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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 217 records · Page 12
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DOE OSTI2021

Parameter Reduction of Composite Load Model Using Active Subspace Method

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model.

active subspace↗
DOE OSTI2022

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).

14 SOLAR ENERGY↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2022

Deep Reinforcement Learning for Distribution System Restoration Using Distributed Energy Resources and Tie-Switches

Distributed energy resources (DERs), such as solar PVs and energy storage, can be used to restore distribution system critical loads after the extreme weather events to increase grid resilience. However, coordinating multiple DERs together with tie-switches for multi-step restoration process under renewable uncertainty is challenging. This paper proposes a deep reinforcement learning to control discrete actions of switching on/off tie switches and DERs for critical load restoration. The restoration problem is first cast into the Markov decision process suitable for DRL. Then, the original soft actor critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions. Numerical comparison results with other stochastic optimization-based approaches on the modified IEEE 33-bus system show that the proposed method can achieve fast critical load restoration in the presence of substation power outage while maintaining system voltage limit throughout the restoration process.

active distribution systems↗
DOE OSTI2025

Coupled Thermo-Hydrological-Mechanical-Chemical Behavior of Anisotropic Granite for Geologic Disposal of High-Level Radioactive Waste: A Core-Scale Laboratory Investigation

The coupled thermo-hydrological-mechanical-chemical (THMC) behavior of rock within an Excavation Damaged Zone (EDZ) is critical for the safety and long-term performance of a geological repository for high-level radioactive wastes. While many laboratory experiments have been conducted to investigate EDZ rocks, the flow and deformation characteristics resulting from anisotropic rock textures and microcrack distribution under triaxial loading and elevated temperatures remain poorly understood. Particularly, cracks at various scales serve as fast paths for fluid flow and solute transport and present as focal points of mechanical weakness, which complicate the coupled THMC processes in anisotropic EDZ rocks and challenge modeling predictions. Here, in this study, a series of core-scale experiments was conducted on three granite samples under repository-relevant conditions. These rock samples were obtained from the Grimsel Underground Research Laboratory (URL), featured by anisotropic minerals (represented by bedding layers) and microcrack distributions and coarse cm-scale grain sizes. During the experiments, samples were subjected to an elevated temperature at 90 °C and different triaxial loading conditions either by radial (normal to bedding layers) or axial (parallel to bedding layers) compaction. Water was injected into the samples, and the rock permeability evolutions and effluent water chemistry were monitored closely. For intact samples, thermal expansion of minerals at 90 °C resulted in a large, 75% irreversible permeability reduction and rock strengthening under radial compaction, while thermal impact was limited to a 15% permeability reduction under axial compaction. In contrast, for a sample containing open cracks, the growth of fractures during the experiment resulted in an abrupt permeability increase and fast failure at 90 °C. The effluent water chemistry indicates much more considerable mineral dissolution from large shear sliding than that in rocks dominated by mechanical compaction. These results helped better understand the coupled THMC processes in anisotropic rocks containing cracks, evaluate the behaviors of EDZ rocks, and predict the long-term evolution of EDZ for the performance of the repositories.

Coupled THMC processes↗
DOE OSTI2025

High-Fidelity Analysis of EV Integration on Real Utility Feeders in Colorado

Residential electric vehicle (EV) charging has the potential to alter long-held assumptions on load characteristics impacting distribution grid planning, operations, and design standards. This study identifies analysis and control methods to increase the affordability of residential EV charging both for Xcel Energy and their customers. The project also provides solutions for more reliable grid interconnection that can support a reliable utility business model prepared for increasing EV charging load in the coming years. For this project, we referenced Level 2 alternating current (AC) onboard charging profiles for various vehicle models and high-fidelity charging data collected at the experimental setup established at the EV Research Infrastructure Laboratory at the National Renewable Energy Laboratory (NREL). Next, we developed EV adoption models for 2030 and 2040 for the Boulder and Aurora regions in Colorado. Moreover, we evaluated different smart charging control algorithms and compared their performance. We developed time-of-use (TOU)-based and grid-aware active EV charging control methods and integrated them within the study region to understand field impacts. Diving deeper, we selected 10 feeders in Boulder and Aurora for high-fidelity grid modeling down to the house level. We executed detailed grid analysis comparing the smart charge management (SCM) algorithms we developed. Finally, we created a novel tool, Electric Vehicle Infrastructure--Distribution System Integration Tool (EVI-DiST), to integrate all the approaches in a single software environment to provide easy integration, fast simulation, and detailed evaluation capability for utility engineers and other stakeholders.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

SolarPlus Optimizer: Integrated Control of Solar, Batteries, and Flexible Loads for Small Commercial Buildings

Building-level microgrids may be a key strategy to unlock the combined potential of flexible loads, renewable generation, and energy storage. However, few software options exist for integrated control of building loads and other distributed energy resources at this scale. The commercial software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice. The SolarPlus Optimizer (SPO) is an open-source building-level microgrid control platform that uses Model Predictive Control to optimize both building loads and behind-the-meter energy storage to reduce energy bills and increase demand flexibility. This paper evaluates the capabilities of SPO in a small commercial building in Northern California under multiple electricity tariffs and demand response scenarios. Comparing SPO operation with an emulated battery and baseline operation employing a commercial optimization service, SPO reduced electricity bills by an estimated 7.3% in summer, 3.2% in spring, and 3.7% in winter. In a “load shape” scenario meant to counter the “duck curve”, SPO achieved 71% fewer violations from the load signal than the baseline control method. During a three hour long load shed event, SPO reduced cooling and refrigeration load by 38%. This research shows significant potential to provide load flexibility for building-level microgrids for this type of control systems. Finally, the paper discusses the future direction of research on open-source control systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2022

SolarPlus-Optimizer v0.1

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 software package, 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.

Prakash, AnandKrishnan↗
DOE OSTI2020

Impacts of Experimentally Obtained Harmonic Spectrums of Residential Appliances on Distribution Feeder

Owing to the increased use of power electronic based appliances and energy-efficient home equipment such as modern lighting loads, the percentage of nonlinear loads has increased in the distribution system, which can impact system performance, loss, and stability. Thus a comprehensive knowledge of their power quality and harmonic analysis is essential to improve the load models, voltage stability assessment, determination of possible interactions at harmonic frequencies, protection planning, and the effect of system impedance. This paper focuses on harmonic load flow analysis for multiple residential load types to determine the expected impacts on an modeled distribution secondary. For this study, the household appliances include lighting, power electronic, resistive, and motor loads with a nominal supply voltage of 120 V single-phase or 240 V split phase at a nominal frequency of 60 Hz. The harmonic spectrums, as obtained from the experimental evaluation of real loads, are used to inform the harmonic load flow for a detailed secondary network, including the distribution service transformer extracted from a real distribution feeder's model. Additionally, the impact of voltage harmonics on a three-phase test load is presented with experimental results. The harmonic data of the transformer terminal voltage, as obtained from the former load flow study, are scaled to generate the source voltage for the three-phase configuration of the grid simulator to make the test setup very similar to a typical secondary design in the U.S.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2020

A Study of Imbalance Levels Attributed to Photovoltaic Penetration in Distribution Systems

Abstract— The number of renewable sources, namely photovoltaics (PV), continues to grow within distribution systems. How placements impact a system’s performance remains a concern within both the research and the utility communities. Beyond system node voltages and real power losses, this work looks to industry standard imbalance metrics and the changes experienced on these values with varied levels of PV. Specifically, four imbalance metrics are presented and studied for an actual 906-bus distribution system. Multiple loading conditions and photovoltaic placement schemes are explored to compare the changes seen on these metrics across a variety of scenarios experienced by the network.

14 SOLAR ENERGY↗
DOE OSTI2020

Comparison of Thermal Management Approaches for Integrated Traction Drives in Electric Vehicles

The continuous push to increase power densities of electric vehicle (EV) traction drive systems necessitates combining electric motor and power electronics into one unit. A single, compact traction drive unit with fewer interconnecting components also facilitates fast, automated assembly of electric vehicles, driving production costs down and enabling wider adoption of EVs. There are a number of challenges associated with the integration of power electronics with the electric machine, including thermal management of the combined traction drive system. However, one important benefit of integration from the thermal management system perspective is the potential for using a single fluid loop instead of two separate cooling systems for the electric machine and the power electronics/inverter. This paper reviews several integration approaches and, employing finite element analysis (FEA), compares thermal management solutions for the combined electric machine and power electronics systems. Namely, three different scenarios are modeled: (1) independent component (motor and power electronics) cooling, which is compared to the combined cooling system approach for (2) radially and (3) axially integrated power electronics modules into the motor enclosure. Temperature distributions for selected thermal loads and thermal resistances from the key heat-generating components to the cooling fluid are compared for each scenario.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Sun, Yangzesheng↗
DOE OSTI2020

Impacts of Experimentally Obtained Harmonic Spectrums of Residential Appliances on Distribution Feeder: Preprint

This paper focuses on the harmonic load flow study for a set of residential loads and the impacts of the harmonic contents on the distribution feeder. Owing to the increased use of the power electronic based appliances and energy efficient home equipment like the lighting loads, the percentage of non-linear loads has increased in the distribution system which can impact the system performance, loss, and stability. Thus a comprehensive knowledge of their power quality and harmonic analysis is essential to improve the load models, voltage stability assessment, determining possible interactions at harmonic frequencies, protection planning, and the effect of the system impedance. For this study, the household appliances include the lighting, power electronics, resistive, and motor loads with a nominal supply voltage of 120 V single-phase or 240 V split-phase at a nominal frequency of 60 Hz. The harmonic spectrum as obtained from the experimental data is used to solve the harmonic load flow for a detailed secondary network including the distribution transformer extracted from a real distribution feeder's model. Additionally, the impact of the voltage harmonic on a three phase test load is presented through experimental results. The harmonic data of the transformer terminal voltage as obtained from the former load flow study is scaled to generate the source voltage for the three phase configuration of the grid simulator to make the test setup very similar to a real scenario.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Forming Interphase Microgrids in Distribution Systems Using Cooperative Inverters

Interphase-microgrids formed by three islanded single-phase feeders in distribution systems are proposed in this paper. Loss of utility caused by natural and human-made disasters may isolate each residential subdivision from the utility. In this case, the household loads are supplied by distributed generation (DG) units which may not be enough to meet the load demand. This can lead to deviations in voltage and frequency, and the deviations may vary for each phase. The proposed solution is to form an interphase-microgrid by seamlessly interconnecting the islanded single-phase feeders. This interphase-microgrid can effectively balance the load demands and DG generation in distribution systems after losing the utility supply. This paper presents different case studies to demonstrate the viability of forming interphase-microgrids for residential distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Characterizing Output Bottlenecks of a Production Supercomputer: Analysis and Implications

This article studies the I/O write behaviors of the Titan supercomputer and its Lustre parallel file stores under production load. The results can inform the design, deployment, and configuration of file systems along with the design of I/O software in the application, operating system, and adaptive I/O libraries.We propose a statistical benchmarking methodology to measure write performance across I/O configurations, hardware settings, and system conditions. Moreover, we introduce two relative measures to quantify the write-performance behaviors of hardware components under production load. In addition to designing experiments and benchmarking on Titan, we verify the experimental results on one real application and one real application I/O kernel, XGC and HACC IO, respectively. These two are representative and widely used to address the typical I/O behaviors of applications.In summary, we find that Titan’s I/O system is variable across the machine at fine time scales. This variability has two major implications. First, stragglers lessen the benefit of coupled I/O parallelism (striping). Peak median output bandwidths are obtained with parallel writes to many independent files, with no striping or write sharing of files across clients (compute nodes). I/O parallelism is most effective when the application—or its I/O libraries—distributes the I/O load so that each target stores files for multiple clients and each client writes files on multiple targets in a balanced way with minimal contention. Second, our results suggest that the potential benefit of dynamic adaptation is limited. In particular, it is not fruitful to attempt to identify “good locations” in the machine or in the file system: component performance is driven by transient load conditions and past performance is not a useful predictor of future performance. For example, we do not observe diurnal load patterns that are predictable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Distributionally Robust Optimization Framework for Stochastic Assessment of Power System Flexibility in Economic Dispatch

Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximum range of net load uncertainty across all buses. In this paper, we consider the stochastic nature of the net load and introduce a distributionally robust optimization framework that assesses system flexibility stochastically, accommodating a minimal extent of system violations. We verify the proposed method by solving the flexibility of the economic dispatch problem on four distinct IEEE standard test systems. Compared to traditional deterministic flexibility evaluations, our approach consistently yields less conservative flexibility outcomes.

distributionally robust optimization↗