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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗
DOE OSTI2014

Control algorithms for dynamic attenuators

Purpose: The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. Methods: The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not requirea priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. Results: The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current modulation) without increasing peak variance. The 15-element piecewise-linear dynamic attenuator reduces dose by an average of 42%, and the perfect attenuator reduces dose by an average of 50%. Improvements in peak variance are several times larger than improvements in mean variance. Heuristic control eliminates the need for a prescan. For the piecewise-linear attenuator, the cost of heuristic control is an increase in dose of 9%. The proposed iterated WMV minimization produces results that are within a few percent of the true solution. Conclusions: Dynamic attenuators show potential for significant dose reduction. A wide class of dynamic attenuators can be accurately controlled using the described methods.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2021

Rapid QSTS Simulations for High-Resolution Comprehensive Assessment of Distributed PV

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Sensitivity of Optimal Solutions to Control Problems for Second Order Evolution Subdifferential Inclusions

In this paper the sensitivity of optimal solutions to control problems described by second order evolution subdifferential inclusions under perturbations of state relations and of cost functionals is investigated. First we establish a new existence result for a class of such inclusions. Then, based on the theory of sequential Γ-convergence we recall the abstract scheme concerning convergence of minimal values and minimizers. The abstract scheme works provided we can establish two properties: the Kuratowski convergence of solution sets for the state relations and some complementary Γ-convergence of the cost functionals. Then these two properties are implemented in the considered case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Measure Guideline: Combined Space and Water Heating Installation and Optimization

Combined space and water heater (combi or combo) systems are defined by their dual functionality. Combi systems provide both space heating and water heating capabilities with a single heat source. This guideline will focus on the installation and operation of residential systems with forced air heating and domestic hot water (DHW) functionality. Past NorthernSTAR research has used a combi system to replace a natural gas forced air distribution system furnace and tank type water heater (Schoenbauer et al. 2012; Schoenbauer, Bohac, and McAlpine 2014). The combi systems consisted of a water heater or boiler heating plant teamed with a hydronic air handler that included an air handler, water coil, and water pump to circulate water between the heating plant and coil. The combi water heater or boiler had a separate circuit for DHW. Past projects focused on laboratory testing, field characterization, and control optimization of combi systems. Laboratory testing was done to fully characterize and test combi system components; field testing was completed to characterize the installed performance of combi systems; and control methodologies were analyzed to understand the potential of controls to simplify installation and design and to improve system efficiency and occupant comfort. This past work was relied upon on to create this measure guideline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Measure Guideline: Combined Space and Water Heating Installation and Optimization

Combined space and water heater (combi or combo) systems are defined by their dual functionality. Combi systems provide both space heating and water heating capabilities with a single heat source. This guideline will focus on the installation and operation of residential systems with forced air heating and domestic hot water (DHW) functionality. Past NorthernSTAR research has used a combi system to replace a natural gas forced air distribution system furnace and tank type water heater (Schoenbauer et al. 2012; Schoenbauer, Bohac, and McAlpine 2014). The combi systems consisted of a water heater or boiler heating plant teamed with a hydronic air handler that included an air handler, water coil, and water pump to circulate water between the heating plant and coil. The combi water heater or boiler had a separate circuit for DHW. Past projects focused on laboratory testing, field characterization, and control optimization of combi systems. Laboratory testing was done to fully characterize and test combi system components; field testing was completed to characterize the installed performance of combi systems; and control methodologies were analyzed to understand the potential of controls to simplify installation and design and to improve system efficiency and occupant comfort. This past work was relied upon on to create this measure guideline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Challenges in reducing the computational time of QSTS simulations for distribution system analysis.

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.

AI↗
DOE OSTI2018

Innovative Technologies for Optical Detection of Stress Corrosion Cracks

Stress corrosion cracks (SCC) represent a major concern for the structural integrity of engineered metal structures. In hazardous or restricted-access environments, remote detection of corrosion or SCC frequently relies on visual methods; however, with standard VT-1 visual inspection techniques, probabilities of SCC detection are low. Here, we develop and evaluate an improved optical sensor for SCC in restricted access-environments by combining a robotically controlled camera/fiber-optic based probe with software-based super-resolution imaging (SRI) techniques to increase image quality and detection of SCC. SRI techniques combine multiple images taken at different viewing angles, locations, or rotations, to produce a single higher- resolution composite image. We have created and tested an imaging system and algorithms for combining optimized, controlled camera movements and super- resolution imaging, improving SCC detection probabilities, and potentially revolutionizing techniques for remote visual inspections of any type.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Feedback-Based Fault-Tolerant and Health-Adaptive Optimal Charging of Batteries

The key technology barriers that hinder the growth of Electric Vehicles (EVs) are long charging time, the shorter life-time of EV batteries, and battery safety. Specifically, EV charging protocols have significant effects on battery lifetime and safety. If not charged properly, the battery could end up with shorter life, and more importantly, improper charging can cause battery faults leading to catastrophic failures. To overcome these barriers, we propose a closed-loop feedback based approach, that enables real-time optimal fast charging protocol adaptation to battery health and possess active diagnostic capabilities in the sense that, during charging, it detects real-time faults and takes corrective action to mitigate such fault effects. We utilize battery electrical-thermal model, explicit battery capacity and power fade aging models, and thermal fault model to capture battery behavior. In conjunction with the models, we adopt linear quadratic optimal control techniques to realize the feedback-based control algorithm. Simulation studies are presented to illustrate the effectiveness of the proposed scheme.

batteries↗
DOE OSTI2022

Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

Electric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic dispatch model of 2035 generation. We find that peak net electricity demand increases by up to 25% with forecast adoption and by 50% in a stress test with full electrification. Locally optimized controls and high home charging can strain the grid. Shifting instead to uncontrolled, daytime charging can reduce storage requirements, excess non-fossil fuel generation, ramping and emissions. Our results urge policymakers to reflect generation-level impacts in utility rates and deploy charging infrastructure that promotes a shift from home to daytime charging.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Energy-Optimal Vehicle Longitudinal Motion Control via Pontryagin’s Minimum Principle and Ultra-Local Model

Longitudinal vehicle motion control is essential for enhancing performance and optimizing a vehicle’s energy usage. However, it remains a challenging task due to the nonlinear and uncertain nature of vehicle dynamics, along with varying driving conditions. This paper presents a novel ultra-local optimal control approach based on Pontryagin’s Minimum Principle (PMP) that circumvents the need for detailed system identification by employing an ultra-local model. The control objective is to minimize the total energy consumption under boundary conditions while ensuring smooth traction force generation. The proposed approach is evaluated using a high-fidelity vehicle model in three representative scenarios: (i) nominal driving, (ii) a change in tire road friction coefficient (TRFC) from 0.5 to 0.65 and road slope from 0% to 5% during the maneuver, with target velocity unchanged, and (iii) a change in target velocity from 20 m/s to 0 m/s during the maneuver, while maintaining nominal TRFC and slope conditions. The simulation results demonstrate that the proposed method delivers robust performance, effectively balancing consumption and tracking accuracy in all tested scenarios.

Waleed khan, Muhammad [The University of Texas at ↗
DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Secondary Voltage Control Via Demand-Side Energy Storage with Temporal Logic Specifications

This paper proposes a secondary voltage control method that can handle the temporal logic specifications (TLSs). The control objective is to schedule a control signal for energy storage system (ESS) such that the voltage variation of a defined critical bus satisfies TLSs. The TLS allows definitions of set and timing constraints at the same time, such as a finite-time restoration. A simplified analytical model is derived to describe the voltage variation of critical buses. Based on this model, a numerical optimal control problem is formulated as a mixed integer linear program to generate the proper control input for the ESS. The proposed control is verified on a lumped distribution system model. With this control diagram, supportive controllers can be easily designed to make voltage behaviors comply with grid codes and avoid unnecessary relay actions.

Zhang, Yichen↗
DOE OSTI2019

Load Flexibility Analysis of Residential HVAC and Water Heating and Commercial Refrigeration

The emergence of connected devices and the Internet of Things has opened the door to the concept of widespread use of building equipment for load-side flexibility. This stems from the increased use of distributed and clean energy resources for electric generation and the growing demand for a more resilient and reliable grid. A significant challenge associated with the use of building equipment as a distributed energy resource is quantifying the magnitude of power or energy flexibility. Unlike batteries that have well-defined and relatively constant capacity and maximum charge/discharge rates, building equipment often has characteristics that vary based on external factors (e.g., weather or occupant behavior). This makes quantifying the availability and flexibility of these resources difficult. Controlling a fleet of equipment while minimizing any adverse impacts to the occupants (e.g., discomfort or food spoilage) is a nontrivial matter. Although there are many different control methodologies available, this study focuses on two: priority-based control (PBC) and model-predictive control (MPC). These were selected because they represent two ends of the control spectrum. PBC is a relatively simple control strategy that requires only the current state of the devices to be controlled and an estimate of how much power each device consumes when turned on. MPC is much more complex and requires a model of the system and forecasts of any disturbances that influence the system. Because MPC has a predictive element, it can prepare for a desired response before the event and therefore is expected to provide a more optimal control relative to PBC. Both MPC and PBC are compared with a baseline simulation that uses feedback control typical of the thermostatically controlled devices being evaluated. This report investigates the load flexibility of three building systems: residential air conditioners, residential water heaters, and commercial refrigeration. Air heating and cooling and water heating account for 45% of residential electricity use, making them good candidates for providing load flexibility. Refrigeration in commercial buildings accounts for 16% of commercial building electricity use, making it another good candidate. Simplified models of each system were generated and validated based on measured data from actual equipment. These models were used to simulate the thermal and power response of the systems using PBC and MPC for a fleet of devices trying to achieve a variety of different responses, including hourly load shed, hourly load up, and daily peak demand reduction. Significant findings from each piece of equipment follow.

42 ENGINEERING↗
DOE OSTI2020

Determining the Reaction Time for Triggering Supportive Control Actions to Guarantee Adequate Frequency Response in Smart Grids

The progressive integration of renewable energy resources in the modern power grid can result in unsatisfactory frequency responses. To address this problem, wind turbine generators (WTGs), in particular, can be employed to support the power grid. Most works, however, only consider a simple step disturbance. In this paper, a new class of worst-case disturbances is introduced and their nefarious impact on renewable-penetrated power systems is investigated. The worst-case disturbances are derived using optimal control theory. More importantly, the proposed mechanism allows to determine the reaction time to trigger supportive control actions of WTGs to ensure satisfactory frequency response. Numerical results are provided and the effects of the worst-case disturbance on the reaction time are presented.

Luo, Jiecai↗