Results for “Load Management” — Page 5

NASA NTRS2000

Dynamic Load-Balancing for Distributed Heterogeneous Computing of Parallel CFD Problems

The developed methodology is aimed at improving the efficiency of executing block-structured algorithms on parallel, distributed, heterogeneous computers. The basic approach of these algorithms is to divide the flow domain into many sub- domains called blocks, and solve the governing equations over these blocks. Dynamic load balancing problem is defined as the efficient distribution of the blocks among the available processors over a period of several hours of computations. In environments with computers of different architecture, operating systems, CPU speed, memory size, load, and network speed, balancing the loads and managing the communication between processors becomes crucial. Load balancing software tools for mutually dependent parallel processes have been created to efficiently utilize an advanced computation environment and algorithms. These tools are dynamic in nature because of the chances in the computer environment during execution time. More recently, these tools were extended to a second operating system: NT. In this paper, the problems associated with this application will be discussed. Also, the developed algorithms were combined with the load sharing capability of LSF to efficiently utilize workstation clusters for parallel computing. Finally, results will be presented on running a NASA based code ADPAC to demonstrate the developed tools for dynamic load balancing.

Ecer, A.↗

DOE OSTI2025

Adapting Grid Criticality for Data Centers

This presentation explores the evolving definition of “critical load” in the electric grid, emphasizing the growing importance of digital infrastructure—particularly data centers—in grid resilience, restoration, and modernization. As utilities increasingly rely on AI-driven analytics and software-defined control systems, data centers have shifted from passive electricity consumers to essential computational hubs that enable National Critical Functions (NCFs) and support real-time grid operations. The deck examines the scale and impact of digital loads, the need for grid modernization to manage rapid load growth, and the diverse computing paradigms required for AI deployment. It introduces a tiered taxonomy for classifying critical loads, highlights operational dependencies between the grid and digital infrastructure, and discusses policy implications for integrating data centers into emergency planning and restoration protocols. Through case studies and practical frameworks, the presentation provides actionable insights for utilities, regulators, and planners navigating the digital transformation of the power sector.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2024

A Simulation and Optimization Framework for Managing Wind-Driven Loading on PV Systems

As PV modules continue to trend toward larger, thinner, and more flexible forms they grow more susceptible to damage from dynamic wind loading. As a result, understanding the impact of wind on PV systems, particularly when mounted on solar-tracking hardware, and identifying robust, stable array layouts and stow strategies is becoming increasingly important for the PV community. We are developing an open-source software package, PVade (PV aerodynamic design engineering), to simulate the cascading fluid-structure interaction that occurs within solar-tracking arrays to enable researchers to test hardware, layout, and tracker control changes, leading to enhanced stability and a reduction in wind-driven damage. We will give an overview of the PVade software and present the latest outcomes from our ongoing validation campaign in which we compare statistical structural responses with field data. From there, we will present simulated results from a larger, multi-row array and highlight the effect of varying tracker angles on stability as measured by both acceleration and deformation, focusing on the stability differences between positive and negative tilt angles.

fluid structure interaction↗

DOE OSTI2024

Energy Efficiency Analysis and Grid Service Results for Commercial Buildings using the VOLTRON IoT Platform

In fiscal year 2021 (FY21), the Commercial Buildings Integration Program (CBI) within the Building Technologies Office (BTO), funded PNNL to conduct a field evaluation of the AIRCx and ILC using an Internet-of-Things (IoT) platform, VOLTTRON. The primary goal of the project is to show that software solutions deployed and delivered through an IoT-platform can identify energy efficiency opportunities and manage peak load (beyond the traditional demand response) in commercial buildings. There are also two secondary goals: 1) show that the energy efficiency solutions will result in identification of significant savings opportunities (10% to 30%) as well as energy cost reductions (10% to 15%) by managing the peak load in commercial buildings, and 2) show that an IoT-based software solution is more cost-effective compared to a manual process in meeting the Re-tuning/retro commissioning (RCx) mandates and there is a pathway for broader adoption of this approach. This report provides AIRCx and ILC results for two building located in District of Columbia managed by the energy service provider Intellimation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2002

Gilgamesh: A Multithreaded Processor-In-Memory Architecture for Petaflops Computing

Processor-in-Memory (PIM) architectures avoid the von Neumann bottleneck in conventional machines by integrating high-density DRAM and CMOS logic on the same chip. Parallel systems based on this new technology are expected to provide higher scalability, adaptability, robustness, fault tolerance and lower power consumption than current MPPs or commodity clusters. In this paper we describe the design of Gilgamesh, a PIM-based massively parallel architecture, and elements of its execution model. Gilgamesh extends existing PIM capabilities by incorporating advanced mechanisms for virtualizing tasks and data and providing adaptive resource management for load balancing and latency tolerance. The Gilgamesh execution model is based on macroservers, a middleware layer which supports object-based runtime management of data and threads allowing explicit and dynamic control of locality and load balancing. The paper concludes with a discussion of related research activities and an outlook to future work.

management locality load balance↗

DOE OSTI2026

Categorizing Plug Load Solutions by Ease of Implementation

Plug loads account for a growing share of U.S. commercial building electricity use, projected to rise from 16% today to 21% by 2050. Managing these loads presents a substantial opportunity for reducing energy costs while providing additional operational benefits, such as improved asset management and occupant comfort. Despite their potential, plug loads are numerous, diverse, and highly occupant-dependent, making control challenging. This publication organizes plug load control strategies by level of effort, offering building owners and operators a staged approach to implement interventions ranging from smart outlets and automatic receptacle controls to behavioral strategies. By following these actionable steps, building stakeholders can reduce energy consumption, lower utility bills, and realize broader operational advantages.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1993

An engineering code to analyze hypersonic thermal management systems

Thermal loads on current and future aircraft are increasing and as a result are stressing the energy collection, control, and dissipation capabilities of current thermal management systems and technology. The thermal loads for hypersonic vehicles will be no exception. In fact, with their projected high heat loads and fluxes, hypersonic vehicles are a prime example of systems that will require thermal management systems (TMS) that have been optimized and integrated with the entire vehicle to the maximum extent possible during the initial design stages. This will not only be to meet operational requirements, but also to fulfill weight and performance constraints in order for the vehicle to takeoff and complete its mission successfully. To meet this challenge, the TMS can no longer be two or more entirely independent systems, nor can thermal management be an after thought in the design process, the typical pervasive approach in the past. Instead, a TMS that was integrated throughout the entire vehicle and subsequently optimized will be required. To accomplish this, a method that iteratively optimizes the TMS throughout the vehicle will not only be highly desirable, but advantageous in order to reduce the manhours normally required to conduct the necessary tradeoff studies and comparisons. A thermal management engineering computer code that is under development and being managed at Wright Laboratory, Wright-Patterson AFB, is discussed. The primary goal of the code is to aid in the development of a hypersonic vehicle TMS that has been optimized and integrated on a total vehicle basis.

Vangriethuysen, Valerie J.↗

DOE OSTI2024

Factors Influencing Grid-connected Heat Pump Water Heater Performance in the Southeast U.S.

Grid-connected heat pump water heaters (HPWH) can shift electrical load while minimizing impacts to hot water availability for occupants. This capability provides a flexible grid resource to utilities seeking to manage peak loads Such load control also can feasibly improve renewable utilization within the utility electric production mix, for instance using off-peak generation during periods with high renewable energy generation. It also offers lower electric bills to customers through increased energy efficiency of HPWH and cuts greenhouse gas emissions. In particular, the Southeast U.S. due to its high penetration of electric water heating presents a promising opportunity for grid-connected HPWHs. This paper builds upon the results of an extensive HPWH load shifting field study conducted in 51 occupied homes in Florida using EcoPort technology (Butzbaugh et al, 2022). In 2022, only an initial evaluation was available. Here, long term load results are available as well as examination of various control strategies and influences. Analysis is conducted for HPWH energy use and load shifting performance based on home occupancy (i.e., low and high) and water heater location (i.e., conditioned and unconditioned) across different temperature profiles. An unexpected outcome of this analysis was the poor performance of HPWHs located in conditioned spaces, possibly because of inadequate air volume from improper installation. We did find higher demand reductions from 2-hour load ups and slightly improved for critical peak signals in the afternoon control periods. As expected, higher occupancy households showed great load reductions.

Fenaughty, Karen↗

DOE OSTI2024

Data Analytics and Visualization of Energy Systems for Critical Infrastructure Insights

Modernization of energy systems including transportation facilities provides opportunities for increased efficiency, expansion of commerce and meeting industry and federal goals. A significant increase in electrical demand is projected to meet these needs, which concentrates at facilities such as airports. For example, Xcel Energy working with two airports in their service area recently published information projecting an up to fivefold increase in electricity demand in the next 25 years [1]. Concurrently, the US Government Accountability Office (GAO) recently surveyed 30 commercial service airports identifying more than 300 outages of more than 5 minutes between 2015 and 2022 [2]. Power, reliability, and resilience planning becomes more important to safely maintain operations and the flow of commerce with fewer energy carriers providing necessary energy to safely move passengers and goods. NREL proposes to develop methodologies to allow owners, utilities, and federal agencies to dynamically analyze, forecast, and manage energy loads at airports, focused upon maintaining the flow of commerce in an efficient, sustainable, and resilient way. To address these energy challenges, a suite of technologies and methodologies can be leveraged to validate concepts, inform design, de-risk solutions and optimize energy management during deployment. These technologies include digitalization of energy systems, microgrid methodologies, and related energy technologies for building and vehicle loads. [1] Electrifying Airport Ecosystems - https://www.enterprisemobility.com/content/dam/enterpriseholdings/marketing/innovation-in-mobility/vehicle-innovation/airport-electrification-study-full-report-2024.pdf [2] Airport Infrastructure: Selected Airport's Efforts to Enhance Electrical Resilience https://www.gao.gov/products/gao-23-105203.

critcal infrastructure↗

DOE OSTI2024

Studying Wind Loading on CSP Collectors to Improve Performance and Reliability

Electricity generation through Concentrating Solar-thermal Power (CSP) adds the advantage of thermal energy storage and heat production. CSP collector costs constitute nearly one-third of total plant costs. Managing wind loading, especially dynamic wind loading caused by the turbulent wind flow, is a significant design challenge. Traditional collector designs rely on wind tunnel experiments and numerical simulations, which do not fully capture dynamic effects. Here, we provide an overview of NREL's activities on wind loading on CSP collectors, aiming to improve their reliability and cost-efficiency. We present insights from field measurements at operational parabolic trough and power-tower CSP plants. Over two years, we measured turbulent wind fields and resulting structural loads in a parabolic trough plant, revealing wind, turbulence, and collector field interactions. Similar measurements are ongoing at the Crescent Dunes heliostat field. These campaigns show how atmospheric turbulence, wind direction, and collector orientation affect dynamic wind loading. Upwind collector structures can generate turbulent structures, causing fluctuating loads at downstream collectors, impacting fatigue lifetime and optical efficiency. Preliminary results indicate that interior collectors experience higher turning moments than those at the edges, suggesting higher drive wear. At Crescent Dunes, we use instrumented heliostats at the field's edge and interior to study the translation of turbulent wind to dynamic structural loads. The campaign is still ongoing, and we will present first findings. Another focus of our work is tying the experimental wind loading findings to optical performance of the collectors, using simulation tools. For example, we derive torsional errors of the parabolic troughs - the angular offset from the sun position along a collector row. This error is impacted by wind-induced mirror deformations. Future work will focus on continuing the measurement efforts and combining them with computational fluid dynamic simulations. By providing detailed field measurements and validated models, our efforts aim to enhance understanding of wind loading on CSP collectors, improving their structural integrity and optical performance.

CSP↗

DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗

NASA NTRS1988

Automation of the space station core module power management and distribution system

Under the Advanced Development Program for Space Station, Marshall Space Flight Center has been developing advanced automation applications for the Power Management and Distribution (PMAD) system inside the Space Station modules for the past three years. The Space Station Module Power Management and Distribution System (SSM/PMAD) test bed features three artificial intelligence (AI) systems coupled with conventional automation software functioning in an autonomous or closed-loop fashion. The AI systems in the test bed include a baseline scheduler/dynamic rescheduler (LES), a load shedding management system (LPLMS), and a fault recovery and management expert system (FRAMES). This test bed will be part of the NASA Systems Autonomy Demonstration for 1990 featuring cooperating expert systems in various Space Station subsystem test beds. It is concluded that advanced automation technology involving AI approaches is sufficiently mature to begin applying the technology to current and planned spacecraft applications including the Space Station.

Weeks, David J.↗

DOE OSTI2025

Advanced Load Forecasting

This presentation presents information about electric utility load forecasting in the U.S. It provides an overview of load forecasting and describes the current state of the industry. Current load forecasting challenges, opportunities, and interests are presented, including feedback from a 2024 workshop on Integrated Distribution System Planning. The presentation also describes a variety of NREL tools and capabilities that support utility load forecasting efforts. This was presented as part of NREL's Utility Planning Resources for Energy Transition Webinar Series.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Smart Energy Storage Integration and Management Platform for Buildings (SESIMP-B)

This project involved the design, development, and testing of a commercial smart service panel. It includes an innovative AI-driven control method to manage smart functions of the panel and a technical-economic feasibility assessment for enhancing building energy resilience and grid-edge technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Effects of Communication Modality on Pilot-Controller Coordination during a Simulated m:N Operation

The last decade or so has seen growing interest in new control paradigms and concepts of operation for uncrewed aircraft systems (UAS) in which multiple aircraft are piloted remotely by a single or relatively small number of people. Referred to as “one-to-many” and “many-to- many” (alternatively, “multi-operator, multi-vehicle”)—and frequently expressed as the corresponding ratios, 1:N and m:N—such novel configurations of aircraft and the people who manage them are seen as critical to the path to future operations involving UAS. Examples of industry domains interested in these control paradigms are small package delivery services utilizing small UAS and passenger-carrying, short-range “Urban Air Mobility” (UAM) operations. Stakeholders in such operations have identified communication and coordination of flight activity with air traffic controllers (ATC) as a barrier to operations. In contrast to present-day flight operations, in which a pilot communicates with one ATC on one radio frequency for one aircraft, multi-vehicle operations potentially entail a significant increase in pilot task load for management of comms. New concepts, such as UAS Service Suppliers (USSs) and Providers of Services to UAM (PSUs), have been proposed to address the known bottleneck for Air Traffic Management (ATM) presented by multi-vehicle operations. While progress has been steadily made over years developing USSs and PSUs, it is generally expected that initial UAM operations will rely on traditional voice-over-radio communication with ATC for purposes of ATM. The current study was a human-in-the-loop simulation that had participants, each possessing a Private Pilot License, act as the ground-based pilot-in- command for multiple vehicles in a hypothetical UAM service in the San Francisco Bay Area. The experiment utilized a 2-by-3, within-subjects design in which the pilot’s Vehicle Load (4 vs. 12) and Comm System (Voice, Datalink, and a Hybrid) were manipulated. The task given to pilots was to use the Comm System to coordinate flight activity for all aircraft with appropriate controllers, having to obtain departure and arrival clearances at “vertiport” facilities and transition clearances for any intermediate airspaces along the route. Pilots were additionally responsible for compliance with vectoring instructions issued by ATC. Subjective workload questionnaires (NASA-TLX) were administered following each experimental trial. Screen recordings of the pilot’s Ground Control Station (GCS) and audio recordings of trials were subsequently coded to obtain performance metrics: response times and error rates. Presented in this paper are results related to pilot responses to vectoring instructions issued by ATC. Workload was found to be significantly higher in the 12-Vehicle condition compared to the 4-Vehicle condition, nearly maxing out the NASA-TLX overall workload scale. There was no significant difference made by the Comm System on workload ratings. Pilots’ response times to communications were fastest in the Voice condition, although overall “service time” for compliance was shorter in Datalink and Hybrid conditions in most cases. Errors by pilots were frequent in both Vehicle Load conditions, most perniciously when using the Voice system. The results of this study suggest tradeoffs in advantages and disadvantages of the three comm systems. Recommendations for communication system design are provided taking the tradeoffs into account.

Garrett G Sadler↗

NASA NTRSDate not supplied

Effects of Communication Modality on Pilot-Controller Coordination during a Simulated m:N Operation

The last decade or so has seen growing interest in new control paradigms and concepts of operation for uncrewed aircraft systems (UAS) in which multiple aircraft are piloted remotely by a single or relatively small number of people. Referred to as “one-to-many” and “many-to- many” (alternatively, “multi-operator, multi-vehicle”)—and frequently expressed as the corresponding ratios, 1:N and m:N—such novel configurations of aircraft and the people who manage them are seen as critical to the path to future operations involving UAS. Examples of industry domains interested in these control paradigms are small package delivery services utilizing small UAS and passenger-carrying, short-range “Urban Air Mobility” (UAM) operations. Stakeholders in such operations have identified communication and coordination of flight activity with air traffic controllers (ATC) as a barrier to operations. In contrast to present-day flight operations, in which a pilot communicates with one ATC on one radio frequency for one aircraft, multi-vehicle operations potentially entail a significant increase in pilot task load for management of comms. New concepts, such as UAS Service Suppliers (USSs) and Providers of Services to UAM (PSUs), have been proposed to address the known bottleneck for Air Traffic Management (ATM) presented by multi-vehicle operations. While progress has been steadily made over years developing USSs and PSUs, it is generally expected that initial UAM operations will rely on traditional voice-over-radio communication with ATC for purposes of ATM. The current study was a human-in-the-loop simulation that had participants, each possessing a Private Pilot License, act as the ground-based pilot-in- command for multiple vehicles in a hypothetical UAM service in the San Francisco Bay Area. The experiment utilized a 2-by-3, within-subjects design in which the pilot’s Vehicle Load (4 vs. 12) and Comm System (Voice, Datalink, and a Hybrid) were manipulated. The task given to pilots was to use the Comm System to coordinate flight activity for all aircraft with appropriate controllers, having to obtain departure and arrival clearances at “vertiport” facilities and transition clearances for any intermediate airspaces along the route. Pilots were additionally responsible for compliance with vectoring instructions issued by ATC. Subjective workload questionnaires (NASA-TLX) were administered following each experimental trial. Screen recordings of the pilot’s Ground Control Station (GCS) and audio recordings of trials were subsequently coded to obtain performance metrics: response times and error rates. Presented in this paper are results related to pilot responses to vectoring instructions issued by ATC. Workload was found to be significantly higher in the 12-Vehicle condition compared to the 4-Vehicle condition, nearly maxing out the NASA-TLX overall workload scale. There was no significant difference made by the Comm System on workload ratings. Pilots’ response times to communications were fastest in the Voice condition, although overall “service time” for compliance was shorter in Datalink and Hybrid conditions in most cases. Errors by pilots were frequent in both Vehicle Load conditions, most perniciously when using the Voice system. The results of this study suggest tradeoffs in advantages and disadvantages of the three comm systems. Recommendations for communication system design are provided taking the tradeoffs into account.

urban air mobility↗

DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

A Queuing Theory Approach to Pilot-Controller Coordination for m:N Operations

In recent years, attention and interest by industry and researchers has grown in a control paradigm for remotely piloted aircraft termed “m:N operations.” In an m:N operation, a team of m remote pilots in command (RIPCs) collaboratively manage the flights of N aircraft. A consequence of an m:N concept of operations is that the RPICs will have to switch attention from one aircraft to another and from one task to another. Previous research in m:N operations has focused on the workload experienced by an RPIC and their level of situation awareness on their flights. Researchers have found that RPIC workload and situation awareness are generally sensitive to increasing N, although NASA’s Multi-Vehicle (m:N) Working Group has suggested that the driver of workload/situation awareness is the number of exceptions requiring human intervention as opposed to the value of N itself. In any case, a natural antecedent of workload is task load. In this paper, queueing theory is applied to a 1:N Urban Air Mobility (UAM) air taxi operation in order to estimate pilot task load for managing radio communications with air traffic controllers (ATCs) under increasing N. An M/M/1 queueing system is used to model the RIPC’s servicing of calls and clearance requests (e.g., departure, arrival, or airspace transition) to ATC for the N aircraft. Important parameters for the queueing model are the task arrival rate and the average service time for task completion. Radio communication times from past human-in-the-loop simulation studies are used to measure service times for a 1:4 and 1:12 UAM operation and to interpolate service times for 4 < N < 12. A Monte Carlo method is then employed, using the measured and interpolated service times, to estimate arrival rate and related queueing statistics. The paper concludes by considering the estimated queuing statistics, particularly the RPIC’s utilization (i.e., proportion of time actively servicing tasks), the length of the task queue over time, and the implications for task-balanced system design.

task load↗