Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Deployment”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Tariff Structures to Encourage Micro-Grid Deployment in Sub-Saharan Africa: Review and Recent Trends

This article reviews trends for micro-grid tariffs in Sub-Saharan Africa from two perspectives: guidelines for setting tariffs and methods for structuring tariffs. Different approaches are briefly described, and general benefits and drawbacks presented based on recent experiences and available literature. The pace of micro-grid deployment has suffered from a lack of private sector investment, which is often inhibited by unfavorable policies and uncertainty around tariffs. Traditional utility tariffs are too low to allow micro-grid investors to recover their full costs, but a variety of new approaches can be applied to address these challenges. Broad consensus suggests that cost-reflective tariffs are a critical enabler for micro-grid scale-up. Such tariffs can be coupled with subsidies or with hybridized approaches as well as unique new methods of tapping alternative revenue streams to maintain affordability for low-income customers and financial sustainability for micro-grids. There is no one-size-fits-all approach so long as lifetime costs can be recouped.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗
DOE OSTI2014

Optimal deployment of thermal energy storage under diverse economic and climate conditions

This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50percent and 100percent) of total on-peak summer cooling loads. Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5percent-15percent) and peak electricity consumption (13percent-33percent). The investigation also indentifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60percent and 100percent in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances.

25 ENERGY STORAGE↗
DOE OSTI2017

The International Database of Efficient Appliances (IDEA): A new tool to support appliance energy-efficiency deployment

Appliance energy-efficiency programs are a central component of many countries’ energy-policy portfolios. A major barrier to optimal implementation of these programs is lack of data to determine market baselines, assess the potential for cost-effective energy savings, and track markets over time to evaluate and verify program impacts. To address this gap, we have developed the International Database of Efficient Appliances (IDEA), a suite of software tools that automatically gathers data that is currently dispersed across various online sources and compiles it into a unified repository of information on efficiency, price, and features for a diversity of appliances and devices in markets around the world. In this article we describe the framework and functionality of IDEA, and we demonstrate its power as a resource for research and policy development related to appliance energy efficiency. Using IDEA data for refrigerators in China and India, we assess the potential for cost-effective energy savings within each market by computing robust indicators that can also be easily compared across different appliances and markets. We find that significant cost-effective savings are available on both markets. We discuss implications for the development of future energy-efficiency deployment programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

CCUS deployment under the U.S. 45Q tax credit and adaptation by other North American Governments: MARKAL modeling results

In 2018, U.S. Congress passed the Bipartisan Budget Act, known as 45Q [H.R. 3761], expanding the corporate income tax credit for carbon capture utilization and storage (CCUS). 45Q provides a performance-based tax credit for carbon capture projects of $30/ton of carbon dioxide (CO 2 ) (tCO 2 ) for anthropogenic CO 2 going to enhanced oil recovery (EOR), and $50/tCO 2 if going to straight storage. There are several conditions; for example, there is a 12-year time limit on tax credits for a new plant that commences construction before 2024 [H.R. 3761]. Here, this study aims to test the incremental impact of the 45Q tax credits on CCUS deployment, CO 2 -EOR, and power generation technological changes for North American (the United States, Canada, and Mexico) long-term energy system development. The scenario results show that offering a 12-year CO 2 storage subsidy provides the motivation needed for CCUS investment during the 12-year subsidy period, and the benefits of such investment can be sustained over the 40-year lifetime under CO 2 taxation. The modeling results show that carbon capture generation replaces uncontrolled fossil-fueled power, not new or existing renewables, so power generation and corresponding emission reductions from renewables remain unaffected by the availability of 45Q in the United States. Our scenario with CCUS technological learning indicates that CCUS could play a very important role under stringent environmental constraints. Thus, accelerated support and funding for the large-scale CCUS demonstrations is important for the execution of both short- and long-term climate mitigation goals. Given the significant role of the United States, Canada, and Mexico on the world energy system, our results represent an important contribution to the study of global energy trends.

42 ENGINEERING↗
DOE OSTI2025

Cataloging US state policy patterns towards microgrid deployment

Frequent extreme weather events have called for rigorous and timely efforts for alternative non-wire solutions. These efforts are getting more widespread to offer a perfect alternative as the conventional grid becomes progressively less resilient. One of these solutions is microgrids that can disconnect from the grid and offer grid resilience during an outage. While this technology is still finding its footing in the industry, states across the US are employing policy patterns and forms of instruments to support its deployment. This study includes a systemic review of the US by conducting a binary analysis of all 50 states (including Washington D.C, excluding other US territories) using seven variables. The results show four major policy approaches to microgrids: i) supporting microgrids through a definitive legislative activity leading to further policy action; ii) direct efforts from the public utilities commissions without a concrete legislative push; iii) initiatives from institutions other than the commissions; and lastly, iv) self-initiated community and private consumer efforts. The results help understand what policy instruments are being used in each of these patterns to support this niche technology that still faces regulatory challenges.

Furqan, Maham↗
DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗
DOE OSTI2024

Mitigating commutation failures in HVDC systems with SFCL deployment

Commutation failures represent a prevalent issue encountered in line-commutated-converter high voltage direct current (LCC-HVDC) systems. As the widespread deployment of HVDC systems continues, the risk associated with commutation failures increases, posing a growing threat to power grids due to their potential to trigger severe consequences, including cascading failures and widespread blackouts. This research paper aims to address the significant issue of commutation failure within direct current (DC) systems through advocating for the use of resistive-type Superconducting Fault Current Limiters (R-SFCLs). To substantiate the efficacy of this proposed strategy, an array of simulations are executed using the PSCAD/EMTDC software. This comprehensive study investigates the performance characteristics of R-SFCLs configured with varying resistance values, scrutinizing their response under diverse fault resistance scenarios and distinct fault initiation times within the LCC-HVDC system. The outcomes of these simulations are that SFCLs confer significant advantages for mitigating commutation failures, surpassing traditional mitigation methods in terms of effectiveness. Consequently, SFCLs emerge as an optimal solution to prevent commutation failures in the HVDC systems.

Commutation failure↗
DOE OSTI2019

Cost curve of large-scale deployment of CO2-enhanced water recovery technology in modern coal chemical industries in China

China has emerged as a world leader in the coal chemical industry, which requires large amount of water and results in considerable CO2 emissions. This situation has led to the challenge of the CO2-Water nexus for China and particularly for the sustainable development of its coal chemical industry. CO2-enhanced water recovery (CO2-EWR) technology can provide large-scale CO2 mitigation and additional water supply in an integrated manner, especially in arid areas. Meanwhile, CO2 streams from industrial separation processes in the coal chemical industries are amenable to separation and can dramatically simplify or even dispense with the capture process. This study presents the first systematic assessment of a cost curve for onshore CO2-EWR potential using CO2 streams from industrial separation processes by an evaluation framework encompassing CO2 emission inventory, site suitability evaluation, and source–sink matching with techno-economic models. Preliminary results focused on the full capacity of several coal chemical processes as of 2015 suggest that CO2-EWR technology can mitigate 269 million tons of CO2 from industrial separation processes at relatively low cost ranging from 12 to 30 USD/t CO2 in China. Furthermore, 404 million tons of underground water could be produced for further desalination and utilization. When additional capacity under development could become fully operational, the emissions of 878 million tons of CO2 could be mitigated and provide 1318 million tons of vital water resources. Therefore, CO2-EWR technology can be essential to clean and sustainable development of the coal chemical industry and may provide low-cost opportunities to accelerate the deployment of large-scale CCUS projects in China.

CO2-enhanced water recovery, Modern coal chemical ↗
DOE OSTI2025

Deployment of portable, modular gas samplers as part of an atmospheric tracer experiment

Underground nuclear explosions release noble gases into the atmosphere that can be detected to support international monitoring efforts. Atmospheric transport models help predict the movement of these gases over long distances, but struggle to predict the movement in the atmosphere local to the release. A field experiment was designed to monitor the movement of 127 Xe within a 5-km radius. Four gas samplers were deployed as part of this experiment to collect atmospheric samples at various distances from the release point. In conclusion, these samples were then analyzed in a near-field lab using a NaI detector and in an off-site lab using gamma-gamma coincidence and beta-gamma coincidence counting.

Radioxenon↗
DOE OSTI2026

Phosphonic-Acid-Reinforced Polymer Hole Transport Layers for Deployable p-i-n Perovskite Photovoltaics

The long-term durability prospects of halide perovskite solar cells are rapidly improving; however, the interface between the hole transport layer (HTL) and the perovskite remains a source of degradation. Alone, polymer- or carbazole-based HTLs suffer from incomplete coverage of the underlying indium tin oxide glass, leading to degradation and compromised performance. Here, we show a multi-HTL approach whereby a polymer HTL is reinforced using a phosphonic acid modification leading to better protection of the buried perovskite interface and more columnar growth of perovskite film, resulting in an ~40-mV open-circuit voltage (VOC) improvement indicative of suppressed interfacial recombination across multiple p-i-n device architectures. Solar cells with this reinforced HTL show higher tolerance to several accelerated stress tests. We report, among the best durabilities for unencapsulated cells, a T90 ~3,000 h (T80 ~5,900 h) at 65 degrees C under continuous 1.2 sun AM 1.5G illumination and maximum power point tracking, representing a nearly 4-fold increase compared with [2-(9H-carbazol-9-yl)ethyl]phosphonic acid (2PACz)-only devices. Furthermore, we deployed a device with this reinforced HTL on a cube satellite, with long-duration operational space testing results exceeding T80 for the complete mission duration of ~100 days in low Earth orbit.

14 SOLAR ENERGY↗
DOE OSTI2014

A compact ultra-clean system for deploying radioactive sources inside the KamLAND detector

We describe a compact, ultra-clean device used to deploy radioactive sources along the vertical axis of the KamLAND liquid-scintillator neutrino detector for purposes of calibration. The device worked by paying out and reeling in precise lengths of a hanging, small-gauge wire rope (cable); an assortment of interchangeable radioactive sources could be attached to a weight at the end of the cable. All components exposed to the radiopure liquid scintillator were made of chemically compatible UHV-cleaned materials, primarily stainless steel, in order to avoid contaminating or degrading the scintillator. To prevent radon intrusion, the apparatus was enclosed in a hermetically sealed housing inside a glove box, and both volumes were regularly flushed with purified nitrogen gas. Finally, an infrared camera attached to the side of the housing permitted real-time visual monitoring of the cable’s motion, and the system was controlled via a graphical user interface.

42 ENGINEERING↗
DOE OSTI2024

Impacts of aluminum-cerium alloy deployment on different cerium commodities

An imbalance problem exists in the rare earth element (REE) market with cerium and lanthanum being more abundant and less valuable than REEs used in permanent magnets such as neodymium, praseodymium, and dysprosium. Research has been conducted on new applications for abundant REEs to improve mining economics. However, potential market impacts of these applications on individual REE commodities are unknown. A system dynamics model was built to investigate the impacts of a new aluminum-cerium (Al-Ce) alloy that can theoretically utilize three separate cerium forms: cerium carbonate, cerium oxide, or cerium metal. Cerium carbonate currently possesses the lowest price of the three available forms making it initially the most favorable choice. However, model results showed that cerium oxide could provide the best economics for future Al-Ce alloy deployment because its larger market size buffers major shocks to price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Deploying effectively dispatchable PV on reservoirs: Comparing floating PV to other renewable technologies

In this article, we present a detailed simulation of floating photovoltaic's (PV) energy yield and associated evaporation reduction potential for the largest 128 US hydropower reservoirs. A recent article by Cavusoglu et al., published in the journal Nature Communications, outlined a hypothetical evaporation engine that could harness the energy of lake water evaporation while simultaneously conserving the water resource. Its authors suggest that evaporation engines deployed across all US lakes and reservoirs could, collectively, yield up to 70% of the total U.S. electricity production. We show that floating PV technology could: (1) deliver considerably more electrical energy than evaporation engines, amounting to 100% of the US production with only a fraction of the lakes; (2) deliver this energy on a firm, effectively dispatchable basis; and (3) conserve as much water as the evaporation engines.

14 SOLAR ENERGY↗
DOE OSTI2026

A polarimetry-based field-deployable non-interruptive mirror soiling detection method

The soiling level of heliostat mirrors in Concentrated Solar Power (CSP) fields is one of the key factors that significantly influences optical efficiency. State-of-the-art methods of monitoring heliostats soiling levels still face various challenges, including slow speed, labor-intensive operations, resolution and accuracy constraints or interruptions to solar field operations. Here, we present a rapid, cost-effective, and non-intrusive method for mirror soiling detection based on polarimetric imaging, referred to as Polarimetric Imaging-based Mirror Soiling (PIMS). The compact PIMS device is designed for integration with unmanned aerial vehicles (UAVs), enabling rapid, large-area assessments of heliostat mirrors for efficient soiling detection. Our method utilizes the correlation between the Degree of Linear Polarization (DoLP) and surface soiling level based on Mie scattering theory and Monte Carlo simulations. Field deployment of the PIMS method requires minimal device installation, and its UAV-based operation allows for soiling detection without interrupting plant activities. The PIMS method holds the potential for mirror soiling detection across various concentrated solar power (CSP) plants and can be further adapted for other types of solar fields, such as parabolic trough systems.

CSP Field↗
DOE OSTI2017

The impact of retail electricity tariff evolution on solar photovoltaic deployment

Here, this analysis explores the impact that the evolution of retail electricity tariffs can have on the deployment of solar photovoltaics. It suggests that ignoring the evolution of tariffs resulted in up to a 36% higher prediction of the capacity of distributed PV in 2050, compared to scenarios that represented tariff evolution. Critically, the evolution of tariffs had a negligible impact on the total generation from PV $-$ both utility-scale and distributed $-$ in the scenarios that were examined.

14 SOLAR ENERGY↗
DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

Autonomie↗