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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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Numerical Simulations of A Conceptual MSR-EES Shoulder Recession

The present study demonstrates our in-house material response solver, Icarus's capability to simulate the ablative conditions, including pyrolysis effects due to the interaction between hypersonic boundary layers and the thermal protection system (TPS). A conceptual aeroshell shoulder design, which undergoes mission-relevant flow and material conditions, is selected for the demonstration purpose. LAURA, a structured flow solver, is used to solve flow around the shoulder at several trajectory points of a flight path. The aerothermal dataset obtained from LAURA is used to enforce boundary conditions on the aeroshell wall to simulate ablation processes. The two-layered material system is stacked with HEEET (TPS) and Aluminum (actual material). A set of base parameters, such as the angle of material orientation with respect to the flow direction, convective heat-transfer co-efficient, and aerothermal boundary conditions on the rear end of the HEEET material layer, is selected to conduct a base-case simulation. A small amount of recession was observed, indicating that the design should be fine to go through the selected flow and material conditions. Furthermore, the parameters mentioned above are individually altered and are observed to affect the shoulder design's recession compared to the base case. Verification of our Icarus setup was also carried out using a one-dimensional grid and FIAT, a one-dimensional material-response solver, to build credibility for our results. As the current work is an uncoupled fluid-material-response simulation, a local sharp mesh deformation in the recessed surface near the shoulder corner is observed.

Prakash Shrestha↗
DOE OSTI2014

SU-E-J-270: Study of PET Response to HDR Brachytherapy of Rectal Cancer

Purpose: Dose-response studies in radiation therapy are typically using single response values for tumors across ensembles of tumors. Using the high dose rate (HDR) treatment plan dose grid and pre- and post-therapy FDG-PET images, we look for correlations between voxelized dose and FDG uptake response in individual tumors. Methods: Fifteen patients were treated for localized rectal cancer using 192Ir HDR brachytherapy in conjunction with surgery. FDG-PET images were acquired before HDR therapy and 6–8 weeks after treatment (prior to surgery). Treatment planning was done on a commercial workstation and the dose grid was calculated. The two PETs and the treatment dose grid were registered to each other using non-rigid registration. The difference in PET SUV values before and after HDR was plotted versus absorbed radiation dose for each voxel. The voxels were then separated into bins for every 400 cGy of absorbed dose and the bin average values plotted similarly. Results: Individual voxel doses did not correlate with PET response; however, when group into tumor subregions corresponding to dose bins, eighty percent of the patients showed a significant positive correlation (R2 > 0) between PET uptake difference in the targeted region and the absorbed dose. Conclusion: By considering larger ensembles of voxels, such as organ average absorbed dose or the dose bins considered here, valuable information may be obtained. The dose-response correlations as measured by FDG-PET difference potentially underlines the importance of FDG-PET as a measure of response, as well as the value of voxelized information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Transactive Campus Energy Systems: Final Report

Transactive energy refers to the combination of economic and control techniques to improve grid reliability and efficiency. The fundamental purpose of transactive energy management is to seamlessly coordinate the operation of large numbers of new intelligent assets—such as distributed solar, energy storage and responsive building loads—to provide the flexibility needed to operate the power grid reliably and at minimum cost, particularly one filled with intermittent renewable generation such as the Pacific Northwest. It addresses the key challenge of providing smooth, stable, and predictable “control” of these assets, despite the fact that most are neither owned nor directly controlled by the power grid. The Clean Energy and Transactive Campus (CETC) work described in this report was done as part of a Cooperative Research and Development Agreement (CRADA) between the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) and the Washington State Department of Commerce (Commerce) through the Clean Energy Fund (CEF). The project team consisted of PNNL, the University of Washington (UW) and Washington State University (WSU), to connect the PNNL, UW, and WSU campuses to form a multi-campus testbed for transaction-based energy management—transactive—solutions. Building on the foundational transactive system established by the Pacific Northwest Smart Grid Demonstration (PNWSGD), the purpose of the project was to construct the testbed as both a regional flexibility resource and as a platform for research and development (R&D) on buildings/grid integration and information-based energy efficiency. This report provides a summary of the various tasks performed under the CRADA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Transactive Campus Energy Systems (Final Report)

Transactive energy refers to the combination of economic and control techniques to improve grid reliability and efficiency. The fundamental purpose of transactive energy management is to seamlessly coordinate the operation of large numbers of new intelligent assets—such as distributed solar, energy storage and responsive building loads—to provide the flexibility needed to operate the power grid reliably and at minimum cost, particularly one filled with intermittent renewable generation such as the Pacific Northwest. It addresses the key challenge of providing smooth, stable, and predictable “control” of these assets, despite the fact that most are neither owned nor directly controlled by the power grid. The Clean Energy and Transactive Campus (CETC) work described in this report was done as part of a Cooperative Research and Development Agreement (CRADA) between the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) and the Washington State Department of Commerce (Commerce) through the Clean Energy Fund (CEF). The project team consisted of PNNL, the University of Washington (UW) and Washington State University (WSU), to connect the PNNL, UW, and WSU campuses to form a multi-campus testbed for transaction-based energy management—transactive—solutions. Building on the foundational transactive system established by the Pacific Northwest Smart Grid Demonstration (PNWSGD), the purpose of the project was to construct the testbed as both a regional flexibility resource and as a platform for research and development (R&D) on buildings/grid integration and information-based energy efficiency. This report provides a summary of the various tasks performed under the CRADA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Developing a Framework for Valuation of Grid Services

The electric system is rapidly evolving and growing, raising new questions about how to define and value grid services delivered by a diverse set of resources, including customer-owned assets. This report advances a service–parameter–value framework that links what a grid service does to why it matters, and how its benefits are evidenced. We build upon prior efforts (Bender et al. 2021) and extend them to include value categories (reliability, resilience, cost & access, and security) with primary and secondary tiers, service-specific value streams that explain how benefits materialize, and metrics that make valuation traceable and comparable. We apply the framework to the six core grid services defined in (Kolln et al. 2023): Frequency Response, Regulation, Reserves, Energy, Voltage Management, and Blackstart and demonstrate it on three use cases to highlight the adaptability of these value streams. Further, we outline how the framework integrates with existing tools such as asset optimization, hosting capacity and cost-benefit analysis, and propose a path to portable qualifications and consistent stacking rules for use in tariffs and filings. Finally, we identify future pathways for research and application research needs to support standardization and decision-ready comparisons across services, assets, and architectures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗
DOE OSTI2014

Visual Analytics for Power Grid Contingency Analysis

Contingency analysis is the process of employing different measures to model scenarios, analyze them, and then derive the best response to remove the threats. This application paper focuses on a class of contingency analysis problems found in the power grid management system. A power grid is a geographically distributed interconnected transmission network that transmits and delivers electricity from generators to end users. The power grid contingency analysis problem is increasingly important because of both the growing size of the underlying raw data that need to be analyzed and the urgency to deliver working solutions in an aggressive timeframe. Failure to do so may bring significant financial, economic, and security impacts to all parties involved and the society at large. The paper presents a scalable visual analytics pipeline that transforms about 100 million contingency scenarios to a manageable size and form for grid operators to examine different scenarios and come up with preventive or mitigation strategies to address the problems in a predictive and timely manner. Great attention is given to the computational scalability, information scalability, visual scalability, and display scalability issues surrounding the data analytics pipeline. Most of the large-scale computation requirements of our work are conducted on a Cray XMT multi-threaded parallel computer. The paper demonstrates a number of examples using western North American power grid models and data.

contingency analysis↗
DOE OSTI2012

Evaluating utility owned electric energy storage systems: a perspective for state electric utility regulators

This report provides a perspective on issues pertaining to the deployment of utility owned electrical energy storage. The intended audience includes state electric utility regulatory authorities, their staffs and the planning personnel in the utilities they regulate. Its purpose is to inform the audience about the potential opportunities for energy storage technologies to play a greater role in the evolving electricity marketplace and grid. The surge of investments in renewable energy (RE) during the last decade, particularly wind and solar energy has stimulated interest in energy storage. These technologies have the capability to balance the variability inherent in many RE technologies. The state public utility commissions’ (PUC) responsibility for regulating utilities leads to a focus on aspects of grid operations and expansion including: voltage and frequency regulation; distributed generation; renewable energy, particularly the administration of Renewable Portfolio Standards (RPS) mandates; and grid capital investment. Energy storage systems can contribute in each of these areas. Given the potential of energy storage technologies to perform these functions, their access to the regulatory process must be improved together with removal of barriers and appropriate and consistent cost benefit analysis methodologies so that they are routinely included in the suite of options considered for providing key grid services. The solutions that deliver the services cost effectively will likely be the solutions put forth by utilities and approved by utility commissions. Two storage system case studies are presented as a means to illustrate some of the fundamental valuation principles particularly pertinent to energy storage systems.

Bhatnagar, Dhruv↗
DOE OSTI2020

Multi-Timescale Integrated Dynamic and Scheduling Model (MIDAS-Solar)

Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power system operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power systems steady-state and dynamic performance. This project helps meet and exceed the Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for electric grid planning and operation. This will be accomplished by developing temporally comprehensive, closed-loop simulation models that seamlessly simulate power systems operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). Both a multi-timescale grid model and an integrated PV model will be developed in this project to accurately study the impacts of PV variability and uncertainty on system reliability at multiple timescales. Using quasi-dynamic simulation methods and data-driven security assessment (DSA) criteria will allow the dynamic characteristics of PV to be fed forward into longer-timescale scheduling models for a complete understanding of the effect of short-term PV dynamics on bulk systems operations (e.g., reserve scheduling and deployment). Upon completion of the proposed model, this project will help operators accurately assess system reliability by deploying energy and reserve scheduling under critical contingency conditions and studying interactions among all types of essential reliability services provided by modern PV power plants.

14 SOLAR ENERGY↗
NASA NTRS2020

The GGCMI Phase 2 Experiment: Global Gridded Crop Model Simulations Under Uniform Changes in CO2, Temperature, Water, and Nitrogen Levels (Protocol Version 1.0)

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

GGCMI↗
DOE OSTI2021

Solar PV Curtailment in Changing Grid and Technological Contexts: Preprint

Solar photovoltaic (PV) systems generate electricity with no marginal costs or emissions. As a result, PV output is almost always prioritized over other fuel sources and delivered to the electric grid. At increasing levels of PV penetration situations arise where PV is curtailed, either because of local supply/demand imbalances or to maintain system flexibility. In 2018, we estimate that about 6.5 million MWh of PV output was curtailed in four key countries: Chile, China, Germany, and the United States. We find that PV curtailment peaks in the spring and fall, when PV output is relatively high but electricity demand is relatively low. Similar to the case of wind, some PV curtailment is attributable to limited transmission capacity connecting sparsely populated solar-heavy regions to load centers.Grid policies generally seek to minimize curtailment because it is viewed as an economic and environmental loss. However, we argue that changing grid and technological contexts warrant new thinking on PV curtailment. In the grid context, as grids integrate more PV and other renewable energy generation, seeking an optimal level of accepted curtailment becomes more efficient than preventing it. In the technological context, emerging technologies such as advanced inverters and low-cost battery storage are making PV systems more flexible. With flexible PV, grid operators can use withheld PV output to provide various non-generation grid services. This withheld PV output is a form of curtailment under prevailing definitions of the term. Hence, policies that aim to minimize curtailment may undercut the ability of grid operators to fully use the emerging capabilities of flexible PV systems. We argue that the changing grid and technological contexts require a re-examination of the curtailment paradigm. We argue that PV output that is withheld to provide grid services is fundamentally different from output that goes unused in response to system constraints. As a result, we propose a more exclusive definition of curtailment as unused PV output rather than the more expansive conventional definition as any reduction in system output from its technical potential. The terminological distinction is more than a question of semantics. Facilitating grid services by withholding PV output may increase the potential value of flexible PV systems to the grid. This shift in thinking may allow grid operators and policymakers to think in terms of PV curtailment management rather than minimization. Effective curtailment management may include policies that increase PV system dispatchability, alternative PV compensation schemes that decouple generator revenue from system output, and policies to increase grid flexibility.

Chile↗
DOE OSTI2012

Toward Real Time Data Analysis for Smart Grids

This paper describes the architecture and design of a novel system for supporting large-scale real-time data analysis for future power grid systems. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components of the grid. As a result, the whole system becomes highly dynamic and requires constant adjusting based on real time data. Even though millions of sensors such as phase measurement units (PMU) and smart meters are being widely deployed, a data layer that can analyze this amount of data in real time is needed. Unlike the data fabric in other cloud services, the data layer for smart grids has some unique design requirements. First, this layer must provide real time guarantees. Second, this layer must be scalable to allow a large number of applications to access the data from millions of sensors in real time. Third, reliability is critical and this layer must be able to continue to provide service in face of failures. Fourth, this layer must be secure. We address these challenges though a scalable system architecture that integrates the I/O and data processing capability in a devise set of devices. Data process operations can be placed anywhere from sensors, data storage devices, to control centers. We further employ compression to improve performance. We design a lightweight compression customized for power grid data. Our system can reduce end-to-end response time by reduce I/O overhead through compression and overlap compression operations with I/O. The initial prototype of our system was demonstrated with several use cases from PNNL’s FPGI and show that our system can provide real time guarantees to a diverse set of applications.

Data Analytics, Power Grid, Big Data↗
DOE OSTI2016

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2 ; 0.65), followed closely by GLEAM (0.68; 64 W m –2 ; 0.62), with values in parentheses representing the R 2 , RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2 ; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2 ; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.

58 GEOSCIENCES↗
DOE OSTI2019

Grid-Scale Battery Storage: Frequently Asked Questions

As costs continue to decline, jurisdictions are seeking to deploy increasing levels of utility-scale battery energy storage. This Greening the Grid document provides system planners and regulators with fundamental information about battery energy storage including which services these devices are capable of, how these devices interact with renewable energy and what barriers exist to their broader deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Modernizing Distribution System Restoration to Achieve Grid Resiliency Against Extreme Weather Events: An Integrated Solution

Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of the electric power grid. As the distribution grids still remain vulnerable to natural disasters, the power industry has focused on methods of restoring distribution systems after disasters in an effective and quick manner. The current distribution system restoration practice for utilities is mainly based on predetermined priorities and tends to be inefficient and suboptimal, and the lack of situational awareness after the hazard significantly delays the restoration process. As a result, customers may experience an extended blackout, which causes large economic loss. On the other hand, the emerging advanced devices and technologies enabled through grid modernization efforts have the potential to improve the distribution system restoration strategy. However, utilizing these resources to aid the utilities in better distribution system restoration decision-making in response to extreme weather events is a challenging task. Therefore, this paper proposes an integrated solution: a distribution system restoration decision support tool designed by leveraging resources developed for grid modernization. We first review the current distribution restoration practice and discuss why it is inadequate in response to extreme weather events. Then we describe how the grid modernization efforts could benefit distribution system restoration, and we propose an integrated solution in the form of a decision support tool to achieve the goal. The advantages of the solution include improving situational awareness of the system damage status and facilitating survivability for customers. The paper provides a comprehensive review of how the existing methodologies in the literature could be leveraged to achieve the key advantages. The benefits of the developed system restoration decision support tool include the optimal and efficient allocation of repair crews and resources, the expediting of the restoration process, and the reduction of outage durations for customers, in response to severe blackouts due to extreme weather hazards.

Automated feeder switch↗
DOE OSTI2017

Measuring the value of microgrids: a benefit–cost framework

This study examines the costs and benefits of microgrids under a variety of business models. Many factors complicate a utility-planning benefit–cost framework when evaluating microgrids. For the purposes of this paper, a ‘microgrid’ is a group of interconnected resources and loads sharing a (usually) single point of interconnection with a larger grid, and capable of balancing load and resources when disconnected from the grid, through dispatch of resources and demand control and/or response. Economically, a microgrid can be examined from a variety of perspectives, and the picture is more complex than for typical utility investments. It is Electric Power Research Institute’s intent to provide unbiased and independent analysis of technology applications, examining economic questions from the perspectives of electricity end-consumers and society as a whole. Among the first steps is determining the point of view the analysis is to assume and narrowing down the set of economic questions to be addressed. Since the nature and major purpose of a microgrid is presumably to provide its internal customers with enhanced reliability and resilience, it is important to ask the question of perspective: For whom is the microgrid an economical choice, and compared with what?

Roark, Jeffrey↗
DOE OSTI2015

RAVEN User Manual

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 (INL). 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 includes providing Application Programming Interfaces (APIs). These APIs 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 of investigating the system response, and 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 development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗