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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unleash Values From Grid-Edge Flexibility: An Overview, Experience, and Vision for Leveraging Grid-Edge Distributed Energy Resources to Improve Grid Operations

The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.

distributed energy resource↗
DOE OSTI2022

Design Power Control Strategies of Grid-Forming Inverters for Microgrid Application

This paper develops and compares two control schemes in the application control layer of a non-phase-locked loop (non-PLL) grid-forming (GFM) inverter to gain insight and understanding into how the two schemes affect the dynamic responses of GFM inverters and the transition operation of microgrids. The first scheme adopts power tracking based on an outer current loop in grid-connected mode and droop control in islanded mode, and the second uses droop control in both grid-connected and islanded modes. Analytical study is developed to compare the performance of these two strategies from various aspects, including fundamental differences, transition operation, power tracking, and P-Q capability with low point-of-common-coupling (PCC) voltages. Extensive simulation testing is carried out to validate the control performance of these two control strategies, and the simulation testing confirms the findings of the analytic study. Finally, the second strategy is recommended because of its superior control performance and ease of implementation. The analysis and results are useful in developing reliable control schemes for non-PLL GFM inverters because increasing numbers of inverters will work as non-PLL grid-forming sources in future grids because of their improved stability and reliability.

grid-forming↗
DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A phase model approach for thermostatically controlled load demand response

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Identification of a Delay Attack in the Secondary Control of Grid-Tied Inverter Systems

This work is developed for the identification of a denial-of-service cyberattack on the secondary controller of inverter systems which is connected to the power grid. The identification is made through the dynamic response of the reactive power (Q) of the system under attack. By observing the dynamic characteristic of Q, it is possible to correlate the attack with the nominal response of the hierarchical controller. The article shows that the occurrence of the attack can be identified through a supervisory control that runs a model in parallel. The article argues that after an early identification of the attack, a local controller can take action to mitigate its effects on the system’s response.

Roig Greidanus, Mateo D.↗
DOE OSTI2011

Control Strategies for Distributed Energy Resources to Maximize the Use of Wind Power in Rural Microgrids

The focus of this paper is to design control strategies for distributed energy resources (DERs) to maximize the use of wind power in a rural microgrid. In such a system, it may be economical to harness wind power to reduce the consumption of fossil fuels for electricity production. In this work, we develop control strategies for DERs, including diesel generators, energy storage and demand response, to achieve high penetration of wind energy in a rural microgrid. Combinations of centralized (direct control) and decentralized (autonomous response) control strategies are investigated. Detailed dynamic models for a rural microgrid are built to conduct simulations. The system response to large disturbances and frequency regulation are tested. It is shown that optimal control coordination of DERs can be achieved to maintain system frequency while maximizing wind power usage and reducing the wear and tear on fossil fueled generators.

17 WIND ENERGY↗
DOE OSTI2015

Enabling Automated Dynamic Demand Response: From Theory to Practice

Demand response (DR) is a technique used in smart grids to shape customer load during peak hours. Automated DR offers utilities a fine grained control and a high degree of confidence in the outcome. However the impact on the customer's comfort means this technique is more suited for industrial and commercial settings than for residential homes. In this paper we propose a system for achieving automated controlled DR in a heterogeneous environment. We present some of the main issues arising in building such a system, including privacy, customer satisfiability, reliability, and fast decision turnaround, with emphasis on the solutions we proposed. Based on the lessons we learned from empirical results we describe an integrated automated system for controlled DR on the USC microgrid. Results show that while on a per building per event basis the accuracy of our prediction and customer selection techniques varies, it performs well on average when considering several events and buildings.

Frincu, Marc↗
DOE OSTI2018

Integrated Model, Batch and Domain Parallelism in Training Neural Networks

We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using minibatch stochastic gradient descent (SGD). Our goal is to find an efficient parallelization strategy for a fixed batch size using $P$ processes. Our method is inspired by the communication-avoiding algorithms in numerical linear algebra. We see $P$ processes as logically divided into a $P_r \times P_c$ grid where the $P_r$ dimension is implicitly responsible for model/domain parallelism and the $P_c$ dimension is implicitly responsible for batch parallelism. In practice, the integrated matrix-based parallel algorithm encapsulates these types of parallelism automatically. We analyze the communication complexity and analytically demonstrate that the lowest communication costs are often achieved neither with pure model nor with pure data parallelism. We also show how the domain parallel approach can help in extending the theoretical scaling limit of the typical batch parallel method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A Hybrid Demand Response Simulator Version 1.0

A hybrid demand response simulator is developed to test different control algorithms for centralized and distributed demand response (DR) programs in a small distribution power grid. The HDRS is designed to model a wide variety of DR services such as peak having, load shifting, arbitrage, spinning reserves, load following, regulation, emergency load shedding, etc. The HDRS does not model the dynamic behaviors of the loads, rather, it simulates the load scheduling and dispatch process. The load models include TCAs (water heaters, air conditioners, refrigerators, freezers, etc) and non-TCAs (lighting, washer, dishwasher, etc.) The ambient temperature changes, thermal resistance, capacitance, and the unit control logics can be modeled for TCA loads. The use patterns of the non-TCA can be modeled by probability of use and probabilistic durations. Some of the communication network characteristics, such as delays and errors, can also be modeled. Most importantly, because the simulator is modular and greatly simplified the thermal models for TCA loads, it is very easy and fast to be used to test and validate different control algorithms in a simulated environment.

Lu, Ning↗
DOE OSTI2014

RAVEN and Dynamic Probabilistic Risk Assessment: Software overview

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7 [], currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Unintentional Islanding Detection Performance with Mixed DER Types

Most inverters for use in distribution-connected distributed energy resource applications (distributed generation and energy storage) are tested and certified to detect and cease to energize unintentional islands on the electric grid. The requirements for the performance of islanding detection methods are specified in IEEE 1547-2018, and specified conditions for certification- type testing of islanding detection are defined in IEEE 1547.1. Such certification-type testing is designed to ensure a minimum level of confidence that these inverters will not island in field applications. However, individual inverter certification tests do not address interactions between dissimilar inverters or between inverter and synchronous machines that may occur in the field. This work investigates the performance of different inverter island detection methods for these two circumstances that are not addressed by the type testing: 1) combinations of different inverters using different types of islanding detection methods, and 2) combinations of inverters and synchronous generators. The analysis took into consideration voltage and frequency ride- through requirements as specified in IEEE 1547-2018, but did not consider grid support functionality such as voltage or frequency response. While the risk of islanding is low even in these cases, it is often difficult to deal with these scenarios in a simplified interconnection screening process. This type of analysis could provide a basis to establish a practical anti- islanding screening methodology for these complex scenarios, with the goal of reducing the number of required detailed studies. Eight generic Groups of islanding detection behavior are defined, and examples of each are used in the simulations. The results indicate that islanding detection methods lose effectiveness at significantly different rates as the composition of the distributed energy resources (DERs) varies, with some methods remaining highly effective over a wide range of conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data

This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," [1]. These files include electricity demand profiles for 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], [3], that produces realistic patterns of residential power consumption, validated using metered data, with a resolution of 10 minutes. Households vary in size and number of occupants and the profiles represent total electricity use, in watts. The files also include in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with the 200 households assuming both Level 1 (1920 W) and Level 2 (6600 W) residential charging infrastructure. The vehicle recharging profiles have been generated using the modeling proposed by Muratori et al. [4], that produces real-world recharging demand profiles, with a resolution of 10 minutes. [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni, "A highly resolved modeling technique to simulate residential power demand," Applied Energy, vol. 107, no. 0, pp. 465 - 473, 2013. [3] M. Muratori, V. Marano, R. Sioshansi, and G. Rizzoni, "Energy consumption of residential HVAC systems: a simple physically-based model," in 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA: IEEE, 22-26 July 2012. [4] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, vol. 58, no. 0, pp. 168-177, 2013.

2009 RECS↗
NASA NTRS1984

Dynamic response of shock waves in transonic diffuser and supersonic inlet - An analysis with the Navier-Stokes equations and adaptive grid

An existing method which solves the multi-dimensional ensemble-averaged compressible time-dependent Navier-Stokes equations in conjunction with mixing length turbulence model and shock capturing technique has been extended to include the shock-tracking adaptive grid systems. The numerical scheme for solving the governing equations is based on a linearized block implicit approach. The effects of grid-motion and grid-distribution on the calculated flow solutions have been studied in relative detail and this is carried out in the context of physically steady, shocked flows computed with non-stationary grids. Subsequently, the unsteady dynamics of the flows occurring in a supercritically operated transonic diffuser and a mixed compression supersonic inlet have been investigated with the adaptive grid systems by solving the Navier-Stokes equations.

Liu, N.-S.↗
NASA NTRS2001

Aeroelastic Stability Computations for Turbomachinery

This paper describes an aeroelastic analysis program for turbomachines. Unsteady Navier-Stokes equations are solved on dynamically deforming, body fitted, grid to obtain the aeroelastic characteristics. Blade structural response is modeled using a modal representation of the blade and the work-per-cycle method is used to evaluate the stability characteristics. Nonzero interblade phase angle is modeled using phase-lagged boundary conditions. Results obtained showed good correlation with existing experimental, analytical, and numerical results. Numerical analysis also showed that given the computational resources available today, engineering solutions with good accuracy are possible using higher fidelity analyses.

Srivastava, R.↗
DOE OSTI2014

RAVEN, a New Software for Dynamic Risk Analysis

RAVEN is a generic software driver to perform parametric and probabilistic analysis of code simulating complex systems. Initially developed to provide dynamic risk analysis capabilities to the RELAP-7 code [1] is currently being generalized with the addition of Application Programming Interfaces (APIs). These interfaces are used to extend RAVEN capabilities to any software as long as all the parameters that need to be perturbed are accessible by inputs files or directly via python interfaces. RAVEN is capable to investigate the system response probing the input space using Monte Carlo, grid strategies, or Latin Hyper Cube schemes, but its strength is its focus toward system feature discovery like limit surfaces separating regions of the input space leading to system failure using dynamic supervised learning techniques. The paper will present an overview of the software capabilities and their implementation schemes followed by same application examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Verification of the ideal magnetohydrodynamic response at rational surfaces in the VMEC code

The VMEC nonlinear ideal MHD equilibrium code [S. P. Hirshman and J. C. Whitson, Phys. Fluids 26, 3553 (1983)] is compared against analytic linear ideal MHD theory in a screw-pinch-like configuration. The focus of such analysis is to verify the ideal MHD response at magnetic surfaces which possess magnetic transform (ι) which is resonant with spectral values of the perturbed boundary harmonics. A large aspect ratio circular cross section zero-beta equilibrium is considered. This equilibrium possess a rational surface with safety factor q = 2 at a normalized flux value of 0.5. A small resonant boundary perturbation is introduced, exciting a response at the resonant rational surface. The code is found to capture the plasma response as predicted by a newly developed analytic theory that ensures the existence of nested flux surfaces by allowing for a jump in rotational transform (ι=1/q). The VMEC code satisfactorily reproduces these theoretical results without the necessity of an explicit transform discontinuity (Δι) at the rational surface. It is found that the response across the rational surfaces depends upon both radial grid resolution and local shear (dι/dΦ, where ι is the rotational transform and Φ the enclosed toroidal flux). Calculations of an implicit Δι suggest that it does not arise due to numerical artifacts (attributed to radial finite differences in VMEC) or existence conditions for flux surfaces as predicted by linear theory (minimum values of Δι). Scans of the rotational transform profile indicate that for experimentally relevant levels of transform shear the response becomes increasing localised. Furthermore, careful examination of a large experimental tokamak equilibrium, with applied resonant fields, indicates that this shielding response is present, suggesting the phenomena is not limited to this verification exercise.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Quantified Fast Frequency Response of Inverter-Based Resources for Bulk-Power System Study

Quantifying fast frequency response (FFR) of inverter-based resources (IBRs) is crucial for robust frequency regulation, managing resources, and improving system reliability in the power grids with high penetration levels of IBRs. This paper proposes a concept of frequency response scheme that its response is quantifiable and applicable to the generic IBR models. To achieve this, a stepwise-energy function is derived as a control module by considering operational constraints of IBRs. Then the module is implemented along with generic IBR models in electromagnetic transient (EMT) and electromechanical-based software. To quantify the FFR, the implemented module calculates available incremental active power for a specific time duration. This energy profile is being updated depending on IBR’s operating conditions and is provided into a power grid upon detecting a frequency event. Thus, due to this characteristic, this scheme can quantify the FFR and enable applicable IBRs to provide their available headroom within frequency arrest period. Thereby, this scheme can help estimate IBRs’ frequency response and is more effective in improving the initial frequency response than frequency-dependent control schemes: rate-of-change-of-frequency based and droop-based control schemes. The performance of the proposed scheme was tested under different IBR penetration levels using PSLF and PSCAD simulators.

Kim, Jinho↗