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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

14 SOLAR ENERGY↗
DOE OSTI2021

Commercial PV Inverter IEEE 1547.1 Ride-Through Assessments Using an Automated PHIL Test Platform

As more countries seek solutions to their de-carbonization targets using renewable energy (RE) technologies, interconnection standards and national grid codes for distributed energy resources (DER) are being updated to support higher penetrations of RE and improve grid stability. Common grid-code revisions mandate DER devices, such as solar inverters and energy storage systems, ride-through (RT) voltage and frequency disturbances. This is necessary because as the percentage of generation from DER increases, there is a greater risk power system faults will cause many or all DER to trip, triggering a substantial load-generation imbalance and possible cascading blackout. This paper demonstrates for the first time a methodology to verify commercial DER devices are compliant to new voltage, frequency, and rate of change of frequency (ROCOF) RT requirements established in IEEE Std. 1547-2018. The methodology incorporates a software automation tool, called the SunSpec System Validation Platform (SVP), in combination with a hardware-in-the-loop (HIL) system to execute the IEEE Std. 1547.1-2020 RT test protocols. In this paper, the approach is validated with two commercial photovoltaic inverters, the test results are analyzed for compliance, and improvements to the test procedure are suggested.

14 SOLAR ENERGY↗
DOE OSTI2021

Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, not enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e. g., system observ-ability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Analysis of PV Fleet Performance in Western United States During August 2020 Extreme Heat Wave

Starting in June 2020 through the end of that year, the western and central United States experienced a widespread heat wave and drought, which resulted in US$5.44 billion (CPI-adjusted) worth of damages. The ongoing heat and drought produced record large wildfires across the region, responsible for an additional US$19.9 billion in damage. These natural disasters directly impacted the energy system in the western US, leading to power blackouts in August 2020. In this work, we analyze the NLR PV Fleets data set, focusing on the geographic area covered by the Western Coordinating Council (WECC). We find that over the 10-day period from August 14 through 24, the median PV system produced 12% lower energy than expected, with some locations experiencing total losses of around 30%. Analyzing the spatial-temporal structure of the data and supplementing with limited operational current and voltage data where available, we find that system underperformance was more strongly impacted by irradiance reduction from wildfire smoke rather than by the high heat itself.

14 SOLAR ENERGY↗
DOE OSTI2012

Markov Model of Accident Progression at Fukushima Daiichi

On March 11, 2011, a magnitude 9.0 earthquake followed by a tsunami caused loss of offsite power and disabled the emergency diesel generators, leading to a prolonged station blackout at the Fukushima Daiichi site. After successful reactor trip for all operating reactors, the inability to remove decay heat over an extended period led to boil-off of the water inventory and fuel uncovery in Units 1-3. A significant amount of metal-water reaction occurred, as evidenced by the quantities of hydrogen generated that led to hydrogen explosions in the auxiliary buildings of the Units 1 & 3, and in the de-fuelled Unit 4. Although it was assumed that extensive fuel damage, including fuel melting, slumping, and relocation was likely to have occurred in the core of the affected reactors, the status of the fuel, vessel, and drywell was uncertain. To understand the possible evolution of the accident conditions at Fukushima Daiichi, a Markov model of the likely state of one of the reactors was constructed and executed under different assumptions regarding system performance and reliability. The Markov approach was selected for several reasons: It is a probabilistic model that provides flexibility in scenario construction and incorporates time dependence of different model states. It also readily allows for sensitivity and uncertainty analyses of different failure and repair rates of cooling systems. While the analysis was motivated by a need to gain insight on the course of events for the damaged units at Fukushima Daiichi, the work reported here provides a more general analytical basis for studying and evaluating severe accident evolution over extended periods of time. This work was performed at the request of the U.S. Department of Energy to explore 'what-if' scenarios in the immediate aftermath of the accidents.

08 HYDROGEN↗
DOE OSTI2012

An efficient modeling method for thermal stratification simulation in a BWR suppression pool

The suppression pool in a BWR plant not only is the major heat sink within the containment system, but also provides major emergency cooling water for the reactor core. In several accident scenarios, such as LOCA and extended station blackout, thermal stratification tends to form in the pool after the initial rapid venting stage. Accurately predicting the pool stratification phenomenon is important because it affects the peak containment pressure; and the pool temperature distribution also affects the NPSHa (Available Net Positive Suction Head) and therefore the performance of the pump which draws cooling water back to the core. Current safety analysis codes use 0-D lumped parameter methods to calculate the energy and mass balance in the pool and therefore have large uncertainty in prediction of scenarios in which stratification and mixing are important. While 3-D CFD methods can be used to analyze realistic 3D configurations, these methods normally require very fine grid resolution to resolve thin substructures such as jets and wall boundaries, therefore long simulation time. For mixing in stably stratified large enclosures, the BMIX++ code has been developed to implement a highly efficient analysis method for stratification where the ambient fluid volume is represented by 1-D transient partial differential equations and substructures such as free or wall jets are modeled with 1-D integral models. This allows very large reductions in computational effort compared to 3-D CFD modeling. The POOLEX experiments at Finland, which was designed to study phenomena relevant to Nordic design BWR suppression pool including thermal stratification and mixing, are used for validation. GOTHIC lumped parameter models are used to obtain boundary conditions for BMIX++ code and CFD simulations. Comparison between the BMIX++, GOTHIC, and CFD calculations against the POOLEX experimental data is discussed in detail.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

A Green Prison: The Santa Rita Jail Campus Microgrid

A large microgrid project is nearing completion at Alameda County’s twenty-two-year-old 45 ha 4,000-inmate Santa Rita Jail, about 70 km east of San Francisco. Often described as a green prison, it has a considerable installed base of distributed energy resources (DER) including an eight-year old 1.2 MW PV array, a five-year old 1 MW fuel cell with heat recovery, and considerable efficiency investments. A current US$14 M expansion adds a 2 MW-4 MWh Li-ion battery, a static disconnect switch, and various controls upgrades. During grid blackouts, or when conditions favor it, the Jail can now disconnect from the grid and operate as an island, using the on-site resources described together with its back-up diesel generators. In other words, the Santa Rita Jail is a true microgrid, or μgrid, because it fills both requirements, i.e. it is a locally controlled system, and it can operate both grid connected and islanded. The battery’s electronics includes Consortium for Electric Reliability Technology (CERTS) Microgrid technology. This enables the battery to maintain energy balance using droops without need for a fast control system.

25 ENERGY STORAGE↗
DOE OSTI2013

Enabling Efficient, Responsive, and Resilient Buildings: Collaboration Between the United States and India

The United States and India have among the largest economies in the world, and they continue to work together to address current and future challenges in reliable electricity supply. The acceleration to efficient, grid-responsive, resilient buildings represents a key energy security objective for federal and state agencies in both countries. The weaknesses in the Indian grid system were manifest in 2012, in the country’s worst blackout, which jeopardized the lives of half of India’s 1.2 billion people. While both countries are investing significantly in power sector reform, India, by virtue of its colossal growth rate in commercial energy intensity and commercial floor space, is better placed than the United States to integrate and test state-of-art Smart Grid technologies in its future grid-responsive commercial buildings. This paper presents a roadmap of technical collaboration between the research organizations, and public-private stakeholders in both countries to accelerate the building-to-grid integration through pilot studies in India.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

High-Performance Computing for Real-Time Grid Analysis and Operation

Power grids worldwide are undergoing an unprecedented transition as a result of grid evolution meeting information revolution. The grid evolution is largely driven by the desire for green energy. Emerging grid technologies such as renewable generation, smart loads, plug-in hybrid vehicles, and distributed generation provide opportunities to generate energy from green sources and to manage energy use for better system efficiency. With utility companies actively deploying these technologies, a high level of penetration of these new technologies is expected in the next 5-10 years, bringing in a level of intermittency, uncertainties, and complexity that the grid did not see nor design for. On the other hand, the information infrastructure in the power grid is being revolutionized with large-scale deployment of sensors and meters in both the transmission and distribution networks. The future grid will have two-way flows of both electrons and information. The challenge is how to take advantage of the information revolution: pull the large amount of data in, process it in real time, and put information out to manage grid evolution. Without addressing this challenge, the opportunities in grid evolution will remain unfulfilled. This transition poses grand challenges in grid modeling, simulation, and information presentation. The computational complexity of underlying power grid modeling and simulation will significantly increase in the next decade due to an increased model size and a decreased time window allowed to compute model solutions. High-performance computing is essential to enable this transition. The essential technical barrier is to vastly increase the computational speed so operation response time can be reduced from minutes to seconds and sub-seconds. The speed at which key functions such as state estimation and contingency analysis are conducted (typically every 3-5 minutes) needs to be dramatically increased so that the analysis of contingencies is both comprehensive and real time. An even bigger challenge is how to incorporate dynamic information into real-time grid operation. Today’s online grid operation is based on a static grid model and can only provide a static snapshot of current system operation status, while dynamic analysis is conducted offline because of low computational efficiency. The offline analysis uses a worst-case scenario to determine transmission limits, resulting in under-utilization of grid assets. This conservative approach does not necessarily lead to reliability. Many times, actual power grid scenarios are not studied, and they will push the grid over the edge and resulting in outages and blackouts. This chapter addresses the HPC needs in power grid analysis and operations. Example applications such as state estimation and contingency analysis are given to demonstrate the value of HPC in power grid applications. Future research directions are suggested for high performance computing applications in power grids to improve the transparency, efficiency, and reliability of power grids.

Contingency Analysis↗
DOE OSTI2014

Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case

Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Probabilistic Analysis of Passive Safety System Reliability in Advanced Small Modular Reactors: Methodologies and Lessons Learned

Many advanced small modular reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended due to deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize with a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has been examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper describes the most promising options: mechanistic techniques, which share qualities with conventional probabilistic methods, and simulation-based techniques, which explicitly account for time-dependent processes. The primary intention of this paper is to describe the strengths and weaknesses of each methodology and highlight the lessons learned while applying the two techniques while providing high-level results. This includes the global benefits and deficiencies of the methods and practical problems encountered during the implementation of each technique.

Grabaskas, David↗
DOE OSTI2017

Wind Generation Participation in Power System Frequency Response: Preprint

The electrical frequency of an interconnected power system must be maintained close its nominal level at all times. Excessive under- and overfrequency excursions can lead to load shedding, instability, machine damage, and even blackouts. There is a rising concern in the electric power industry in recent years about the declining amount of inertia and primary frequency response (PFR) in many interconnections. This decline may continue due to increasing penetrations of inverter-coupled generation and the planned retirements of conventional thermal plants. Inverter-coupled variable wind generation is capable of contributing to PFR and inertia with a response that is different from that of conventional generation. It is not yet entirely understood how such a response will affect the system at different wind power penetration levels. The modeling work presented in this paper evaluates the impact of wind generation's provision of these active power control strategies on a large, synchronous interconnection. All simulations were conducted on the U.S. Western Interconnection with different levels of instantaneous wind power penetrations (up to 80%). The ability of wind power plants to provide PFR - and a combination of synthetic inertial response and PFR - significantly improved the frequency response performance of the system.

17 WIND ENERGY↗
DOE OSTI2015

Results of a Demonstration Assessment of Passive System Reliability Utilizing the Reliability Method for Passive Systems (RMPS)

Advanced small modular reactor designs include many advantageous design features such as passively driven safety systems that are arguably more reliable and cost effective relative to conventional active systems. Despite their attractiveness, a reliability assessment of passive systems can be difficult using conventional reliability methods due to the nature of passive systems. Simple deviations in boundary conditions can induce functional failures in a passive system, and intermediate or unexpected operating modes can also occur. As part of an ongoing project, Argonne National Laboratory is investigating various methodologies to address passive system reliability. The Reliability Method for Passive Systems (RMPS), a systematic approach for examining reliability, is one technique chosen for this analysis. This methodology is combined with the Risk-Informed Safety Margin Characterization (RISMC) approach to assess the reliability of a passive system and the impact of its associated uncertainties. For this demonstration problem, an integrated plant model of an advanced small modular pool-type sodium fast reactor with a passive reactor cavity cooling system is subjected to a station blackout using RELAP5-3D. This paper discusses important aspects of the reliability assessment, including deployment of the methodology, the uncertainty identification and quantification process, and identification of key risk metrics.

Bucknor, Matthew↗
DOE OSTI2014

The Development of a Demonstration Passive System Reliability Assessment

In this paper, the details of the development of a demonstration problem to assess the reliability of a passive safety system are presented. An advanced small modular reactor (advSMR) design, which is a pool-type sodium fast reactor (SFR) coupled with a passive reactor cavity cooling system (RCCS) is described. The RELAP5-3D models of the advSMR and RCCS that will be used to simulate a long-term station blackout (SBO) accident scenario are presented. Proposed benchmarking techniques for both the reactor and the RCCS are discussed, which includes utilization of experimental results from the Natural convection Shutdown heat removal Test Facility (NSTF) at the Argonne National Laboratory. Details of how mechanistic methods, specifically the Reliability Method for Passive Systems (RMPS) approach, will be utilized to determine passive system reliability are presented. The results of this mechanistic analysis will ultimately be compared to results from dynamic methods in future work. This work is part of an ongoing project at Argonne to demonstrate methodologies for assessing passive system reliability.

Bucknor, Matthew↗
DOE OSTI2016

SELF-POWERED WIRELESS SENSOR NODE POWER MODELING BASED ON IEEE 802.11 COMMUNICATION PROTOCOL

Design and technical advancements in sensing, processing, and wireless communication capabilities of small, portable devices known as wireless sensor nodes (WSNs) have drawn extensive research attention and are vastly applied in science and engineering applications. The WSNs are typically powered by a chemical battery source that has a load dependent finite lifetime. Most applications, including the nuclear industry applications, require WSNs to operate for an extended period of time beginning with their deployment. To ensure longevity, it is important to develop self-powered WSNs. The benefit of self-powered WSNs goes far beyond the cost savings of removing the need for cable installation and maintenance. Self-powered WSNs will potentially offer significant expansion in remote monitoring of nuclear facilities, and provide important data on plant equipment and component status during normal operation, as well as in case of abnormal operation, station blackouts or post-accident evaluation. Advancements in power harvesting technologies enable electric energy generation from many sources, including kinetic, thermal, and radiated energy. For the ongoing research at Idaho National Laboratory, a solid-state thermoelectric-based technology, the thermoelectric generator (TEG), is used to convert thermal energy to power a WSN. The design and development of TEGs to power WSNs that would remain active for a long period of time requires comprehensive understanding of WSN operational. This motivates the research in modeling the lifetime, i.e., power consumption, of a WSN by taking into consideration various node and network level activities. A WSN must perform three essential tasks: sense events, perform quick local information processing of sensed events, and wirelessly exchange locally processed data with the base station or with other WSNs in the network. Each task has a power cost per unit tine and an additional cost when switching between tasks. There are number of other considerations that must also be taken into account when computing the power consumption associated with each task. The considerations includes: number of events occurring in a fixed active time period and the duration of each event, event-information processing time, total communication time, number of retransmission, etc. Additionally, at the network level the communication of information data packets between WSNs involves collisions, latency, andretransmission, which result in unanticipated power losses. This paper presents stochastic modeling of power demand for a schedule-driven WSN utilizing Institute of Electrical and Electronics Engineers, IEEE, 802.11 communication protocols. The model captures the generic operation of a schedule-driven WSN when an external event occurs, i.e., sensing, following by processing, and followed by communication. The results are verified via simulation.

IEEE 802.11 Protocol↗
DOE OSTI2015

Investigating Power System Primary and Secondary Reserve Interaction under High Wind Penetration Using Frequency Response Model

Power system frequency needs to be maintained close to its nominal value at all times to avoid machine damage, under-frequency load-shedding and even blackouts. Adequate primary frequency response and secondary frequency response are the primary forces to correct an energy imbalance at the second to minute level. As wind energy becomes a larger portion of the world's energy portfolio, there are greater oppotunities for wind to provide frequency response services. This paper addresses one area of frequency control that has been missing in previous work - the reliabilty impacts and interactions between primary and secondary frequency control. The lack of a commercially available tools to simulate the interaction of these two responses has limited the energy industry's understanding of when the depletion of primary control reserve will impact the performance of secondary conrol response or vice versa. To investigate this issue, in this paper we develop a multi-area frequency response integration model with combined primary and secondary frequency control capabilities.

17 WIND ENERGY↗
DOE OSTI2016

Preliminary Modeling of Accident Tolerant Fuel Concepts under Accident Conditions

The catastrophic events that occurred at the Fukushima-Daiichi nuclear power plant in 2011 have led to widespread interest in research of alternative fuels and claddings that are proposed to be accident tolerant. Thus, the United States Department of Energy through its NEAMS (Nuclear Energy Advanced Modeling and Simulation) program has funded an Accident Tolerant Fuel (ATF) High Impact Problem (HIP). The ATF HIP is funded for a three-year period. The purpose of the HIP is to perform research into two potential accident tolerant concepts and provide an in-depth report to the Advanced Fuels Campaign (AFC) describing the behavior of the concepts, both of which are being considered for inclusion in a lead test assembly scheduled for placement into a commercial reactor in 2022. The initial focus of the HIP is on uranium silicide fuel and iron-chromium-aluminum (FeCrAl) alloy cladding. Utilizing the expertise of three national laboratory participants (INL, LANL, and ANL) a comprehensive mulitscale approach to modeling is being used including atomistic modeling, molecular dynamics, rate theory, phase-field, and fuel performance simulations. In this paper, we present simulations of two proposed accident tolerant fuel systems: U3Si2 fuel with Zircaloy-4 cladding, and UO2 fuel with FeCrAl cladding. The simulations investigate the fuel performance response of the proposed ATF systems under Loss of Coolant and Station Blackout conditions using the BISON code. Sensitivity analyses are completed using Sandia National Laboratories’ DAKOTA software to determine which input parameters (e.g., fuel specific heat) have the greatest influence on the output metrics of interest (e.g., fuel centerline temperature). Early results indicate that each concept has significant advantages as well as areas of concern. Further work is required prior to formulating the proposition report for the Advanced Fuels Campaign.

36 MATERIALS SCIENCE↗
DOE OSTI2014

ANALYSIS OF SAFETY IMPACTS FROM EXTERNAL FLOODING USING THE RISK-INFORMED SAFETY MARGIN CHARACTERIZATION (RISMC) TOOLKIT

The existing fleet of U.S. nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This paper demonstrates how Idaho National Laboratory (INL) researchers use the RISMC Toolkit to investigate complex nuclear plant phenomena using RAVEN and RELAP-7. The analysis focused on a highly relevant topic currently facing some nuclear power plants – specifically flooding issues. This research and development looked at challenges to a hypothetical pressurized water reactor, including: (1) a potential loss of off-site power followed by the possible loss of all diesel generators (i.e., a station black-out event), (2) earthquake induced station-blackout, and (3) a potential earthquake induced tsunami flood. The analysis is performed by using a set of codes: a thermal-hydraulic code (RELAP-7), a flooding simulation tool (NEUTRINO) and a stochastic analysis tool (RAVEN) – these are currently under development at INL. Using RAVEN, we were able to perform multiple RELAP-7 simulation runs by changing specific parts of the model in order to reflect specific aspects of different scenarios, including both the failure and recovery of critical components. The simulation employed traditional statistical tools (such as Monte-Carlo sampling) and more advanced machine-learning based algorithms to perform uncertainty quantification in order to understand changes in system performance and limitations as a consequence of power uprate. Qualitative and quantitative results obtained gave a detailed picture of the issues associated with potential accident scenarios. These types of insights can provide useful material for decision makers to perform risk-informed margins management.

97 MATHEMATICS AND COMPUTING↗