert minor effects on individual reaction energy barriers but significantly influence the populations of local configurations associated with initial states that are most favorable for reactions. These findings elucidate the pronounced impact of electric fields on interfacial water dissociation and reveal a mechanism for electric-field-controlled chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗
DOE OSTI2024

Building ControlScore: General Service Administration Office Building Deployment

Improvements to building control systems can lead to energy savings and increased occupant comfort. In an optimized system, process variables such as air temperature will closely follow their desired setpoints and avoid excess energy use. Typically, experts must manually inspect individual control loops to identify poor performance and opportunities for improvement. However, this approach is difficult in modern buildings that have a prohibitively large number of controllers. To address this issue, Pacific Northwest National Laboratory (PNNL) created the ControlScore concept, which takes operating data from the many controllers within a building and generates standardized scores for each loop on a scale of 0 to 10 (a score of 0 indicates poor control, a score of 10 indicates good control). PNNL applied the Building ControlScore application to all available data from a General Services Administration office building within the period of January 1, 2023, to March 9, 2023. The building scored a 4.7 overall, with all 74 of the building’s loops fitting a roughly normal distribution centered around 5. These results indicate that the analyzed systems have below-average performance with room for improvement, especially in the poorly scored systems. Airflow loops tended to have much lower scores than zone temperature loops. The lowest and highest performing systems in the building section were identified, as were all loops with a score less than 1. While the ControlScore identifies loops and systems that aren’t meeting their designated setpoints, it does not indicate the cause of those issues. For example, consider a supply air terminal unit’s airflow loop that received a low score due to it delivering less air than specified by the setpoint. The lower-than-desired airflow could be due to equipment limitations (e.g., the terminal unit or duct serving is too small to accommodate that airflow), malfunctioning equipment (e.g., a stuck damper or bad sensor), or something else entirely. The ControlScore does not diagnose problems it simply identifies the symptoms that can be explored and addressed by building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Building ControlScore: Research Laboratory Building Deployment

Improvements to building control systems can lead to energy savings and increased occupant comfort. In an optimized system, process variables such as air temperature will closely follow their desired setpoints and avoid excess energy use. Typically, experts must manually inspect individual control loops to identify poor performance and opportunities for improvement. However, this approach is difficult in modern buildings that have a prohibitively large number of controllers. To address this issue, Pacific Northwest National Laboratory (PNNL) created the ControlScore tool which captures operating data from the many controllers within a building and generates standardized scores for each loop on a scale of 0 to 10 (a score of 0 indicates poor control, a score of 10 indicates good control).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

CMIP7 data request: Earth system priorities and opportunities

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Review of data-driven models for quantifying load shed by non-residential buildings in the United States

Shifting and shedding power demand in buildings can be cost-effective techniques for grids to function reliably and for end users to earn compensation. Grid operators reimburse customers in proportion to the quantity of load shed. Simple data-driven methods are used to quantify this shed, which is the difference between a measured load during the event and modeled "baseline" that would have occurred in absence of the event. These methods have evolved over the years and in many cases have been integrated with building physics, to make them a hybrid between physics based and empirical models. However, there is no comprehensive analysis that provides guidance to building operators, grid operators and researchers in selecting appropriate models based on their specific needs and available data. Here, this work aims to fill this gap by critically assessing the performance of baseline models put forward from the year 2000 through 2023. The literature reviewed includes reports generated by grid operators, reports from national laboratories and academic journal articles. The work outlines modeling features like the inputs, training period, estimation method, adjustments to fine tune the predictions and metrics to evaluate the performance. A comprehensive list of 50 models has been provided. For each model, the study explores the applicability of the model to weather sensitive buildings, variability in the building profile, timing of the event, and whether the building reduces energy consumption before an event. The work identifies the situations in which a particular model works and draws lessons based on evidence of performance. Finally, recommendations to aid in model selection are given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity: Model Archive

This Modeling Archive is in support of an NGEE Arctic publication "Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity" in the Journal Biogeosciences (https://doi.org/10.5194/bg-17-5849-2020), which includes the model data used in the publication. CH4 emissions from terrestrial systems are posited to increase, which can offset mitigation efforts and accelerate climate change. Yet, the accuracy of modeled CH4 emissions is sensitive to the prescribed CH4 production (or emission) temperature dependencies that are currently uncertain. Here, we use a comprehensive biogeochemistry model (ecosys) to investigate factors modulating CH4 production and emission rates across a permafrost thaw gradient encompassing a partly thawed bog and a fully thawed fen. We find that seasonally varying substrate availability drives lower and higher modeled methanogen biomass and activity, and thereby CH4 production, during the earlier and later periods of the thawed season, respectively. Package follows the Model data archiving guidelines with data in a *.zip file with three subfolders containing *.csv files and raw model output files; a data dictionary table (data_dictionary.csv) and two model output description tables (ecosys_plantspecies_output_notes.csv and ecosys_soil_ouput_notes.csv) to explain the format and meaning of individual output variables; and a user guide as a *.pdf. A detailed model description can be found in the supplement of (Grant, 2013). The ecosys source code is available at https://doi:10.5281/zenodo.3906642. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An Analysis of PNM's Renewable Reserve Requirements to Meet New Mexico's Decarbonization Goals

Over the next three years, the Public Service Company of New Mexico (PNM) plans to increase utility-scale solar photovoltaic (PV) capacity from today’s roughly 330MW to about 1600MW. This massive increase in variable generation—from about 15% to 75% of peak load—will require changes in how PNM operates their system. We characterize the 5 and 30-minute solar and wind forecast errors that the system is likely to experience in order to determine the level of reserves needed to counteract such events. Our focus in this study is on negative forecast error (in other words, shortfalls relative to forecast) – whereas excess variable generation can be curtailed if needed, a shortfall must be compensated for to avoid loss of load. Calculating forecast error requires the use of the same forecasting methods that PNM uses or a reasonable approximation thereof. For wind, we use a persistence forecast on actual 5-minute 2019 wind output data (scaled up to reflect the amount of wind capacity planned for 2025). For solar, we use a formula incorporating the clear sky index (CSI) for the forecast. As the solar on the grid now is a small fraction of what is planned for 2025, we generated 5-minute solar data using 2019 weather inputs. We find that to handle 99.9% of the 5-minute negative forecast errors, a maximum of 275MW of variable generation reserve during daylight hours, and a maximum of 75MW during non-daylight hours, should be sufficient. Note that this variable generation reserve is an additional reserve category that specifies reserves over and above what are currently carried for contingency reserve. This would require a significant increase in reserve relative to what PNM currently carries or can call upon from other utilities per reserve sharing agreements. This variable generation reserve specification may overestimate the actual level needed to deal with PNM’s planned variable generation in 2025. The forecasting methodologies used in this study likely underperform PNM’s forecasting – and better forecasting allows for less reserve. To obtain more precise estimates, it is necessary to consider load and use the same forecasting inputs and methods used by PNM.

14 SOLAR ENERGY↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗