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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Small Hydropower Interconnections: State Interconnection Processes

Small hydropower projects with rated power output between 0 to 20 MW have been the predominant source of hydropower growth over the past decade in the United States (DOE 2016; Johnson et al. 2018). However, interconnection to electricity distribution and transmission grids is a persistent barrier. Interconnection of an electricity generating unit is overseen by the distribution or transmission owner, who use interconnection standards and requirements that vary by state. The differences between standards in standards may affect the final cost, timeline, and success of a small hydropower project. Small hydropower project developers across the United States have found interconnection procedures to be fraught with cost surprises and schedule overruns. System operators have struggled to understand impacts to overburdened or rapidly evolving transmission and distribution grids. The results of these shortcomings have been stranded costs and unrealized small hydropower potential. Though regulatory actions and policy recommendations at the state level have increased the situational awareness of interconnection challenges, the remote locations of small hydropower resources and the relatively small revenues associated with energy production through small hydropower facilities continue to make interconnection processes and requirements confusing and costly. Noting these challenges, the U.S. Department of Energy Water Power Technologies Office enlisted Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to investigate the small hydropower interconnection landscape across the United States. The second in a series, this paper investigates the interconnection process in each state in the U.S. to compare their attributes. Subsequent papers in the series will analyze these interconnection processes (“Small Hydropower Interconnections: Analysis of Interconnection Processes”) and present best practices (“Small Hydropower Interconnections: Best Practices”) that will help overcome barriers to future small hydropower development. The first paper in the series examined the state of small hydropower projects in the United States (“Small Hydropower Interconnections: Small Hydropower in the United States”) to understand the industry characteristics.

13 HYDRO ENERGY↗
DOE OSTI2022

Enumeration reduction algorithm for the characterization of multi-primary LED systems

Multi-primary tunable LED lighting systems can generate a vast number of spectral outputs, with the exact number depending on the resolution of the control signal and number of LED primaries. Computing all combinations to identify optimal spectral power distributions (SPDs) with a specified chromaticity would require a tremendous amount of time and computational power. Here, an enumeration reduction (ER) algorithm is described to reduce the computation time by defining a bounding pyramid in the three-dimensional tristimulus space that maps to the circumscribed square of the target region in the chromaticity diagram. This method enables computing only the necessary amount of tristimulus values (and resulting chromaticity coordinates) for SPDs that fall into a user-defined target chromaticity area, reducing computation time by avoiding the need to generate each combination and calculate its chromaticity coordinates. The results show that the proposed ER algorithm can greatly reduce the time – up to 922 times in a series of tests – to determine a set of metamers compared to a full enumeration computation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Coherency-Aware Learning Control of Inverter-Dominated Grids: A Distributed Risk-Constrained Approach

Here, this letter investigates the importance of integrating the coherency knowledge for designing controllers to dampen sustained oscillations in wide-area power networks with significant penetration of inverter-interfaced resources. Coherency is a fundamental property of power systems, where time-scale separation in frequency dynamics leads to clustered behavior among generators of different groups. Large-scale penetration of inverter-driven low inertia resources replacing conventional synchronous generators (SGs) can lead to perturbation in the coherent partitioning; hence, integrating such information is of utmost importance for oscillation control designs. We present the coherency-aware design of a distributed output feedback-based reinforcement learning method that additionally incorporates risk constraints to capture the uncertainties related to net-load fluctuations. The use of domain-aware coherency information has produced improved training and oscillation performance than the coherency-agnostic control design, hence proving to be effective in controller design. Finally, we validated the proposed method with numerical experiments on the benchmark IEEE 68-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Developing High PV Penetration Cases for Frequency Response Study of U.S. Western Interconnection

Recent large penetrations of solar photovoltaic (PV) generation and the inertial characteristics of inverter-based generation technologies have caught the attention of those in the electric power industry in the United States. This paper presents a systematic approach to developing test cases of high penetrations of PV for the Western Interconnection. First, to examine the accuracy of the base case model, the Western Electricity Coordinating Council (WECC) model is validated by using measurement data from synchronized phasor measurement units. Based on the 2022 Light Spring case, we developed four high PV penetration cases for the WECC system that are of interest to the industry: 5% PV+15 % wind, 25% PV+15% wind, 45% PV+15% wind, 65% PV+15% wind). Additionally, a method to project PV is proposed that is based on collected, realistic PV distribution information, including the current and future PV power plant locations and penetrations in the WECC system. Both the utility-scale PV plant and residential rooftop PV are included in this study.

14 SOLAR ENERGY↗
DOE OSTI2022

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2024

Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.

Baquedano-Aguilar, Mario D.↗
DOE OSTI2023

A Generative Model for Synthetic Electroluminescence Images

This work will develop modular, open-source model and analysis components including crack detection workflow and parameterization for quantitative inspection of large EL large datasets. These tools will allow users to quickly and accurately assess the extent and types of cracking in their modules. Measured statistical distributions of crack parameters, together with the imposed stress and electrical properties will be used to generate models to predict future crack behavior and power loss.

Pierce, Benjamin Garrett↗
DOE OSTI2021

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

An Embedded Rule-Based Diagnostic Expert System in Ada

Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

Jones, Robert E.↗
NASA NTRS1998

Stochastic feedback and the regulation of biological rhythms

We propose a general approach to the question of how biological rhythms spontaneously self-regulate, based on the concept of "stochastic feedback". We illustrate this approach by considering at a coarse-grained level the neuroautonomic regulation of the heart rate. The model generates complex dynamics and successfully acounts for key characteristics of cardiac variability, including the l/f power spectrum, the functional form and scaling of the distribution of variations, and correlations in the Fourier phases indicating nonlinear dynamics.

NASA Discipline Cardiopulmonary↗
DOE OSTI2019

Heuristic Dynamic Programming for Adaptive Virtual Synchronous Generators

In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Distributed Generation Valuation and Compensation

This white paper can help guide a state as it considers issues associated with distributed generation valuation and compensation. States may address a common set of questions and issues in the valuation process, but differences in market expectations, policy priorities, and regulations result in different responses. Key issues include: Context is important. Valuations and compensation strategies will vary based on goals and objectives they are being designed to achieve. Goals and objectives should be made clear up front and will drive the perspective used in performing valuations and how outcomes are applied. Utilities and stakeholders can have different interpretations of how value elements should be calculated. In some states, the objective of standardized calculators and methods is to reduce ambiguity and inconsistencies in how valuations are performed. Certain value elements are difficult or impossible to quantify and most efforts to establish workable value of solar or value of distributed energy resource tariffs are emerging and nascent. Assessing locational and temporal value of distributed generation and applying that in compensation schemes is a new and emerging field of study, being explored by a handful of research organizations and advanced states and utilities. The most advanced states, such as California, are using demonstration projects to test valuation and compensation methodologies or are applying valuation and compensation strategies to a subset of customer projects, such as for community solar projects (e.g., Oregon and New York) before rolling out programs to the full customer base. A variety of states are moving away from full net metering, in many cases substituting avoided cost rates (sometimes with an adder) in lieu of full retail rate compensation, instead of pursuing valuation of distributed energy resource approaches. For example, in Indiana a 25% adder is applied to average wholesale electricity prices and in Mississippi a 2.5 cents/kWh adder is applied to avoided cost rates. These adders appear to have been established through policy directives rather than a comprehensive cost of service valuations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2024

Ensuring Solution Uniqueness in Three-Phase Power System State Estimation

This paper is concerned with the issue of potential non-unique solutions in three-phase state estimation. Theory of observability analysis for positive sequence power system state estimation is based on certain assumptions that avoid possibility of multiple solutions. Also, it is shown that observability of a positive sequence network remains independent of the network parameters or the operating state. When extending single-phase observability analysis directly to the three-phase case, this paper considers the possibility of converging to multiple solutions, i.e. solution non-uniqueness, even for cases where state estimator successfully converges. The study illustrates via numerical examples the likelihood of converging to entirely different solutions for certain network parameters. It also examines how the operating state, particularly under unbalanced loading, leads to solution non-uniqueness. The paper then describes an alternative approach to ensure a unique solution in three-phase state estimation. This method aims to accurately and uniquely estimate the state of any unbalanced three-phase system, irrespective of load imbalance, network configuration, existence of synchronous generators or transformers.

Power System State Estimation, Three-Phase, Distri↗
DOE OSTI2018

Survivability of Autonomous Microgrid during Overload Events

Grid-forming sources are voltage sources that draw necessary currents to meet any load changes. A load step can cause part or all of these sources to become overloaded in a microgrid. This paper presents an overload mitigation controller that addresses the two overload issues in a microgrid by actively controlling the sources’ frequency. When part of the sources in a microgrid is overloaded, the controller autonomously transfers the extra load to other sources by rapidly reducing its frequency. The frequency difference between sources during transient results in a change of phase angle, which redistributes the power flow. When all sources in a microgrid are overloaded, each source keeps dropping the frequency. Therefore, under frequency load shedding can be used to trip the non-critical loads resulting in the survival of microgrid. The advantages of these concepts are that communications between sources are not needed during transient, and the robust voltage control is maintained. Lastly, simulation and field tests from CERTS/AEP microgrid test site verify that the control strategy is effective in both purely inverter-based microgrids and inverter & generator mixed microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Using Energy Storage to Support Puerto Rico's Transmission System

As part of the DOE's multi-laboratory effort to provide analysis and tools to support reconstruction and modernization of the Puerto Rico electric grid, Sandia National Laboratory was tasked with making recommendations for how to use energy storage to support the transmission system. Puerto Rico's electric grid is outdated and still recovering from the 2017 hurricane season, and targeted improvements are needed to restore reliability and to provide resilience for future extreme events. This report examined the most critical near-term issues with the transmission system: frequency regulation and response, and analyzed the impacts of incorporating energy storage systems of varying sizes with the goal of immediately minimizing load shedding while laying the foundation for future renewable energy integration. The analysis concluded that 240 MW/60 MWh of energy storage would stabilize system frequency sufficiently to avoid loss of load for rapid load changes or generation outages up to and including loss of the largest generation unit on the island.

24 POWER TRANSMISSION AND DISTRIBUTION↗