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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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DOE OSTI2026

Regulatory and Technical Challenges and Barriers to Adoption of Distributed Wind Energy in Agricultural Settings

Distributed wind (DW) energy development can benefit agricultural landowners through the possibility of improved resilience of electrical service from onsite generation and associated economic benefits. DW development currently faces technical and regulatory challenges related to interconnection of projects with distribution or transmission equipment on the main electrical grid. A review was conducted of barriers to adoption for agricultural DW projects and potential roles of various stakeholders in addressing them. One major barrier is that the unique characteristics of wind energy generation such as its variability and intermittency, may require upgrades for the whole power line, which can lead to prohibitive cost burdens on individual interconnection customers. Another barrier is compliance with federal, state, or utility-level regulations that require technology-agnostic, industry-standard equipment that is often not technically realistic for DW projects. DW developers, grid infrastructure owner-operators, regulators, and standards publishers can work together to address these challenges and facilitate DW development.

17 WIND ENERGY↗
DOE OSTI2020

Solar Potential Analysis for the MISO Region

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Clean Power Research completed a scenario-based analysis that estimates and optimizes the levelized cost of capacity for high renewables penetrations across the entirety of the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. The analysis described in this report was preceded by an analysis completed in 2018 that used the same modeling framework to optimize generation resources located only in Minnesota.

14 SOLAR ENERGY↗
DOE OSTI2025

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

17 WIND ENERGY↗
DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗
DOE OSTI2024

The Grid Value of Ocean Current Energy in Florida

Ocean current energy technology has been proposed as a potential contributor to Florida's energy portfolio. There has been limited investigation of how this energy would be valued when integrated into the Florida electrical grid. This study assesses three future grid scenarios to evaluate the impact of adding zero-cost ocean current energy to each. The Resource Planning Model, a tool developed by the National Renewable Energy Laboratory, is used to identify the least-cost generation mix through 2050, with and without ocean current energy. The first scenario is a base case and assumes existing policies in which the addition of ocean current energy does not retire fossil-based technologies but variable generation technologies. In the second scenario, solar and storage technologies are lower cost, and the addition of ocean current generation enables those technologies along with wind to retire existing natural gas units earlier. In the third scenario, which requires a 95% reduction in carbon emissions from 2020 levels by 2050, ocean current energy can play a role in decarbonization along with other variable generation technologies. This analysis is intended to inform stakeholders on the opportunity, potential challenges, and overall value to the grid of ocean current technology from a reliability and availability focused perspective.

capacity expansion model↗
DOE OSTI2020

Exploring Renewable Energy Opportunities in Select Southeast Asian Countries: A Geospatial Analysis of the Levelized Cost of Energy of Utility-Scale Wind and Solar Photovoltaics

The costs of renewable energy-based electricity generation have fallen precipitously in recent years to levels that are increasingly competitive with traditional generation such as fossil fuel-based generation. As these costs become increasingly competitive, private developers, policymakers, and energy system planners are searching for opportunities to harness high-quality renewable energy resources. Developing economies are setting ambitious targets and exploring how cost-effective, grid-connected renewable energy options can help power economic growth and meet growing electricity demands. This includes the member states of the Association of Southeast Asian Nations (ASEAN) that are determined to reach a target of 23% of renewable energy in the region's total primary energy supply by 2025. A critical gap to identifying opportunities and scaling up renewable energy is the lack of quality data and analyses to support decisions on the investment and deployment of renewables - including wind and solar photovoltaics (PV). This work supports decision making by providing high-quality data and spatial analysis of the cost of utility-scale wind and solar PV generation in select countries of Southeast Asia - specifically, the ASEAN member states. Generation costs are expressed as the levelized cost of energy (LCOE) - a commonly used metric that represents the net present value of the unit cost of electricity during the lifetime of a particular electricity generation technology. This is the first spatial estimate of LCOE for these technologies within the ASEAN member states - providing insights into the roles that renewable energy resource quality and other factors may play in generation costs.

14 SOLAR ENERGY↗
DOE OSTI2023

The Lack of a QBO-MJO Connection in Climate Models With a Nudged Stratosphere

The observed stratospheric quasi-biennial oscillation (QBO) and the tropospheric Madden-Julian oscillation (MJO) are strongly connected in boreal winter, with stronger MJO activity when lower-stratospheric winds are easterly. However, the current generation of climate models with internally generated representations of the QBO and MJO do not simulate the observed QBO-MJO connection, for reasons that remain unclear. Furthermore, this study builds on prior work exploring the QBO-MJO link in climate models whose stratospheric winds are relaxed toward reanalysis, reducing stratospheric biases in the model and imposing a realistic QBO. A series of ensemble experiments are performed using four state-of-the-art climate models capable of representing the MJO over the period 1980–2015, each with similar nudging in the stratosphere. In these four models, nudging leads to a good representation of QBO wind and temperature signals, however no model simulates the observed QBO-MJO relationship. Biases in MJO vertical structure and cloud-radiative feedbacks are investigated, but no conclusive model bias or mechanism is identified that explains the lack of a QBO-MJO connection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Wind turbine performance enhancement with minimal structural load penalty: A design philosophy

The performance benefits of using tip devices on wind turbines has been well-documented. However, previous studies show that adding blade tip devices such as winglets leads to a significant increase in blade root bending moment, potentially requiring structural reinforcement with cost and weight drawbacks. A new and unique design philosophy for retrofit blade tip devices for wind turbines is presented. By balancing generated aerodynamic and centrifugal loads, these devices offer an increase in power production without the need for structural reinforcement. Predicted performance and cost benefits of using retrofit blade tip devices on the National Renewable Energy Laboratory 5 MW reference wind turbine are shown. Finally, the addition of blade tip devices resulted in significant improvements in the coefficient of power (C p ) and annual energy production (AEP).

17 WIND ENERGY↗
DOE OSTI2022

A reference open-source controller for fixed and floating offshore wind turbines

Abstract. This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide modern controller features. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All of the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5 MW reference wind turbine and International Energy Agency 15 MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by over 10 % compared to the NREL 5 MW reference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by nearly 30 % when using the platform feedback routine instead of a more traditional low-bandwidth controller.

17 WIND ENERGY↗
DOE OSTI2022

Enhanced convection for higher module and system efficiency (Final Technical Report)

The goal of this work is to develop new solar photovoltaic (PV) modules and solar system-scale designs that promote an increase of the convective heat transfer coefficient of at least 40%, reducing the operating temperature of the solar PV panels, and leading to a boost on the annual energy yield by at least 5%. By achieving this goal, we further foresee decreasing solar panel degradation by +0.3%/year, reducing the LCOE by 2.9-4.5¢/kWh. Currently, this question and/or approach is not considered though it is a relevant factor in performance/efficiency. The technology proposed will be at a mature stage by the end of the project and could be aligned to be proven on a full-field test. PV users, installers and manufacturers will be interested in the technology due to the aforementioned benefits. The work was conducted through a suite of wind tunnel experiments, numerical simulations and field experiments. These were then evaluated through a correlation encompassing the entire data set and implementing findings into NREL SAM. The work resulted in eight peer reviewed publications with three others in preparation. The key results of the project are 1.) Showing the capability of increasing the solar farm power production by increasing the heat transfer coefficient; 2.) Providing strategies for achieving this through large scale arrangement manipulation (heights, spacings, angles), flow deflectors and vortex generators; 3.) Demonstrating scalability of results by comparing numerical simulations, wind tunnel experiments and field experiments; 4.) Providing a correlation to be able to capture the various arrangements while exploiting the heat transfer coefficient; and 5.) Proving the feasibility of such strategies and their capacity through a comprehensive techno-economic analysis. For the heat transfer coefficients, gains of 40% were attained and increases in power in the excess of 5% were observed. For heat mitigation strategies, consideration at the PV array scale were inter-panel spacing, panel height and panel angles while at the module level, wind breaks such as solid walls and selected gaps were evaluated as well as vortex generators on the panel. All proposed strategies saw an increase in heat transfer coefficient. For scalability, results were compared against field experiments and a correlation for the heat transfer quantity was attained leading to a parametric understanding of the variations and impact in the heat transfer coefficient independent of tools implemented (wind tunnel/numerical simulations). The strategies were also further assessed by a technoeconomic analysis finding improvements based on LCOE with respect to location (several states with varies atmospheric conditions/geographic locations) yielded different results. Improvements on this end were also observed especially towards the colder climates and when considering bi-facial modules. The quantitative demonstrations of such conclusions are provided in the main portion of this document with complementary results and their narration. Milestones and go/no-go points were successfully met in the project.

14 SOLAR ENERGY↗
DOE OSTI2020

Field-emission plasma enhancement of H₂–O₂ micro-combustion

In this paper, a detailed three-dimensional computational model is developed to assess the potential of micron-scale field emission dielectric barrier discharge (FE-DBD) plasma actuators in non-premixed microburners with channel heights in the range of L z = 0.25–0.75 mm. Results for H 2 –O 2 combustion with Reynolds number Re = 100 are studied for different configurations of plasma actuator arrays. Without plasma actuation, results agree with corresponding experiments, although in this case the burner typically suffers from incomplete combustion due to thermal quenching, radical quenching, and slow diffusive mixing. For the range of conditions simulated, plasma actuation increases combustion completeness from 25%–70% to 40%–80% by enhancing mixing, generating radicals, and Joule heating. Ionic wind resulting from the FE-DBD plasma disrupts the otherwise simple laminar flow in the burner, accelerating the growth of a mixing layer and enhancing heat transfer to channel walls. The larger, lower temperature flames reduce thermal gradients (and thereby thermal stresses) in the walls. Radical generation due to electron-impact dissociation extends the reaction zone in the diffusion flame and increases combustion completeness. In all cases, the additional heat release with plasma actuation (100–190 W) is significantly larger than the power needed to sustain the plasma (≲0.1 W), suggesting that plasma actuation may provide a significant advantage in energy generation using realistic portable micro-combustion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Deployable Wind-Hybrid Power Systems for Defense and Disaster Response Applications

This report presents an analysis of the performance of deployable energy systems comprised of wind energy systems integrated with diesel generators, photovoltaic systems, and battery storage to meet the load requirements of a representative U.S. Army forward operating base. The analysis is conducted using HOMER, a microgrid analysis software that can search through a wide range of parameters to design and optimize microgrid power systems. The search parameters include the system architecture, the wind and solar resources, and the availability of diesel fuel. The results of the analysis measure the relative performance of the different systems and environments in terms of the overall transportation cost to deploy the system and the ability to provide resilience in terms of meeting mission critical loads.

02 PETROLEUM↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2020

Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

Abstract We examine links between tropical Pacific mean state biases and El Niño/Southern Oscillation forecast skill, using model‐analog hindcasts of sea surface temperature (SST; 1961–2015) and precipitation (1979–2015) at leads of 0–12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model‐analog forecast skill has been demonstrated to match or even exceed traditional assimilation‐initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10‐m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6‐month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model‐analog hindcast skill—that is, how well a model can capture the observed evolution of tropical Pacific anomalies—is an informative El Niño/Southern Oscillation metric for climate simulations.

Ding, Hui↗
DOE OSTI2022

Advanced Distributed Wind Turbine Controls Series: Part 2-Wind Energy in Isolated Grids – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

In an isolated grid, wind turbines are typically deployed to provide energy to maximize energy production, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) this paper demonstrates, through desktop simulations, advanced wind turbine controls that can be employed to support higher contributions of wind in isolated grids, and to demonstrate ways that wind can play a role in supporting stability of an isolated grid. This isolated grid used in these desktop simulations is comprised of a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a simulated diesel generator (2 MW) and various types of loads (critical, dynamic). We developed a model of the subsystems in MATLAB/Simulink and validated them with available data from real-world components on NREL's Flatirons Campus. These validated models are then configured for various case studies. We compare the output of the desktop simulation with a baseline case with the diesel generator. Active and reactive power control of the wind turbine can help improve frequency and voltage responses in the isolated grid, respectively. By utilizing a small integrated battery energy storage system in the DC-link of the wind turbine, we also demonstrate that wind turbines can help blackstart a critical load comparable to its rated power and support other renewables (e.g. solar PV) come online and pick up an additional load. This report illustrates some of these reliability and resilience services a wind turbine can provide in an isolated grid.

17 WIND ENERGY↗
DOE OSTI2026

An Intermediate-Scale Version of the Volturnus + Floating Offshore Wind Turbine Platform Concept in a Real Ocean Environment with an Operating Turbine off the Coast of Maine (Final Scientific/Technical Report)

This project was undertaken to advance the technical readiness and commercial viability of a next-generation, industrialized concrete floating foundation for offshore wind turbines called VolturnUS +. The design objective is to deliver a platform that is lower cost, faster to manufacture, simpler to deploy, and optimized for domestic supply chains and local workforce participation. To enable project financing and commercial adoption, an at-sea demonstration under representative operating conditions was required and therefore this project aimed to deploy a ¼-scale VolturnUS+ prototype offshore the Coast of Maine.

17 WIND ENERGY↗
DOE OSTI2022

Development Support for NREL's System Advisor Model (SAM): Cooperative Research and Development, CRADA Number CRD-20-16998 (Final Report)

The variable nature of renewable generation, which depends on the sun shining and wind blowing for example, presents challenges for adequate and least-cost resource planning. The deployment of renewable generation assets is anticipated to increase due to various drivers, such as declining costs, favorable government policies, and increasing procurement by corporations and consumers. High levels of renewable penetration are anticipated to pose flexibility, reliability, and cost challenges to the electrical system. The development and implementation of new features into performance modeling software, such as the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM), is one way to help think through these challenges and develop appropriate strategies.

14 SOLAR ENERGY↗
DOE OSTI2022

Distributed Wind Project Database

The PNNL research team continually collects cost, incentive, generation, and customer data from turbine manufacturers, operations and maintenance providers, state and federal agencies, and other stakeholders for distributed wind projects installed in the United States. These data are critical for identifying trends, opportunities for growth, and prioritizing investments for both WETO and industry stakeholders. Making this information available allows interested parties to better understand distributed wind market trends and characteristics.

17 WIND ENERGY↗