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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data Center Power Systems: Architectures, Impact on Grid Reliability, Modeling Considerations, and Megawatt-Scale Hardware Testing [Slides]

This slide deck describes typical power systems of large datacenters along with reliability problems to bulk power systems from large-scale integration of datacenters. The slide deck covers the architecture of datacenter power systems, different power electronic converters used inside datacenters, their operation modes, and R&Dopportunities in maintaining grid stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Climate Impact on CO2 Capture Efficiency and Levelized Cost of Liquid solvent-based DAC (Direct Air Capture) System

The transition towards net-zero emission poses economic, technical, and political challenges for the commercial-scale deployment of CO2 removal technologies by 2050. The opportunities for large-scale deployment will largely depend on the distinctive conditions found in the different locations of the world such as energy cost, carbon intensity of energy source, construction, transportation, and weather conditions. In this work, we focus on one of the promising negative emission technology, DAC (Direct Air Capture), and its response to different weather conditions. This work presents two potential operation scenarios: (i) natural gas standalone, (ii) grid electricity connected DAC plant. For the first time, we investigated the influence of temperature and RH (relative humidity) of the air on liquid-based DAC system and its response to carbon capture efficiency and levelized cost of carbon capture with operational sensitivity analysis. It is observed that the overall energy demand decreases from 11.1 to 8.3 GJ/ton-CO2 as the CO2 capture rate increases from 40 to 85%. We observed that a CO2 capture rate of 75% is only possible above 17°C even at 90% RH and this drops tremendously at lower temperatures. It is also observed that water evaporation in the air contactor is highest at dry and low RH as expected. The sensitivity analysis showed that weather conditions are insensitive to CO2 capture efficiency for the liquid-solvent based DAC plant for both scenarios. In addition, higher CO2 capture efficiency is achievable from lower upstream methane emissions and carbon intensity of grid electricity (for grid-connected scenario). Lastly, the levelized cost of natural gas standalone scenario varies from 239.7 to 408.8 $/t-CO2 is sensitive to temperature conditions more than relative humidity and compared to electricity grid-connected scenario, from 265.3 to 440.1 $/t-CO2.

An, Keju↗
DOE OSTI2024

Impact of Hydrological Data on Power System Operational Studies: Preprint

Hydropower is expected to play an important role in maintaining grid reliability and flexibility as the share of of variable renewable energy increases. While the current hydropower operational models have been studied and used widely, they haven't been updated for decades to meet new performance standards. For example, current steady state and dynamic models often neglect hydrological conditions, which may lead to unrealistic expectations when relying on hydropower for energy and ancillary services. To study this impact, a multi-timescale hydrological model was created by leveraging the National Renewable Energy Laboratory-developed Multi-timescale Integrated Dynamics and Scheduling (MIDAS) tool. Using MIDAS, we compare the impact of considering hydrological conditions in a day-ahead unit commitment (DAUC) schedule on the reduced 240-bus Western Interconnect (WI) test system under winter and summer case studies. We show that neglecting current hydrological conditions of hydropower plants in power system models can lead to an overestimation of hydropower capabilities, which could lead to power balancing issues. For example, power system DAUC simulation results of our reduced test system show that in the case where hydrological conditions are not considered in the model, an approximate 31% overestimation of hydropower capabilities occurs in the summer case and approximately 60% occurs in the winter case compared to what is available. Additionally, results show an underestimation of WI day-ahead power system generation costs by approximately $54M - $80M in the weekly summer scenario and $116M - $126M in the weekly winter scenario. This analysis helps to underscore the importance of considering hydrological data in power system operational studies.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
NASA NTRS2014

Design and Expected Performance of GISMO-2, a Two Color Millimeter Camera for the IRAM 30 m Telescope

We present the main design features for the GISMO-2 bolometer camera, which we build for background-limited operation at the IRAM 30 m telescope on Pico Veleta, Spain. GISMO-2 will operate simultaneously in the 1 and 2 mm atmospherical windows. The 1 mm channel uses a 32 × 40 TES-based backshort under grid (BUG) bolometer array, the 2 mm channel operates with a 16 × 16 BUG array. The camera utilizes almost the entire full field of view provided by the telescope. The optical design of GISMO-2 was strongly influenced by our experience with the GISMO 2mm bolometer camera, which is successfully operating at the 30 m telescope. GISMO is accessible to the astronomical community through the regularIRAMcall for proposals.

Two-Color mm camera↗
DOE OSTI2021

Modeling and Evaluation of Cyber-Attacks on Grid-Interactive Efficient Buildings

Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.

Fu, Yanyang↗
DOE OSTI2022

1.2.2.405 HydroWIRES Topic D1: Capacity Expansion Model (CEM) Enhancements

Long-term grid planning tools have difficulty representing detailed hydropower operating characteristics, which depend not only on technological specifications but also on water management practices and regulations. As a result, the value of hydropower is incompletely characterized, and the potential role of hydropower in the performance and resiliency of the future electric grid is not fully understood. This work will fill that gap by developing new ways to represent hydropower resource, technology, and operational characteristics in electric sector capacity expansion models and implementing them in the open-source version of the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) model. ReEDS is a well-established national scale grid planning tool used since 2003 by the U.S. Department of Energy and others to explore the evolution of the U.S. electric sector. Improvements will include a comprehensive national resource assessment for pumped storage hydropower and methods for modeling multiple hydropower technology categories characterized by technical, regulatory, and economic characteristics. The project will provide guiding principles and strategies for improving hydropower modeling in capacity expansion models and deliver a first-of-its kind versatile PSH dataset. All data, code, and methods will be publicly available, allowing the industry to better identify the value of hydropower in the future electricity system and make more informed planning decisions.

capacity expansion↗
DOE OSTI2022

Chapter 3: Power Conversion Technologies: The Advent of Power-to-Gas, Power-to-Liquid, and Power-to-Heat

Decreasing levelized costs of renewable electricity coupled with expanding deployment are enabling a rapid reduction in the carbon intensity of the electrical grid. However, this transition is occurring more slowly in other sectors, including transportation and manufacturing. Power-to-X technologies provide a means to accelerate this transition in those sectors by converting renewable electricity into chemical energy and heat. In this chapter, we provide an overview of the existing and emerging Power-to-X technologies covering the production of methane (hydrogen is covered in Chapter 5) (i.e. Power-to-Gas), chemicals and fuels (e.g. ammonia, carbon monoxide, ethylene, methanol, ethanol, and formic acid), and heat (e.g. for industrial and building applications); discuss the overarching and technology-specific challenges and opportunities; and highlight the implications of deployment of Power-to-X technologies on power generation. We emphasize that Power-to-X technologies do exist today at various stages of development and commercialization and that a transition to lower carbon intensity for the transportation and manufacturing sectors is possible, but it requires immense growth of the electrical grid and appropriate systems design and operations.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Grid-responsive hydrogen production: Capital utilization and current density vs. efficiency in variable electricity markets

To achieve low-cost hydrogen production from water electrolyzers, grid tied electrolysis may need to operate dynamically to minimize the cost of supplying energy to the electrolyzer stack and produce hydrogen during low-cost hours and turn off/down during high-cost hours. Operating systems in this way can decrease capital utilization (capacity factor) and electricity costs. This strategy would shift the dominant cost drivers away from electricity (and thus efficiency) to the capital costs of the system, due to the underutilized capital when operating at low-capacity factors. Increasing the operational current density of the system could, in effect, reduce the capital cost of the system while producing hydrogen at a lower efficiency on a per unit energy basis. In the variable electricity cost profiles analyzed in this paper, increasing the current density for liquid alkaline from 0.5 A/cm2 to 1.5 Ac/m2 and proton exchange membrane electrolyzers from 2 A/cm2 to 4 A/cm2 resulted in substantial reductions in the levelized cost of hydrogen. Additionally, as capacity factors and electricity costs decrease, the optimal operating current density of the electrolyzer systems analyzed increases. These findings suggest R&D efforts should focus on increasing the operational current densities, reducing the turn down ratios, and understanding the durability implications of those strategies on low-temperature liquid alkaline and proton exchange membrane electrolyzers.

08 HYDROGEN↗
DOE OSTI2023

Developing Renewable Energy Applications for Water Treatment Technologies and Transmission

The need for smaller treatment facilities for more isolated or rural municipal entities and industrial water treatment facilities continues to grow. From environmental justice and energy needs in remote communities that have never been serviced by larger scale utilities to smaller onsite treatment requirements to meet discharge regulations, energy efficient treatment systems that can leverage renewable energy sources can be an important asset to owners. Climate change and population dynamics have changed the needs for distributed water treatment systems and the manner in which they function. New treatment technologies that can operate under dynamic conditions and leverage variable energy sources (e.g., renewable energy, RE) can provide substantial efficiencies and cost savings. The National Renewable Energy Laboratory (NREL) is embarking on research projects that will perform multi-scale studies to investigate treatment technologies on varying source water quality that can operate under unsteady state flow conditions (timing) in response to energy availability, supply, and demand. A pilot-scale system will be built to assess modularity and scaling of treatment system operating conditions and performance. Modeling will also be used to determine feasibility and integration into grid emulation platforms to understand the use and value of operation with RE. Optimization will be completed for the treatment and distribution systems to document strengths and weaknesses of using RE power sources. Results of this effort will advance dynamic water treatment understanding under variable conditions and integration of RE sources to power these systems. This will lead to further decarbonization of municipal and private treatment facilities while supporting a paradigm shift in water treatment process operation and system design to achieve net-zero goals and move toward energy-sensitive resilient water treatment systems.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Focus on monitoring and control of complex supply systems

The ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Controller Verification of a Smart-Grid Compatible 200 kHz Single-stage Photovoltaic Microinverter

This paper presents control system design, implementation, and experimental validation of a single-stage 400 W, 200 kHz solar photovoltaic (PV) microinverter using hardware-in-the-loop (HIL) and hardware testing. The selected circuit topology is based on a Gallium Nitride (GaN) direct-matrix based dual active bridge (DAB) converter with a low voltage active power decoupler (APD) circuit. Control performance is verified, smart-grid compatibility is tested, and circuit operation is confirmed. Controller HIL (CHIL) is shown to aid in a complex power electronics system design by 1) enabling detailed control development prior to hardware implementation, 2) expanding the use of automated testing, and 3) increasing confidence in control performance prior to prototype testing. Altogether, these factors make HIL a valuable tool in complex power electronic designs.

14 SOLAR ENERGY↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
DOE OSTI2022

Anomaly Detection and Mitigation in FACTS-based Wide-Area Voltage Control Systems using Machine Learning

With the increasing deployment of Flexible AC Transmission System (FACTS) devices in wide-area voltage control systems (WAVCS) for achieving improved voltage stability of bulk power systems, the possibility for cyber attacks on these systems is also increasing. Successful stealthy cyber attacks that are difficult to detect by traditional informational technology (IT)-based cybersecurity solutions or threshold-based bad data detectors can lead to a voltage collapse in power grid. This paper presents the testbed-based attacks implementation and real-time evaluation of machine learning (ML) algorithm for detecting and mitigating stealthy cyber attacks on FACTS-based WAVCS on a hardware-in-the-loop (HIL) testbed. Initially, we discuss the implementation of a fuzzy logic controller (FLC) that controls a Static VAR Compensator (SVC) device deployed in a two-area four-machine Kundur power system for improving transient voltage stability. Later, the ML-based Anomaly Detection and Mitigation (ADM) system is implemented on the cyber-physical HIL testbed to detect and mitigate various stealthy cyber attacks, which are injected in real-time over the wide-area network (WAN). The experimental results show accurate and effective performance of ADM system in detecting and mitigating anomalies while keeping the grid stable and within the system operating limits, as defined by the North America Electric Reliability Corporation (NERC).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Grid-Forming Inverters for Stability Improvements in Bulk Power Systems with High Inverter-Based Resources Penetration

The grid-forming inverter (GFM) has been considered recently as a means to enhance grid stability in areas with Inverter-based Resources (IBRs) concentration. This paper assesses the potential improvements the GFM can bring to Bulk-power Systems (BPS) through two distinct studies. The first study was conducted on West Texas in ERCOT with presence of more than 35GW of IBRs, the Grid-Following (GFL) batteries were replaced with GFM batteries. The results demonstrate that the GFM significantly improved the voltage profile and active power injection during and after a fault. The second study focused on a local area within ERCOT grid. In this case, a battery operating as GFL within the local study area was replaced with a GFM battery. The findings clearly illustrate that GFM can strengthen the area and allow a higher IBRs power generation and a higher power export from the local study area.

Quedan, Amro↗
DOE OSTI2021

Topology-Agnostic, Scalable, Self-Healing, and Cost-Aware Protection of Microgrids

Most microgrid protection schemes found in published literature suffer from a lack of generality in that they work well for the assumed topology, including type and placement of sources. Other generic protection schemes tend to be too complicated, too expensive, or both. To overcome these draw- backs, a topology-agnostic, scalable, and cost-aware protection based on fundamental principles that work in the presence of high penetration of inverter-based resources (IBRs) is developed and tested in this paper. Here, the protection system also implements stable automatic reconfiguration of the healthy sections of the system after clearance of fault, thus increasing resilience by self- healing. To achieve this ambitious goal, stable inverter models are developed that operate in unbalanced networks in grid-connected and islanded modes, even with 100% IBRs, share power without conflicting controls, and can ride through faults while limiting fault currents. The scheme is tested for primary and backup protection and reconfiguration on the IEEE 123-node feeder in grid-connected and islanded modes with 15 IBRs connected to the system.

42 ENGINEERING↗
DOE OSTI2022

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework that builds on the deep deterministic policy gradient from demonstrations to identify an optimal restoration strategy. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. In conclusion, the proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Du, Yan↗
DOE OSTI2021

DRACO: An Overview [Slides]

DRACO (Diffusion ReACtiOn) is a diffusion and chemistry code designed to: 1) Operate on 3D with an unstructured grid defining an arbitrary geometry of interacting parts. 2) Generate its own meshes and use meshes created by other software. 3)Model the transport of any number of diffusing quantities: Concentrations, pressures, temperature, etc. 4) Allow diffusion coefficients to depend in an arbitrary way on concentration, temperature, position, time, etc. 5) Model general chemistry between concentrations with arbitrary reaction rates. 6) Allow arbitrary initial conditions, boundary conditions, and sources/sinks. 7) Allow all of the above to be specified by the user.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗