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DOE OSTI2021

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Field Validation of a Building Operating System Platform

The U.S General Services Administration's (GSA's) Green Proving Ground program, in partnership with the National Renewable Energy Laboratory. completed a large pilot study of an Energy Management Information Systems (EMIS) with Automated System Optimization (ASO). Four test bed facilities, each with different building characteristics and systems, were chosen for the implementation of cloud-based EMIS with ASO. Depending on functionality, this tool can be extremely effective in energy management and energy optimization in buildings. The capabilities evaluated in the pilot ranged from energy savings and energy consumption predictions to evaluations of user acceptance, operability, and ease of installation. This report presents the methodology, lessons learned and best practices, and deployment recommendations for the GSA's portfolio of commercial office space, comprising more than 8,500 properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Field Validation of a Grid-Interactive Efficient Building Software Solution

The U.S. General Services Administration's (GSA's) Green Proving Ground (GPG) program, in partnership with the National Laboratory of the Rockies (NLR), completed a field study of a Grid-Interactive Efficient Buildings (GEB) software solution. The study focused on a single testbed facility to test the GEB functionality of the software solution, along with other features. The testbed facility - a courthouse - is a common building type in GSA's vast building portfolio, offering potentially impactful findings on a scalable level. The study evaluated Prescriptive Data's technology, Nantum OS, a connected building operating system ("GEB Solution") which aggregates multiple sources of previously siloed building data and combines that data with external sources, such as weather information or utility signals, into a single integrated platform. A GEB Solution is a type of Energy Management Information System (EMIS). EMIS is defined as a system of devices, data services, and software applications that communicates with any building system or third-party data source to aggregate and transform data into new capabilities to aid in the optimization of energy use at the building, campus, or agency level. This specific GEB Solution is an EMIS with ASO, automated system optimization, offering supervisory control of certain aspects of the Building Automation System (BAS). Multiple features were evaluated including, but not limited to, Continuous Demand Management to avoid setting new monthly kilowatt (kW) peaks, energy efficiency for reduction of kilowatt hours (kWh) and natural gas consumption, and automated demand response (ADR) for purposes of lowering demand during a utility called Demand Response (DR) event. The testbed facility was the Foley Federal Building and US Courthouse ("Foley Federal Building") located in Las Vegas, NV. This is a 209,496 sq. ft. building constructed in the 1960s with major renovations in 2004. The facility was a good candidate due to the large prevalence of office and courthouse spaces in the GSA portfolio of buildings. It also has many features which allow integration into and control of the building and a strong facilities team to assist with the study. Quantitative and qualitative performance objectives were developed using GSA's GPG GEB project template along with input from the vendor and building facility staff; these are outlined in Table 1. The quantitative performance objectives focused on continuous demand management, energy efficiency, and automated demand response. The qualitative performance objectives focused on the ease of installation and commissioning as well as the operability of the GEB solution. Other performance metrics that are reported on include carbon reduction, cost effectiveness, and occupant acceptance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as p2r, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these miniapps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as \ptor, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these mini-apps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

Atif, Mohammad [Brookhaven] (ORCID:000000026889770↗

DOE OSTI2022

IACMI Project 4.10: Blade Finishing Automation (Final Report)

IACMI Project 4.10, the Innovative Wind Turbine Blade Finishing with Advanced Automated Technology project, performed research addressing the challenges and opportunities associated with automating wind turbine blade finishing operations, including blade trimming and grinding. These activities, developed and conducted in close cooperation with wind industry partners, included: • The development of new automation technology and systems; • The integration of automation technology and components; • The improvements of sensor technology and their deployment in automation systems • The elimination of extensive manual programing for the automation of wind blade finishing; • The rethinking of the approach to specific work tasks for automation systems; • The demonstration at scale of innovative wind turbine blade finishing automations systems. The IACMI Wind TA team worked with GE Renewable Energy and LM Wind Power to establish the metrics of the automated blade finishing system developed in the project. In addition, the project team helped to integrate the advanced automated systems into wind turbine blade production facilities. This project was divided into two phases. Phase I provided project management, preliminary techno-economic model development, early US wind turbine industry outreach, and early research into robotic solutions for automated wind blade finishing. This second phase continued with project management, as well as final techno-economic model development, advanced research into robotic solutions for automated wind blade finishing, as well as specifications and procurement of a robotic system for automated wind blade finishing. The 4.10 project leveraged the capabilities and facilities established through the Institute for Advanced Composites Manufacturing Innovation (IACMI), the Colorado Office of Economic Development and International Trade (OEDIT) and the National Renewable Energy Laboratory (NREL). The IACMI Wind Technology Area (TA) designed and prepared for deployment a robotic automated wind turbine blade finishing system at the NREL/IACMI Composites Manufacturing Education and Technology (CoMET) facility. The results of this project led to follow-on funding to build upon and expand research in automated wind turbine blade finishing.

17 WIND ENERGY↗

DOE OSTI2025

Critical Energy Infrastructure Cybersecurity: Enhanced Cyber Resilience for Federal Energy Systems

This presentation is an overview of FEMP Resilient and Secure Infrastructure and Facilities. An educational and interactive workshop centered on resilient and secure federal infrastructure and facilities, with a focus on inverter-based resources at Federal sites, building automation systems, and Federal supply chains. This workshop will illustrate an all-hazards scenario and discuss how Federal agencies can be positioned to resist these real-world scenarios.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are thus a key element of many beamlines at X-ray facilities, used to either to focus a beam or image a sample. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam; aligning CRLs is thus typically treated as a time-consuming manual process. Many groups circumvent day-to-day realignment by using engineering controls (e.g. mounting optics) that sacrifice some of the beam’s focusing characteristics, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. Our approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder-Mead (SNM) simplex method. We demonstrate the efficacy of this method by numerical simulation and then extend this test to real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron X-ray beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Proving the Business Case for Building Analytics

As building monitoring becomes more common, facilities teams are faced with an overwhelming amount of data. These data do not typically lead to insights or corrective actions unless they are stored, organized, analyzed, and prioritized in automated ways. Buildings are full of energy savings potential that can be uncovered with the right analysis. With analytic software applied to everyday building operations, owners are using data to their advantage and realizing cost savings through improved energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Evolving Ground Mobility at Airports

Airports not only serve air traffic, but are leading regional ground mobility hubs. Emerging mobility technologies often first appear in implementation at airports, where technologies undergo refinement and improvement before broader application in other settings. With a goal of better understanding the mobility landscape and related research needs through the perspective of airport industry practitioners and leaders, the NREL team facilitated brainstorming and conversation to identify trends and critical evaluation objectives. This report includes information on several key topics and challenges, such as enabling mobility electrification, interaction with grid and building sectors, demographic profiles and serving the needs of people with reduced mobility, way finding in ever larger facilities, mobility automation, and other emerging themes.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Facility Cybersecurity Framework Best Practices

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought. The U.S. Department of Energy’s (DOE) Federal Energy Management Program (FEMP) funded the Pacific Northwest National Laboratory (PNNL) to develop various cybersecurity tools, trainings, and reports to aid federal facility managers – and other building owners and operators – in better applying frameworks and lessons learned from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), risk management framework (RMF), DOE’s cybersecurity capability maturity model (C2M2), and a wide variety of industry best practices and guidance documents (i.e., NIST 800 series, Department of Defense United Facilities Criteria). This set of tools, collectively known as the FEMP Facility-Related Control System Cyber Toolkit (FRCS Cyber Toolkit)2, is focused on cybersecurity concerns from facility-related control systems and other operational technology (OT), such as industrial control systems (ICS). The FRCS Cyber Toolkit can be applied across six of the sixteen critical infrastructure sectors designated by the Department of Homeland Security, including government facilities, healthcare and public health, commercial facilities (e.g., public assembly, offices, lodging), financial services (e.g., banking and insurance), emergency services (e.g., fire and police stations), and information technology. With increasingly converged IT and OT systems, it is crucial to address OT cybersecurity considerations and assess how the seam of these two systems could impact the overall cybersecurity posture of a facility. The objective of this report is to provide an overview of the best possible method to use FRCS Cyber Toolkit (section 2.0) and distilled cybersecurity best practices for the federal facilities to address growing non-linear cyber threats (section 3.0). Recommendations in this document are aggregated from several NIST and other documents (see Appendix A for additional details).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Transforming Energy through Sustainable Mobility: Expanding Energy-Efficient Mobility Options

As the nation's premier facility for energy-efficient transportation research and development solutions, NREL blazes new trails with a whole-system approach that combines vehicle, building, and infrastructure connectivity and automation to optimize mobility and increase energy efficiency. NREL also explores new options for high-performance engines to maximize energy efficiency and on-road vehicle performance, while minimizing emissions, enhancing energy security, increasing U.S. jobs, and lowering drivers' costs.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗