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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
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 OSTI2021

Lignin monomer conversion into biolubricant base oils

Despite progress in the depolymerization of lignin, only a few studies convert the obtained monomers to value-added products. Here we introduce a strategy to synthesize branched benzene lubricant (BBL) and branched cyclic lubricant (BCL) base oils from lignin-derived monomers and aldehyde. We perform carbon–carbon coupling via Brønsted acid-catalyzed hydroxyalkylation/alkylation (HAA) then hydrodeoxygenation (HDO). Optimum HAA reaction conditions achieve up to 90% guaiacol conversion and an HAA product containing 76% BBL and 24% enal condensation product over a P-SiO 2 catalyst. Subsequent HDO of HAA products over an Ir-ReO x /SiO 2 catalyst produces a lubricant-ranged mixture of BCL (C 24 ) up to yield (82%) and small fractions of dodecyl cyclohexane and C 10 and C 15 carbons alkanes. The kinematic viscosity, viscosity index, and Noack volatility of these base oils are comparable to commercial petroleum-derived poly α-olefin Group IV and refrigerant base oils. In conclusion, this approach provides a sustainable pathway for replacing petroleum-derived base oils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Techno-economic and life cycle analysis of bio-hydrogen production using bio-based waste streams through the integration of dark fermentation and microbial electrolysis

Hydrogen derived from bio-based sources, or biohydrogen (bioH 2 ), has the potential to reduce GHG emissions from industrial and transportation sectors, owing to the low carbon footprint and myriad applications like refinery operation, ammonia production, steel production, fuel cell, etc. To evaluate the commercialization potential of bioH 2 production, we modeled bioH 2 production and conducted techno-economic analysis (TEA) and life cycle analysis (LCA) of two facilities producing 50 metric tonnes of bioH 2 per day from cheese whey (CW) and solid food waste (SFW) through the integration of dark fermentation (DF) and microbial electrolysis cell (MEC) technologies. LCA results showed that CW and SFW can produce carbon-negative bioH 2 , with emissions of −8.6 and −8.0 kg GHG kg −1 bioH 2 with carbon sequestration and renewable electricity resources, respectively, making bioH 2 potentially eligible for a tax credit of $\$3$ kg −1 H 2 based on provision 45 V of the U.S. Inflation Reduction Act (IRA). In this study, bioH 2 production treats waste streams to generate fresh water, thus, potentially can receive waste water treatment fee that varies with regions. The MEC capital cost dominates the bioH 2 cost, which is mainly determined by current density. With a current density of 20 A m −2 , the production cost for CW input varied between $\$17$ and $\$24$ kg −1 bioH 2 , while that for SFW input ranged from $\$29$ to $\$30$ kg −1 bioH 2 under different operating conditions, considering the 45 V tax credit, waste water treatment fee and production revenue. If the current density increases to 100 A m −2 , the bioH 2 cost decreases to a range of $\$4.0$–$\$6.9$ for CW and $\$5$–$\$6$ for SFW scenarios. This study also shows that low-cost bioH 2 can be produced using CW waste stream as feedstock.

Ganguly, Arna [Argonne National Laboratory (ANL), ↗
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↗
DOE OSTI2022

Hydro-battery Hybrids – A Case for Holistic Assessment of Hybrid Energy Systems

With increasing penetration of renewable energy resources like solar and wind, the flexibility offered by the hydropower generation facilities would be instrumental in providing grid reliability. However, hydropower’s capabilities are often constrained to meet asset management targets and environmental flow requirements. To meet these necessary requisites while simultaneously being able to utilize hydropower’s full potential, hydro plus battery hybrids offer a logical solution. However it is often challenging to make the case for economic feasibility of hydro-battery hybrids at the required scale of battery storage. To that end, this paper proposes a multi-objective optimization framework to maximize advantages of hydro-battery hybrid systems from three different avenues: new market opportunities, environmental benefits and machine wear and fatigue. Using an illustrative case study of a peaking plant, we simulate hydropower operations for with and without 120 MWh of battery storage for four different simulated flow patterns ranging from peaking operations to run-of-the-river operations and two intermediate flow patterns in between. The results demonstrate that hybridizing the hydropower plant can potentially generate additional revenue while simultaneously enable more environmental friendly operations and reduce the overall machine wear and tear.

Chalishazar, Vishvas H.↗
DOE OSTI2024

High-Penetration Microgrids Providing Grid Stability Using Frequency Watt Control

The U.S. grid is rapidly transitioning towards utilizing inverter-based renewable energy resources such as solar, wind, and batteries, reducing the carbon emission footprint. Inverter-based microgrid control architectures remain a critical focus to address power system stability issues in future high penetration markets lacking spinning generation assets. Idaho National Laboratory (INL) is researching an active layered inverter based frequency-Watt control scheme that provides distribution level stability in high-penetration markets where grid inertia is lacking. Hardware in the loop case study was implemented using INL’s Microgrid Testbed to combat scalable frequency deviations ranging from 60 Hz down to 50 Hz initialized by a hydropower model implementing step loads using a 540-kW grid emulator. Our research findings demonstrate the importance of distribution level, inverter-based active frequency-Watt controls utilizing a battery energy storage system (BESS) to provide adequate frequency support at the point of common coupling without major power infrastructure upgrades.

14 SOLAR ENERGY↗
DOE OSTI2025

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning↗
DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗
DOE OSTI2021

A Low Voltage DC Power Electronic Hub to Support Buildings

This paper presents the communication, control, and architecture, for a low voltage (nominal 480V) hybrid AC/DC microgrid for supporting small commercial buildings with critical data center loads. The proposed system is based on a dc-power electronic hub (PEH) that seamlessly integrates renewable energy resources, energy storage, and back-up generation to support commercial building economical energy management and reliability. This PEH concept provides a parallelization of critical power to the commercial and industrial buildings leading to increased efficiency and reliability compared to traditional uninterruptable power supplies. The demonstration of the proposed PEH architecture and controls is conducted through a controller hardware in the loop validation.

Starke, Michael↗
DOE OSTI2021

Photovoltaic (PV) System Levelized Cost of Energy (LCOE) Evaluation with Grid Support Function Valuation and Service Lifetime Estimation

Photovoltaic (PV) systems play a critical role in renewable energy resource grid integration, and levelized cost of energy (LCOE) is commonly used to evaluate PV system feasibility in modern power grids. In this work, a revised PV system LCOE calculation model is derived to quantify the potential of LCOE reduction. Particularly, the grid support functions are valuated to offset the investment and operation costs of PV systems, which thereby reduces the LCOE. Meanwhile, PV system service lifetime is also estimated with the derived PV inverter reliability model, considering the critical components (i.e., semiconductor devices and capacitors). The case studies with field datasets are conducted to validate the effectiveness of the developed LCOE calculation model.

Zhao, Shijia↗
DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2023

Evaluating the Risk of Enabling Energy Storage Systems to Provide Multiple Services

Battery energy storage systems (BESS) are emerging as a promising, versatile technology for modern power grids due to their inherent distributed nature, their ability to inject bidirectional power flows and to provide multiple grid services. With the proliferation of intermittent renewable energy resources, the BESS procurement is also expected to increase, thereby creating systematic and strategic opportunities for BESS to various revenue streams for providing multiple grid services. Utilizing a co-simulation architecture, this work presents evaluation framework for enabling Utility-scale BESS with simultaneous provision of multiple services. The proposed framework is implemented on a real-life feeder modeled in GridLAB-D, leveraging multi-service grid support. A Risk-Aware control strategy is also implemented in this work that that takes into account the uncertain nature of some of the stochastic services and ensures the BESS to deliver their service commitments. This promotes overall grid reliability and encourages BESS implementation through the revenue streams from providing multiple grid services.

Theisen, John↗
DOE OSTI2023

Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

deep kernel learning↗
DOE OSTI2024

Switching-cycle-based Startup for Grid-connected Inverters

Conventional inverter startups, or grid synchronization, are hindered by slow dynamics and inrush current issues, which impede the integration of more renewable energy resources into the power grid. This paper overcomes the barriers by introducing a novel switching cycle-based startup approach for grid-connected inverters, eliminating the need for voltage sensors and phase-locked loops (PLLs). The proposed method surpasses the bandwidth constraints of traditional PLL-based techniques and achieves grid synchronization in just two switching cycles. Relying solely on sensed three-phase currents, synchronization is accomplished without inrush currents or the need for additional hardware or increased software complexity, resulting in a faster inverter startup. The paper also presents a sensitivity analysis to discuss the impact of critical parameters on the method's accuracy. As a result, the effectiveness of the proposed technique is demonstrated through simulation and experimental results.

14 SOLAR ENERGY↗
DOE OSTI2023

Guest Editorial Special Issue on Emerging Topics of Power Electronics Interfaced Battery Energy Storage System

No doubt, battery energy storage systems have been the enabling solution to balance generation and consumption of power systems with high-penetration renewable energy resources, long-range electric vehicles, and various smart devices. Upon the battery has been manufactured, the rest of implementation issues become how to interface battery and related systems, how to regulate the charging/discharging power, and how to keep battery energy storage systems (BESSs) safe, reliable, and long-term service. In this editorial, we discuss how power electronic converters and associated control and optimization, which can maximize the value of BESSs, are devoted to provide cost-effective, efficient, and even revolutionary technologies.

25 ENERGY STORAGE↗
DOE OSTI2020

Data-Enhanced Hierarchical Control to Improve Distribution Voltage with Extremely High PV Penetration

Dynamic, scalable, and interoperable control paradigms are required to enable efficient, secure, reliable, and resilient distribution grid operations with widespread grid integration of renewable energy resources. This paper presents our recent research on developing a novel, holistic, data-enhanced hierarchical control architecture that addresses the formidable challenges faced by emerging distribution grids with increasing penetrations of distributed energy resources. The proposed architecture integrates centralized monitoring and control with distributed grid-edge control and thus effectively deals with multi-spatiotemporal dynamics existing in the grid. Simulation results are provided to demonstrate the effectiveness of the proposed control architecture.

14 SOLAR ENERGY↗