Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “renewable energy resources”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Estimating Energy Market Schedules using Historical Price Data

The global climate crisis is expected to reshape the energy generation landscape in the coming decades. Increasing integration of non-dispatchable renewable energy resources into energy infrastructures and markets creates uncertainty as well as new opportunities for flexible energy systems. To conduct proper economic evaluation of flexible energy systems, such as integrated energy systems (IES), advancements in modelling of market interactions, such as bidding, is crucial. This work presents a shortcut algorithm which uses two mixed integer linear programs to compute dispatch schedules (e.g., hourly power production targets) that are constrained by the resource's bid information and characteristics (e.g., minimum up and down times) based on historical locational marginal price (LMP) data. The proposed algorithm is approximately 100 times faster and uses orders of magnitude less data than a full production cost model (PCM). We find the shortcut simulator recapitulates generator dispatch signals for the Prescient PCM with approximately 4% error for the RTS-GMLC test system.

electricity generation↗
DOE OSTI2021

Technoeconomic assessment of hydrogen cogeneration via high temperature steam electrolysis with a light-water reactor

Increased electricity production from renewable energy resources, coupled with low natural gas (NG) prices, has caused existing light-water reactors (LWRs) to experience diminishing returns from the electricity market. This reduction in revenue is forcing LWRs to consider alternative revenue streams, such as introduction hydrogen production or desalination, to remain profitable. This paper performs a technoeconomic assessment (TEA) regarding the viability of retrofitting existing pressurized-water reactors (PWRs) to produce green hydrogen (H 2 ) via high-temperature steam electrolysis (HTSE). Such an integration would allow nuclear facilities to expand into additional markets that may be more profitable in the long term and eliminate CO 2 emissions from the hydrogen production process. Here, to accommodate such an integration, a detailed single market levelized cost of hydrogen (LCOH) and multimarket analyses were conducted of HTSE process operation, requirements, costing, and flexibility. Alongside this costing analysis, market analyses were conducted on the electric and hydrogen markets in the PJM interconnect. Utilizing a novel stochastic, dispatch optimization approach results suggest that a positive gain is achievable, and by operating in multiple markets, the nuclear facility can avoid the sale of electricity during times of low electricity market pricing, while maintaining the ability to capitalize on the high electricity market pricing. It should be noted that the analysis conducted is a differential cash flow analysis and, as such, does not present profit levels. LCOH analysis results demonstrate the potential exists to produce hydrogen at a cost as low as $1.20/kg. This price is lower than traditional steam methane reforming (SMR) allowing nuclear based hydrogen production to disrupt the existing hydrogen production market.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Techno-economic analyses of several redox flow batteries using levelized cost of energy storage

Development of inexpensive long-duration energy storage supports widespread deployment of variable renewable energy resources onto the electricity grid. Flow batteries are a promising class of devices for long-duration energy storage. Techno-economic modeling is needed to evaluate commercial feasibility of existing technologies and to help guide research and development of new technologies. Levelized cost of storage is a useful metric that accounts for capital and operating costs and energy throughput over the life of a project. This metric is used to compare the economic prospects of lithium ion to eight aqueous and two hypothetical nonaqueous flow batteries in four use cases. Finally, flow batteries with inexpensive active materials are favored in long-duration applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2021

Model-free control for frequency response support in microgrids utilizing wind turbines

Islanded microgrids integrated with renewable energy resources bring additional challenges to the primary frequency control due to their power-electronic interfaced generations. Developing an efficient control strategy for inertia emulation and frequency response support of microgrids is becoming an important issue. Wind turbine dynamics, however, will degrade the emulated inertial and primary responses with most traditional control strategies. On the other hand, compensation methods often require the knowledge of system models at the design stage and state estimation for feedback once deployed online. To overcome these challenges, this paper explores a model-free control (MFC) strategy to emulate the desired inertia and support the frequency response of a diesel-wind microgrid system. Additionally, the proposed MFC strategy utilizes a model reference concept to obtain guaranteed inertial response and improve the frequency response of microgrids. This work considers different control strategies with different operating points. The proposed controller is implemented and verified using the modified 33-bus full nonlinear model of the three-phase diesel-wind system in Simulink. Simulation results demonstrate the improved frequency response by precisely emulating the desired inertia using the MFC strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Cooperative fault management for resilient integration of renewable energy

Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Here, real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Validation of Phasor-Domain Transmission and Distribution Co-simulation Against Electromagnetic Transient Simulation

The rapid deployment of renewable energy resources has led to the widespread use of power electronics in modern power systems. As these systems transition from being dominated by large synchronous machines to increasingly incorporating inverter-based resources (IBRs), traditional methods are becoming inadequate. Addressing this challenge, this paper introduces a scalable phasor-domain T\&D co-simulation framework based on open-source software. It focuses on the framework's validation against the PSCAD Electromagnetic Transient (EMT) analysis tool. The validation results demonstrate the framework's high-fidelity and a computational time speed-up of 60 to 100 times, marking a pioneering validation effort in T\&D co-simulation research.

Inverter-based resources, co-simulation, Electroma↗
DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Hydroxide melt induced corrosion of Ni at elevated temperatures under steam electrolysis conditions

Molten alkali electrolyte based high temperature water electrolysis (HTWE) process offers an efficient pathway to integrate renewable energy resources for large scale and economic hydrogen production. Long term and stable operation of these systems require an in-depth understanding of materials stability under anodic and cathodic exposure conditions of the cell and cell stacks. In the present study, we report our findings on the corrosion of Ni in the presence and absence of (LieNa) hydroxide melt at 600 °C under oxidizing and reducing conditions representative of HTWE. While the Ni electrode was found to remain thermodynamically stable in metallic form under cathodic (reducing) exposure conditions, the corrosion rate in molten hydroxide under oxidizing conditions was found to be nonparabolic in nature. A cyclic voltammetry study provides the breakdown of the passive metal-oxide surface layer at the anodic overpotential region between 0.45 V and 2 V in molten hydroxide under oxidizing conditions. As a result, a thermochemical process for accelerated corrosion based on the oxide scale fluxing in hydroxide melt has been developed.

08 HYDROGEN↗
DOE OSTI2025

Facile synthesis of co-doped Carbon nanofibers for supercapacitor applications

Here, in this study, the synthesis and electrochemical performance of carbon nanofibers (CNFs) co-doped with aluminum (Al) and nitrogen (N) and decorated with zinc oxide (ZnO) nanoparticles are investigated. After electrospinning polyacrylonitrile (PAN) nanofibers, the nanofiber mats are coated with Al and ZnO precursors by the dip coating method and converted into carbon nanofibers by thermal treatments at 850 °C. Co-doping and nanoparticle decoration affect the efficacy of carbon nanofibers as supercapacitors. With a maximal specific capacitance of 206.28 Fg -1 , it is believed that our produced carbon nanofibers, which can effectively store the renewable energy resources required to meet the rising energy demand, offer superior electrochemical properties compared to traditional supercapacitor materials. The effective incorporation of Al, N, and ZnO into the CNF structure, in conjunction with enhanced electrochemical performance, represents a significant advancement in the development of functional carbon nanofibers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10 th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

AGB↗
DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Reversible redox chemistry in azobenzene-based organic molecules for high-capacity and long-life nonaqueous redox flow batteries

Redox flow batteries (RFBs), often categorized as aqueous and nonaqueous systems, represent a type of large-scale energy storage technology for renewable energy resources. Redox-active organic molecules have recently drawn extensive interests in RFBs as promising active materials due to their elemental abundance, structural diversity and high tunability of properties, but employing organic molecules in nonaqueous systems is far limited in terms of useable capacity and cycling stability. Here we introduce a class of azobenzene-based organic compounds with the azo group as redox-active center in the p-conjugated structure as new active materials to realize high-performance nonaqueous RFBs with long cycling life and high capacity. By screening suitable organic solvents with high solubility and conductivity, the azobenzene organic molecule was capable to achieve a stable long cycling with a low capacity decay of 0.014% per cycle and 0.16% per day over 1000 cycles. And the stable cycling of electrolytes under a high concentration of 1 M was also realized, delivering a high reversible capacity of ~46 Ah L -1 . The unique lithium-coupled redox chemistry accompanied with a voltage increase was observed and revealed by experimental characterization and theoretical simulation. With the reversible two-electron redox activity of azo group in p-conjugated structures, azobenzene as an azo-aromatic molecule represents a class of promising redox-active organics for potential grid-scale energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2023

Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling

To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage is likely to play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. Additionally, we further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities. Capacity expansion modelling (CEM) approaches need to account for the value of energy storage in energy-system decarbonization. A new Review considers the representation of energy storage in the CEM literature and identifies approaches to overcome the challenges such approaches face when it comes to better informing policy and investment decisions.

25 ENERGY STORAGE↗
DOE OSTI2020

Sustainable energy and fuels from biomass: a review focusing on hydrothermal biomass processing

Fossil fuels are being replaced with renewable energy resources (biomass and biomass waste, solar, geothermal, wind, etc.) to ensure sustainable development, reduce the dependence on fossil fuels, address environmental challenges including climate change. Today, biomass produces 5 × 10 19 kJ of energy/year, which corresponds to 10% of the annual global energy consumption. Considering the variety of biomass resources, this value is predicted to reach 150 × 10 19 kJ by 2050. Biomass may become even more important for use as an energy resource and chemical raw material in the 21st century. Hydrothermal biomass conversion stands out as a promising and alternative technology. Methods such as traditional gasification and pyrolysis require dry biomass. Hydrothermal techniques have been developed to eliminate the cost and time required for drying biomass. The purpose of this process is to decompose biomass with a high moisture content into small molecules and reduce its oxygen content to obtain liquid fuels or valuable chemicals. Finally, this review presents the current and future state of energy, energy sources, biomass properties and biomass conversion technologies with a focus on hydrothermal technologies.

09 BIOMASS FUELS↗
DOE OSTI2024

Inverter Model Validation and Calibration Using Phasor Measurement Unit Data

As the penetration of inverter-based renewable energy resources increases in the power grid, especially at the distribution and microgrid levels, the need to accurately represent them in planning studies increases as well. However, due to the lack of well-established standard procedures, and vendor reluctance towards the detailed sharing of proprietary models, automated dynamic model validation and parameter calibration tools for inverter based resources (IBRs) remain scarce. This work presents a model validation and parameter calibration platform for representing IBRs with generic phasor-domain models. Phasor measurements of power system events are used for continuous validation using the data playback method, and model parameters are re-calibrated if a significant mismatch between measurements and model response is observed. Unique features of the proposed platform include- (a) an iterative Bayesian optimization approach towards parameter calibration to address a possible mismatch between the structures of generic models implemented in simulation softwares and actual commercial inverters, (b) error metrics designed to account for a possible mismatch between the time resolution of simulation and measurements, and (c) analysis of the measurement-simulation mismatch to provide guidance to engineering personnel regarding model shortcomings. The performance of the platform has been illustrated using both simulated data and field measurements to validate/calibrate inverter models in GridLAB-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Hydrokinetic Energy Conversion System using Underwater LLC-type Resonant Converter

The hydrokinetic energy of river and ocean currents is a valuable renewable energy resource. Conventional systems for harvesting hydrokinetic energy usually have bulky passive components, lengthy cables connecting the underwater turbine/generator with onshore power converters, and limited control over power generation. This paper presents a new hydrokinetic energy harvesting system using an LLC-type resonant converter that is placed underwater in close proximity to the generator. By using SiC devices and the soft switching characteristic of LLC resonant converter, the converter operates in 10s of kHz, significantly reducing the passive components. In addition, the LLC converter provides flexible power regulation of the system. Furthermore, the underwater placement of the power converter takes advantage of the cooling provided by the water and simplifies the system by reducing its connecting cable length.

Dai, Hang↗