Search NASA⌕ Search

SEARCH · Search NASA

Results for “grid responsive”

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.

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

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Thermal Energy Storage and its Potential Applications in Solar Thermal Power Plants and Electricity Storage

With the rapid growth of renewable power generation, economically storing large quantities of solar- and wind-generated electricity may become as important as renewable power itself. The combination of renewable power generation and energy storage can overcome the variability of renewable power generation alone and create the opportunity for renewable generation to provide base-load electricity.For peak power usage, the integration of renewable power and storage of excess electricity has several significant and positive impacts: expanding the renewable energy portion of total electricity generation, improving the peak-load response, and coordinating the electricity supply and demand over the grid. Several energy storage approaches exist, with mechanical, chemical, and electricalmethods either in use now or being developed. Comparing their efficiencies as well as their economic uses for different scales and applications helps determine the right technology for the right purpose. This paper will study the possibility of using thermal energy storage as a means for electricity storage, and compare it to other energy storage methods including batteries, flywheels, compressed air, and pumped hydropower.

14 SOLAR ENERGY↗
DOE OSTI2012

Development and testing of a photometric method to identify non-operating solar hot water systems in field settings

This report presents the results of experimental tests of a concept for using infrared (IR) photos to identify non-operational systems based on their glazing temperatures; operating systems have lower glazing temperatures than those in stagnation. In recent years thousands of new solar hot water (SHW) systems have been installed in some utility districts. As these numbers increase, concern is growing about the systems dependability because installation rebates are often based on the assumption that all of the SHW systems will perform flawlessly for a 20-year period. If SHW systems routinely fail prematurely, then the utilities will have overpaid for grid-energy reduction performance that is unrealized. Moreover, utilities are responsible for replacing energy for loads that failed SHW system were supplying. Thus, utilities are seeking data to quantify the reliability of SHW systems. The work described herein is intended to help meet this need. The details of the experiment are presented, including a description of the SHW collectors that were examined, the testbed that was used to control the system and record data, the IR camera that was employed, and the conditions in which testing was completed. The details of the associated analysis are presented, including direct examination of the video records of operational and stagnant collectors, as well as the development of a model to predict glazing temperatures and an analysis of temporal intermittency of the images, both of which are critical to properly adjusting the IR camera for optimal performance. Many IR images and a video are presented to show the contrast between operating and stagnant collectors. The major conclusion is that the technique has potential to be applied by using an aircraft fitted with an IR camera that can fly over an area with installed SHW systems, thus recording the images. Subsequent analysis of the images can determine the operational condition of the fielded collectors. Specific recommendations are presented relative to the application of the technique, including ways to mitigate and manage potential sources of error.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as 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. RAVEN has now increased in maturity enough for the Beta 1.0 release.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Supervisory Control of Loads and Energy Storage in Next-Generation Zero Energy Buildings

A zero energy building (ZEB)—also known as a net zero energy or zero net energy building—is a building that exports as much renewable energy as the total energy it imports from other sources on an annual basis (DOE 2015). Large-scale and commercially viable ZEBs are now in the marketplace, and they are expected to become a larger share of the commercial building footprint as government and private sector policies continue to promote the development of buildings that produce more on-site energy than they use. However, the load profiles of ZEBs are currently perceived by electric utilities to be unfavorable and unpredictable. As shown in Figure ES-1, ZEB load profiles can have abrupt changes in magnitude, at times switching rapidly between exporting and importing electricity. This is a challenge for utilities, which are responsible for constantly balancing electricity supply and demand across the grid. Addressing these concerns will require new strategies and tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

After Action Report: Idaho National Laboratory Continuity of Operations Program 2017 Exercise September 26, 2017

On September 26, 2017, the Battelle Energy Alliance, LLC (BEA) Continuity of Operations Program (COOP) conducted an annual exercise to demonstrate the COOP responder’s ability to maintain COOP’s primary mission essential function (PMEF) and essential supporting activities (ESAs) during a national loss of commercial power caused by a malware attack to the United States power grid. The continuity emergency management team (CEMT) and continuity emergency response group (CERG) responded to CERG notifications and worked through simulated challenges, using COOP’s 3-D scenario visualization tool (3DSVT), that challenged COOP’s PMEF, essential support activities (ESAs), and impacted mission critical personnel’s human capital. The PMEF and ESAs were successfully maintained as the CERG developed incident action plans through several phases of the scenario, addressing appropriate needs, concerns and strategic planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Photovoltaic Microinverter Testbed for Multiple Device Interoperability

IEEE Standard 1547-2003 conformance of several interconnected microinverters was performed by Sandia National Laboratories (SNL) to determine if there were emergent adverse behaviors of co-located aggregated distributed energy resources. Experiments demonstrated the certification tests could be expanded for multi-manufacturer microinverter interoperability. Evaluations determined the microinverters' response to abnormal conditions in voltage and frequency, interruption in grid service, and cumulative power quality. No issues were identified to be caused by the interconnection of multiple devices.

42 ENGINEERING↗
DOE OSTI2019

Requirements for Robust DERMS Control and Cyber Attack Scenarios

A Distribution Management System (DMS) is responsible for monitoring and maintaining the health of a distribution grid. Common DMS applications include fault location isolation and service restoration, on-line power flow (OLPF), volt/VAR optimization (VVO), and switch order management. These systems were developed primarily in the area of top-down, unidirectional power flow, with large centralized generating plants moving electricity through the transmission grid and into the distribution grid from and thence to consumers. The recent and ongoing addition of distributed energy resources (DER) to the grid, including rooftop and community solar and wind, means that power is now flowing into the distribution grid from the edge, which complicates the functioning of the DMS. To ameliorate this problem, DMS use distributed energy resource management systems (DERMS) to control DER such as solar inverters. DERMS comprise software management tools that allow management and near real-time control of an array of DER. DERMS control devices and grid services including smart inverters, capacitor banks, on-load tap changes, voltage regulators, and customer loads.

14 SOLAR ENERGY↗
DOE OSTI2021

Evidence of Completion of Milestone 4: Simulation Testbed Validated with Experimental Data

Milestone 4 is given in the SOPO as being due in quarter 5 (ending 9/11/2020) and is described thus: Milestone 4: Enhanced Simulation Testbed Validated with Experimental Data (UM, Mathieu). Simulation testbed validated with data obtained from experimental testbed, specifically, nonlinear load behaviors and communication network issues observed in the experimental testbed will be modeled in the simulation testbed. The simulation testbed should accurately capture TCL real and reactive power consumption (including during extreme events associated with nonlinear behaviors and communication network failures) to within 5% RMSE error with respect to data obtained from the experimental testbed. The Tasks comprising the work to achieve Milestone 4 are outlined here.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems. Final Report

In this project, Clarkson University, in collaboration with Southern Methodist University and University of Pittsburgh, conducted the research to model, design, and implement a sophisticated generation and transmission co-optimization planning decision tool, called Multi-stage and Multi-timescale robust Co-Optimization Planning (MMCOP). The MMCOP decision tool intends to facilitate generation and transmission co-optimization planning of emerging power systems, while mitigating risks and uncertainties in both short-term operation dynamics and long-term policy and technology changes. Long-term power system planning aims at optimizing asset utilization by investing in a proper mix of various generation technologies and transmission lines to supply the future load growth. In particular, the Clean Power Plan (CPP), which is designed to combat climate change and reduce carbon emissions by setting a national limit on carbon pollution from power plants, may dramatically change the landscape of the power industry by further promoting clean energy and phasing out emissions-intensive generation technologies. In addition, novel non-wire alternatives (e.g., demand response (DR), distributed generation (DG), energy efficiency (EE), and smart grid technologies) and the computational complexity for large-scale systems significantly complicate the system planning procedure even further. However, existing conventional planning approaches neglect short-term variability and uncertainty of renewable energy, hourly chronological operation details, and physical nonlinear characteristics of the alternating current transmission network. As a result, existing conventional planning approaches may not work properly, and power systems reliability could be in jeopardy. In observing the limitations of existing conventional planning approaches and addressing new challenges of emerging power systems, the main scope of this project to develop co-optimization planning models within a multi-stage and multi-timescale framework. In particular, random contingencies, key uncertainty factors, and AC power flows are included to derive expansion plans while considering both long-term reliability and short-term flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Cyber100 Compass User Guide

This document provides an overview of the Cyber100 Compass tool and serves as a guide to users interested in understanding the cybersecurity risks facing their clean energy transition. The National Renewable Energy Laboratory (NREL) developed the Cyber100 Compass tool for cyber risk assessment at the system-of-systems level for system planners trying to reach high levels of renewables. Two offices of the U.S. Department of Energy (DOE) - the Office of Electricity and the Office of Cybersecurity, Energy Security, and Emergency Response-funded NREL to develop this framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. Cyber100 Compass can help systems planners apply the principle of security-by-design at scale. Investing in an upfront understanding of system-of-systems (where the constituent systems are operating entities of the different renewable resources) risks from high-renewable grids will result in a more resilient grid at a lower long-term cost.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Protection of 100% Inverter-dominated Power Systems with Grid-Forming Inverters and Protection Relays – Gap Analysis and Expert Interviews

This report summarizes a gap analysis resulting from a literature review and expert interviews conducted by subject matter experts from Sandia National Laboratory, Siemens, and the Electric Power Research Institute (EPRI) in Spring 2023. The gap analysis consists of two main parts: The fault-ride through (FRT) behavior of grid-forming (GFM) inverter-based resources (IBR) and the response of state-of-the-art protection relays to the fault currents and voltages from GFM IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Planning and Operations in Electricity Markets Under System Transformation

United States electricity markets, planning mechanisms, and operational procedures are currently evolving in concert with three key trends. First, a range of new resources—solar, wind, energy storage, hybrid co-located storage, and distributed energy—are coming online and require new solutions to ensure they are efficiently integrated into existing systems. Second, consumers now face more opportunities to participate in markets by providing demand response and engaging in two-way interactions with the grid. Third, there is an increasing need for enhanced coordination between generation and transmission planning as well as across transmission and distribution systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Evidence of Completion of Milestone 3: Experimental Testbed

Construction and deployment of the experimental testbed encountered scheduling delays, and completion was subsequently further delayed by curtailment of LANL operations due to the COVID-19 pandemic. Between March and October, LANL was in a state of “Limited Operations” such that only certain “mission-critical” work was performed with approximately 25% on-site staffing. In spite of this, much of the lab space preparation was executed and the five initial model house” units were constructed and installed during this period. Still, the pace of construction was impacted by difficulty in coordinating personnel and minimizing contact between workers. Currently, LANL is in the mode of “Normal Operations with Maximum Telework,” which continues to limit the availability of on-site personnel. Nevertheless, Milestone 3 is now complete. This document describes the experiment as it is deployed and provides status for each task for this milestone.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Evidence of Completion of Milestone 4: Simulation Testbed Validated with Experimental Data

Milestone 4 is given in the SOPO as being due in quarter 5 (ending 9/11/2020) and is described thus: Milestone 4: Enhanced Simulation Testbed Validated with Experimental Data (UM, Mathieu) Simulation testbed validated with data obtained from experimental testbed, specifically, nonlinear load behaviors and communication network issues observed in the experimental testbed will be modeled in the simulation testbed. The simulation testbed should accurately capture TCL real and reactive power consumption (including during extreme events associated with nonlinear behaviors and communication network failures) to within 5% RMSE error with respect to data obtained from the experimental testbed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Controller Requirements for Managing Community Microgrids

Microgrid control systems are central to the functioning of a microgrid. They are responsible for managing distributed energy resources (DER) in both grid-connected and islanded modes of operation. Although microgrids are being developed and deployed by many utilities, several questions are being raised about the control system that manages them. This document provides the detailed list of requirements for a feeder-level microgrid controller to manage various islanded and grid-connected use cases. Functionally, the use cases include control scenarios that require the controller to maintain energy balance, prevent constraint violations, manage switching equipment, manage grid-forming (GFM) and grid-following (GFL) DER, manage critical/non-critical loads, and coordinate with peer systems to operate joined island areas. In addition to detailed requirements, the document includes interface and performance metrics for functional evaluation. These requirements can be applied to utility-managed microgrid controllers that exclusively manage utility-owned equipment; requirements also apply to third-party managed microgrid controllers that coordinate with the utility- and customer-owned equipment. The document can also be used by technology developers and project developers in industry to specify desired control strategies and performance characteristics for community microgrid controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Equipment Self-Assessment Guide Checklist

This Equipment Self-Assessment Checklist is designed for asset owners and operators (AOOs) responsible for the deployment, operation, maintenance, or cybersecurity oversight of grid systems and digital energy technologies. It provides a structured inspection checklist for evaluating the security, integrity, and operational trustworthiness of equipment across substations, generation sites, distributed energy resources (DERs), and control environments.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2020

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Fldgen v1.0: an emulator with internal variability and space–time correlation for Earth system models

Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We introduce the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.

58 GEOSCIENCES↗