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DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Advances in Desiccant Wheels for Dehumidification, VOC Mitigation, and CO 2 Removal for Energy-Efficient IAQ Management

Humidity control is pivotal to maintain occupant thermal comfort and suppress mold growth in indoor environments. Furthermore, poor indoor air quality (IAQ) due to the presence of volatile organic compounds (VOCs) and high concentrations (>1,000 ppm) of CO 2 can cause health issues and negatively affect cognitive performance. Therefore, providing high-quality indoor air has gained significant attention over the past decade. Conventional cooling coil and filter-based HVAC systems have limited capability to meet the augmented demand for occupant thermal comfort and high indoor air quality. Moreover, modern buildings are increasingly airtight to save energy, and increasing ventilation to mitigate VOC and CO 2 concentration is discouraged. Separate sensible and latent cooling technology using a rotary desiccant wheel presents a promising solution in this respect. Because of the development of desiccant materials with high water vapor, VOC, and CO 2 uptake, desiccant wheels can be used as an integrated technology option for IAQ management. Here, to promote desiccant wheel use for energy-efficient management of IAQ in buildings, this article reviews recent advancements in using desiccant wheels for dehumidification, VOC mitigation, and CO 2 capture from outdoor air. Finally, the article presents the authors’ perspective by summarizing the key research gaps in the field and discussing the future direction of research to address these gaps from two different aspects, namely, suitable adsorbent material development and desiccant wheel design.

CO2 capture↗

DOE OSTI2026

A dual membrane-adsorption evaporator for solar-powered lithium extraction from complex brines

A reliable supply of lithium is required to meet the increased demand for batteries over the coming decades. In this work, we demonstrated the potential to effectively extract lithium from brines by coupling solar-powered evaporation, adsorption, and membrane technologies together. We first synthesized a three-dimensional adsorptive evaporator by coating a lithium manganese oxide material onto a cotton stick using an easily scalable, one-step method. An osmotic membrane was then installed at the root of the evaporator to enhance the lithium to magnesium selectivity, prevent scaling caused by divalent cations, and thus further increase the water evaporation flux. The operation of the dual membrane-adsorption evaporator is entirely driven by osmosis and capillary force, demanding no extra energy input. The integration of the osmotic membrane was found to increase the lithium to magnesium selectivity over 10-fold to higher than 40. The dual process also produced high lithium to calcium selectivity and proved to sustain long-term stability by self-managing its internal osmotic pressure. Last, a test was done in three simulated brine solutions with varied brine chemistry and the evaporation rate stayed high and consistent throughout the long-term test. This illustrates that this technology can be used in varied brine solutions throughout the world providing efficient lithium capture and separation from competing divalent cations.

Eskafi, Aydin F. [University of California, Berkel↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis

Electrical energy storage systems offer a wide range of options to efficiently manage our power supply infrastructure. Energy and environmental security via renewable resources and energy storage devices will also play a critical role in addressing the supply-demand challenges. A novel energy efficient storage system based on near isothermal compressed air energy storage concept, named as Ground-Level Integrated Diverse Energy Storage (GLIDES) is analyzed for integration with residential and commercial buildings. Additionally, the influence of different configurational aspects on key performance and cost attributes is presented in this study. GLIDES modules in the range of 0.5 kWh–15 kWh configurations were considered for residential buildings whereas 25 kW h–500 kW h modules were considered for commercial buildings. Detailed cost and performance analysis of the GLIDES module proved them to be an effective resource in lowering the peak energy demand and corresponding utility bills.

25 ENERGY STORAGE↗

DOE OSTI2020

Uncertainty Analysis Framework for the Hospital Resource Supply Model for Covid-19

In March and April of 2020 there was widespread concern about availability of medical resources required to treat Covid-19 patients who become seriously ill. A simulation model of supply management was developed to aid understanding of how to best manage available supplies and channel new production. Forecasted demands for critical therapeutic resources have tremendous uncertainty, largely due to uncertainties about the number and timing of patient arrivals. It is therefore essential to evaluate any process for managing supplies in view of this uncertainty. To support such evaluations, we developed a modeling framework that would allow an integrated assessment in the context of uncertainty quantification. At the time of writing there has been no need to execute this framework because adaptations of the medical system have been able to respond effectively to the outbreak. This report documents the framework and its implemented components should need later arise for its application.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Hydropower representation in water and energy system models: a review of divergences and call for reconciliation

Abstract Reservoir-based hydropower systems represent key interactions between water and energy systems and are being transformed under policy initiatives driven by increasing water and energy demand, the desire to reduce environmental impacts, and interacting effects of climate change. Such policies are often guided by complex system models, whereby divergence in system representations can potentially translate to incompatible planning outcomes, thereby undermining any planning that may rely on them. We review different approaches and assumptions in hydropower representation in water and energy systems. While the models and issues are relevant globally, the review focuses on applications in California given its extensive development of energy and water models for policy planning, but discusses the extent to which these observations apply to other regions. Structurally, both water-driven and energy-driven management models are similar. However, in energy models, hydropower is often represented as a single-priority output. Water management models typically allocate water for competing priorities, which are generally uninformed by dynamic electricity load demand, and often result in a lower priority for hydropower. In water models, constraints are increasingly resolved for non-energy components (e.g. inflow hydrology and non-energy water demand); few analogues exist for energy models. These limitations may result in inadequate representations of each respective sector, and vastly different planning outcomes for the same facilities between the two different sectors. These divergent modeling approaches manifest themselves in California where poorly reconciled outcomes may affect decisions in hydropower licensing, electricity grid flexibility and decarbonization, and planning for environmental water. Fully integrated water-energy models are computationally intensive and specific to certain regions, but better representation of each domain in respective efforts would help reconcile divergences in planning and management efforts related to hydropower across energy and water systems.

Rheinheimer, David E. (ORCID:0000000315259069)↗

DOE OSTI2024

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)

Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Model Predictive Control for a Grid-interactive Efficient Thermal Storage-integrated Heat Pump System

Building heating and cooling systems can be used to overcome the mismatch between the intermittent supply of renewable power and the fluctuating demand for electricity. A novel underground thermal energy storage integrated with a dual-source heat pump has been proposed to mitigate the mismatch while meeting the thermal demand of buildings efficiently. Conventional thermostat control with heuristic rules cannot provide intelligent decisions to maximize the thermal efficiency and flexibility of the proposed system. Advanced control strategies like model predictive control (MPC) have provided a new paradigm for grid-interactive efficient building operation with the advancement of computation and sensing. This study developed an MPC for the proposed system to provide grid service for Demand Side Management and minimize the operating cost of building owners. A control-oriented dynamic model of the proposed system has been developed. Given an objective function and proper constraints, an optimization problem is formulated to determine the optimal control strategy of the system. Dynamic Programming is adopted to solve the optimization problem. A rule-based control (RBC) is also developed to achieve similar goals. Short-term simulations are conducted to compare the system performance resulting from the two controls. The simulation results indicate that the MPC performs more intelligently than the RBC in charging thermal energy storage and selecting heat pump sources by taking advantage of the predicted cooling demands of the building and the performance of the integrated system. As a result, the MPC could save energy and reduce operating costs compared with the RBC. A case study shows that, for a 3-day operation, the MPC saves 36.9% energy and reduces 38.5% operating cost compared with the RBC.

Shi, Liang↗

DOE OSTI2020

Piecewise Linear Optimization for Public EV Charging Depots in Different Utility Environments

With the proliferation of electric vehicles (EVs), EV supply equipment technology is being pushed to increasingly higher power levels, such as extreme fast charging (XFC) at 300kW or more. As more public XFC depots are connected to electric distribution systems, utility operators will mitigate their impact with pricing schemes and programs such as demand response, demand charges, and time-varying prices. In this paper we describe an XFC management system that combines an on-site battery energy storage system and an optimization application that minimizes the cost to operate the depot in light of the utility pricing programs. We have designed the optimizer specially to use fast and simple linear programming so that it can be deployed to low-cost computing platforms. We also describe how several utility pricing schemes are integrated into a single piecewise model, reducing the engineering effort to implement the optimizer in different utility environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Data-Driven Modeling of a High Capacity Cryogenic System for Control Optimization

The Cryogenic Moderator System (CMS) is responsible for maintaining a steady flow of cold neutrons for numerous physics experiments at the Spallation Neutron Source (SNS) in Oak Ridge National Laboratory (ORNL). Sudden losses in beam power, known as beam trips, cause a major disturbance to the CMS due to large step changes in cooling demands. Ongoing efforts on upgrading the neutron beam power from 1.4 to 2.0MW are expected to generate larger transients that can further strain the CMS subsystems if they are not properly controlled. To manage such disturbances, four flow valves and one electric heater are adjusted by five decentralized proportional-integral-derivative (PID) controllers. However, the original PID gains were calibrated empirically based only on tracking performance and not based on disturbance rejection. To address this issue without compromising current CMS operations, a control-oriented model was developed to recalibrate the PID controllers offline. The zero-dimensional (0-D) model was based on simple physics-based principles and data-driven system identification techniques. The CMS was broken into several subsystems for analysis, each of which corresponds to a parametric model tied to the thermodynamic states of the working fluid. The model parameters were identified using the nonlinear least squares method where the residuals were calculated from available sensor data. Simulation results show that the proposed model can capture the dynamics of the CMS at steady state and during beam trips.

Maldonado Puente, Bryan↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids with Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Unified Memory: GPGPU-Sim/UVM Smart Integration

CPU/GPU heterogeneous compute platforms are an ubiquitous element in computing and a programming model specified for this heterogeneous computing model is important for both performance and programmability. A programming model that exposes the shared, unified, address space between the heterogeneous units is a necessary step in this direction as it removes the burden of explicit data movement from the programmer while maintaining performance. GPU vendors, such as AMD and NVIDIA, have released software-managed runtimes that can provide programmers the illusion of unified CPU and GPU memory by automatically migrating data in and out of the GPU memory. However, this runtime support is not included in GPGPU-Sim, a commonly used framework that models the features of a modern graphics processor that are relevant to non-graphics applications. UVM Smart was developed, which extended GPGPU-Sim 3.x to in- corporate the modeling of on-demand pageing and data migration through the runtime. This report discusses the integration of UVM Smart and GPGPU-Sim 4.0 and the modifications to improve simulation performance and accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗