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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

Here, in this study, real-time predictive control and optimization model based on an ANN (artificial neural network) was developed to evaluate the cooling energy saving performance of the optimized control of CndWT (condenser water temperature). For this purpose, the difference in TCEC (total cooling energy consumption) between the conventional control strategy when the CndWT produced by the cooling tower is fixed and the optimized control strategy when real-time control of the CndWT through the optimal ANN model is applied was compared and analyzed. For the modeling of the building to be simulated, the co-simulation of EnergyPlus and MATLAB was built through the middleware Building Controls Virtual Test Bed. For the prediction of TCEC, an ANN model was developed through MATLAB's neural network toolbox. The model accuracy of the ANN was examined through Cv(RMSE) index and as a result, Cv(RMSE) of the optimized ANN model turned out to be approximately 25 %. More importantly, the predictive control technique was able to save TCEC by 5.6 % compared to the conventional control method constantly fixing CndWT set-point to 30 °C. These results showed that the CndWT needs to be dynamically controlled using artificial intelligence technique such as ANN model and that significant energy savings were achievable compared to the conventional fixed control.

42 ENGINEERING↗
NASA NTRS1998

Advanced Electric Distribution, Switching, and Conversion Technology for Power Control

The Electrical Power Control Unit currently under development by Sundstrand Aerospace for use on the Fluids Combustion Facility of the International Space Station is the precursor of modular power distribution and conversion concepts for future spacecraft and aircraft applications. This unit combines modular current-limiting flexible remote power controllers and paralleled power converters into one package. Each unit includes three 1-kW, current-limiting power converter modules designed for a variable-ratio load sharing capability. The flexible remote power controllers can be used in parallel to match load requirements and can be programmed for an initial ON or OFF state on powerup. The unit contains an integral cold plate. The modularity and hybridization of the Electrical Power Control Unit sets the course for future spacecraft electrical power systems, both large and small. In such systems, the basic hybridized converter and flexible remote power controller building blocks could be configured to match power distribution and conversion capabilities to load requirements. In addition, the flexible remote power controllers could be configured in assemblies to feed multiple individual loads and could be used in parallel to meet the specific current requirements of each of those loads. Ultimately, the Electrical Power Control Unit design concept could evolve to a common switch module hybrid, or family of hybrids, for both converter and switchgear applications. By assembling hybrids of a common current rating and voltage class in parallel, researchers could readily adapt these units for multiple applications. The Electrical Power Control Unit concept has the potential to be scaled to larger and smaller ratings for both small and large spacecraft and for aircraft where high-power density, remote power controllers or power converters are required and a common replacement part is desired for multiples of a base current rating.

Soltis, James V.↗
DOE OSTI2022

Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings

Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.

commercial buildings↗
DOE OSTI2025

Energy performance of an operational government building retrofitted with ceiling phase change material tiles in a mixed-humid climate

The aging U.S. building stock requires various retrofit measures to enhance their energy efficiency. Here, this study explores the integration of thermal energy storage and advanced building controls as viable retrofit solutions for load flexibility and peak demand response while maintaining the occupants' comfort. A detailed assessment is conducted on the energy use of an administrative building in Sumner County, Kansas, focusing on the implementation of phase change materials (PCMs) in the ceiling of occupied zones. First, a time-resolved, whole-building energy model is developed in EnergyPlus, incorporating complex thermal behaviors such as air exchange between the plenum space and occupied zones, envelope leakage, and operational schedules. The model is then validated using experimental field test data, and subsequently a parametric assessment of key PCM properties and application strategies is performed to evaluate cooling electricity demand benefits. The parametric study shows that the optimal retrofit strategy, comprising a PCM with 23°C peak melting temperature, 0.125 in. (3.17 mm) thickness, and 150 kJ/kg latent heat, combined with active controls that include 8 h of precooling, forced convection under the ceiling, and a 2°C thermostat setback during peak hours, can result in a maximum load shift during the peak period of 99.6 % and the total electricity savings during the peak period of 98.9 % for the optimum case and thus provide significant cost savings under time-of-use pricing scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Design and Demonstration of Emergency Control Modes for Enhanced Engine Performance

A design concept is presented for developing control modes that enhance aircraft engine performance during emergency flight scenarios. The benefits of increased engine performance to overall vehicle survivability during these situations may outweigh the accompanied elevated risk of engine failure. The objective involves building control logic that can consistently increase engine performance beyond designed maximum levels based on an allowable heightened probability of failure. This concept is applied to two previously developed control modes: an overthrust mode that increases maximum engine thrust output and a faster response mode that improves thrust response to dynamic throttle commands. This paper describes the redesign of these control modes and presents simulation results demonstrating both enhanced engine performance and robust maintenance of the desired elevated risk level.

aircraft engine simulation↗
DOE OSTI2021

Energy Management Information Systems Technical Resources Report

Guide supports federal facility staff in understanding, designing, procuring, and implementing Energy Management Information Systems (EMIS) as a valuable component of their portfolio-level energy and water planning and management strategies. As a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance, EMIS tools present significant opportunities for federal sector energy savings and improved operational performance. EMIS are at the forefront of transforming energy management best practices by providing building owners and operators with well-organized building performance and energy consumption data, enabling a host of analytic capabilities. These capabilities include portfolio-wide energy benchmarking, data visualization, and key performance indicator tracking; automated fault detection and diagnostics (AFDD); artificial intelligence for predictive analytics and control; automated measurement and verification of energy conservation measures; and supervisory control enabling automated system optimization and demand management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Enhancing Building Energy Efficiency through Advanced Sizing and Dispatch Methods for Energy Storage

Energy storage and electrification of buildings hold great potential for future decarbonization. However, there are several technical and economic barriers that prevent large-scale adoption and integration of energy storage in buildings. These barriers include integration with building control systems, high capital costs, and the necessity to identify and quantify value streams for different stakeholders. To overcome these obstacles, it is crucial to develop advanced sizing and dispatch methods to assist planning and operational decision-making for integrating energy storage in buildings. This work develops simple and flexible optimal sizing and dispatch framework for thermal energy storage (TES) and battery energy storage (BES) systems in large-scale office building. The optimal sizes of TES, BES, as well as other building assets are determined in a joint manner instead of sequentially to avoid sub-optimal solutions. The interaction between the sizing at the planning stage and hourly or sub-hourly dispatch at the operating stage is explicitly modeled. The solution is determined considering both capital costs in optimal sizing and operational benefits in optimal dispatch. Comprehensive assessments are performed using simulation studies to quantify potential energy, economic, and emission benefits by different utility tariffs and climate locations, to improve our understanding of the techno-economic performance of different TES and BES systems, and to identify barriers for adopting energy storage for buildings. Finally, the proposed framework will provide guidance to a broad range of stakeholders to properly design energy storage in buildings and maximizes potential benefits, thereby advancing affordable building energy storage deployment and helping us accelerating the transition towards a cleaner and more equitable energy economy.

Yu, Mingyung↗
DOE OSTI2022

Potential Heating Energy and Cost Savings of Dual Fuel Heat Pump Controls as a Residential Building Equipment Retrofit in the U.S.

This study investigated the energy and cost savings potential by modeling a dual fuel heat pump system with its control for a residential building in (5A) cold climate in U.S. The simulation analysis showed that the application of the DFHP system and its control to the target building can save the heating energy by 42% over a gas furnace system (baseline system). Although the HP-only system can save more heating energy (i.e., 53%) over the baseline system than DFHP system does, the DFHP with its control demonstrated the higher cost savings when a time of use electricity rate was considered.

Yoon, Yeobeom↗
DOE OSTI2023

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Solarize Fairbanks BRITE: Facilitating Efficient, Resilient Homes in Cold Climates

Solarize Fairbanks began an annual Solarize campaign in the Interior Alaska city in 2020, located in IECC climate zone 8, with a goal to increase the number of solar PV panels in the community. The campaign provides peer support, education, bulk purchase discounts, and simplified installation of solar PV technologies for homes, businesses, and nonprofits. In 2021, the campaign began offering energy audits to building and homeowners with a bulk discount; however, building owners were responsible for pursuing next steps on their own. In 2022, a diverse team of local, state, and federal partners formed a team to create a process to further facilitate energy efficiency improvements alongside solar PV technology. The resulting project, Solarize Fairbanks - Building Resilience for the Interior (BRITE) aims to build out an efficiency component over 3 years. If implemented, it will be the first efficiency add-on to a solarize campaign in Alaska. In year one, the team conducted energy audits of four nonprofits located in the cold climate of Interior Alaska and is providing technical assistance and fundraising for the nonprofits to pursue the recommended retrofits. These audits provided insights on the types of retrofits that could be expected to increase efficiency, comfort, and resiliency of buildings, including LED lighting retrofits, increased envelope insulation, improved building controls, and air source heat pump technology. A pre- and post-retrofit analysis will provide further insight on the energy savings and other benefits of the retrofits. It will also inform the offerings in the following years of the BRITE add-on to Solarize campaigns. In this presentation, program implementers will review the past campaigns of Solarize Fairbanks, summarize the energy efficiency and resiliency analyses of the nonprofit buildings, cover future plans for Solarize Fairbanks BRITE, and provide recommendations for other communities pursuing similar programs.

Alaska↗
DOE OSTI2024

Adaptive Computing and Multi-Fidelity Strategies for Control, Design and Scale-Up of Renewable Energy Applications

We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

adaptive computing↗
NASA NTRS2003

En Route Descent Advisor Multi-Sector Planning Using Active and Provisional Controller Plans

As decision support tools are developed to support controllers in complex air traffic control environments, new approaches to maintaining situation awareness and managing traffic planning must be developed to handle the ever-increasing amounts of alerting and advisory data. Within high-density metering and other environments where flight path changes are the rule, not the exception, and where interactions between these changes are required, current trial planning approaches are limited by potential increases in workload. The Enroute Descent Advisor (EDA) is a set of decision support tool capabilities for managing high-density en route traffic subject to metering restrictions. The EDA system s novel approach builds aircraft plans from combinations of user intent data and builds controller plans from combinations of aircraft plans to effectively maintain situation awareness during traffic planning. By maintaining both active (current) and provisional (proposed) controller plans, EDA supports controllers in coordinated traffic planning both within and between sectors. Ultimately, EDA s multi-sector planning approach will facilitate a transition from current sector-oriented operations to a new trajectory-oriented paradigm, enabling new levels of efficiency and collaboration in air traffic control.

Vivona, Robert↗
DOE OSTI2021

Metagames and Hypergames for Deception-Robust Control

Cyber-physical systems (CPSs) consist of computing and communication devices integrated with physical components such as sensors and actuators. Increasing connectivity to the Internet for remote monitoring and control has made CPSs more vulnerable to deliberate attacks, which are distinctly different from random perturbations in the system. This provides a way for purely cyber attacks to have physical consequences. Stuxnet is a prominent example of such an attack, one in which the malware acted over an extended period of time while deliberately remaining undetected. Such attacks can be described as Advanced Persistent Threats (APTs) -- long-term, stealthy attacks. Here, we extend our previous work on hypergames to develop defender strategies that are robust to deception and do not rely on attack detection. We prove that the defender can bound the attacker payoff with these strategies even when the attacker can choose between different attack modes, and we numerically demonstrate our approach on a realistic building control system. Finally, we discuss next steps in extending this approach towards an operational capability.

hypergames, cyber-physical systems, robust control↗
DOE OSTI2022

Zero Export Feeder Through Transactive Markets

This project is aimed at creating a transactive energy market to address the challenges faced by utility providers when increasing distributed energy resource (DER) adoption in their service area. One major challenge is mitigating export back to the grid during times of excess production. The transactive energy market operates at the distribution level and balances the supply and demand on the feeder, thus maintaining a zero-energy export at the primary feeder head. The market participants in this case are the residential customers on the feeder, who bid into the market. Building controls are then optimized based on the settled price. Market performance was demonstrated in this study by simulating different levels of DER penetration on a selected Pepco feeder. The feeder successfully achieved a zero export while providing cost-effective electricity to the participants, demonstrating that this market design can enable high DER penetration on existing feeders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Adopting occupancy-based HVAC controls in commercial building energy codes: Analysis of cost-effectiveness and decarbonization potential

Recent research has shown the energy-saving potential of occupancy-based HVAC controls (OBCs) in commercial buildings. However, building energy codes have not fully adopted this technology. This study aims to evaluate the cost-effectiveness and decarbonization benefits of OBCs and provide guidance for integrating occupancy sensors into building energy code development. To this end, a parametric simulation using EnergyPlus and a nationwide cost-effectiveness analysis are carried out considering three building types and 40 representative cities in the U.S. Here, the findings reveal that the current cost-effectiveness performance of OBCs is limited due to the high cost of occupancy sensors. However, incorporating the societal cost of carbon factor in future energy and environmental policy could greatly enhance the actual cost-effectiveness performance. Besides, a reduction in the cost of occupancy sensors to approximately 60% of the current price level could also greatly shorten the discounted payback period of OBCs. Additionally, OBCs demonstrate significant potential in building decarbonization, with potential CO 2 emissions savings of more than 5.56 million metric tons across the three building types and 40 selected cities. Finally, policy implications are provided to guide the incorporation of occupancy-based HVAC controls in future energy codes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗