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At least 595 records · Page 33

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Puerto Rico Demand Response Impact and Forecast Tool (PR-DRIFT) - Beta Version [Slides]

Due to high fossil fuel imports, high electricity rates, an unreliable electricity grid, and 100% renewable electricity goals, demand response can play a crucial role for the Puerto Rico electricity grid. The Puerto Rico Demand Response Impact and Forecast Tool (PR-DRIFT) is a spreadsheet-based tool in which users can estimate the potential impacts of demand response, energy efficiency, and VRE and storage adoption in Puerto Rico from 2021 through 2040. The tool includes projections for solar, wind, and battery adoption based on released RFPs and energy targets (including Act-17 2019) to generate a projected net load profile for each hour through 2040. Based on user inputs and default assumptions, the tool also projects load profile impacts of demand response and energy efficiency, and is specifically focused on highlighting projected demand response technical potential. This demand response technical potential can be used by local utilities, regulators, program administrators, or researchers to help design demand response programs for larger impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Transient Modelling of HTGR Thermal Load Follow in Modelica

The ability of nuclear power plants to load follow electrical power demand is particularly important as we move to higher variable renewable (VRE) energy production on low carbon energy grids. High temperature gas reactors (HTGRs) are capable of load following by adjusting thermal output, but their high temperatures and thermal inertia significantly impact the power transients they are able to follow. To aid investigation into new integrated energy systems with advanced reactors, it is necessary to have an open-source transient model of a full HTGR primary and secondary. This paper introduces two new open-source models for a HTGR and corresponding balance of plant (BOP) written for the HYBRID library developed at Idaho National Laboratory. A transient thermal load follow test case was taken by the work from Brits et al. to benchmark our open-source model against transient operation results from other proprietary codes that exist. The control scheme for the two new models in combination is discussed and a comparison of the response in transient load follow between the two codes performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Advanced Power Electronics and Electric Machines

The advanced power electronics and electric machines (APEEM) research group at the National Renewable Energy Laboratory (NREL) has developed world-class experimental and modeling capabilities for designing and evaluating efficient and reliable power electronics and electric machines thermal management systems. They also design, fabricate and characterize advanced power electronics packaging, and are developing state-of-health monitoring techniques. These researchers deliver safe, reliable, high performing, power-dense components that allow seamless integration between renewable energy sources, electric transportation, and the grid, helping to make widespread electric vehicle (EV) adoption and greenhouse gas emissions reduction more feasible. This document outlines the group's major capabilities in the areas of power electronics; module development and characterization; thermal modeling and management; thermomechanical reliability analysis of devices, modules, inverters/converters, and electric machines; physics-of-failure-based reliability analysis; and microelectronics. It also overviews the group's state-of-the-art equipment for fluid-based thermal management; thermal measurement & characterization; thermomechanical reliability analysis; micro- and power electronics measurement & characterization; and prototype fabrication, as well as the group's world-class modeling and simulation capabilities.

advanced gate drivers↗

Demand Response in Bangalore: Implications for Electricity System Operations

Recent Greening the Grid studies for India highlight the benefits of flexible resources for integrating variable renewable energy onto India’s electricity system. India’s ambitious renewable energy targets, which are particularly focused on renewable resource states such as Karnataka, will face fewer challenges when combined with new planning and operational strategies and technologies. This report explores one such strategy - demand response - by which the system operator shifts load throughout a day to minimize system wide production costs. To explore this strategy, we added demand response resources to Karnataka’s electricity system in a production cost model of India, using load shifting potential analyzed by Lawrence Berkeley National Labs. We then investigated the impacts of increasing demand response capacity under several renewable resource scenarios. Our results show the addition of demand response enables fuel shifting from high-marginal-cost and emissions-intensive subcritical coal and diesel generation to zero-marginal-cost and emissions-free renewable generation. Accordingly, the value that demand response provides to the system increases as the renewable penetration increases. In addition to reducing production costs and emissions, demand response reduces the time that thermal generators spend at their minimum output levels, which typically represents a less efficient and costlier operational state. Agricultural load shifting provides greater value to the system than residential, commercial, or industrial loads. Agricultural demand response is more flexible than other sectors because it is not exposed to subdaily operational constraints and it can operate for more hours per day without impacting customer satisfaction. Further, the first increment of demand response that is added to a system provides the greatest value; further additions provide additional benefits but have a decreasing impact. The insights we discuss could be leveraged by system planners and operators in other jurisdictions, particularly those facing significant renewable energy penetrations, to develop their own demand response programs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A review of future weather data for assessing climate change impacts on buildings and energy systems

The effectiveness of climate change impact assessments and the development of adaptation strategies depend on the availability of high-quality future weather data. However, significant gaps exist between the needs of the energy research community and the focus of the climate modeling community, primarily due to a historical lack of communication and collaboration between the two groups. Here, to address this issue, this work provides a comprehensive overview of the critical aspects involved in creating future weather data for building and energy system modeling, including emissions scenarios, general circulation models, downscaling methods, categories of future weather data, and uncertainties in climate simulations. Moreover, it critically evaluates the applicability and suitability of various types of future weather data in five key application scenarios: energy use analysis, resilience analysis, HVAC design, utility-scale analysis, and renewable energy analysis. Finally, this work presents recommendations for high-level actions and research directions to foster collaboration between the energy research and climate modeling communities and to promote the integration of future weather data into energy codes and the design practices of buildings and energy systems.

Climate change↗

Zero carbon solid-state rechargeable redox fuel for long duration and seasonal storage

Here, this work presents a unique thermochemical process for charging magnesium-manganese-oxide-based solid-state rechargeable redox fuel. High-temperature heating of the processing furnace can be driven by either renewable electricity or concentrated solar power. The simple recyclable fuel charging concept is based on a tubular falling bed reactor with countercurrent oxygen-depleted gas flow for complete heat recuperation. The main focus of this work is achieving solid flowability at high temperatures (1,450°C) and extracting chemically charged solid at ambient temperature with minimal energy loss. The operation strategies described in this work have enabled consistent magnesium-manganese-oxide particle flow up to 1,450°C. The measured extent of the thermal reduction reaction after cooling the particles is more than 90% of the fully reduced state at equilibrium. The thermal-to-chemical efficiency and overall system efficiency are 96% and 35% respectively, which are the highest reported for thermochemical fuels to date.

25 ENERGY STORAGE↗

A consolidated bioprocess design to produce multiple high-value platform chemicals from lignocellulosic biomass and its technoeconomic feasibility

5-Hydroxymethyl furfural (HMF) and furfurals are DOE-listed platform chemicals that can be derived from the renewable carbon in the lignocellulosic biomasses and have the potential to replace petroleum-derived alter- natives. High substrate cost and use of expensive solvents limit the economic feasibility of bio-based HMF production on an industrially relevant scale. The study presents an experimental optimized condition that maximizes the chemical-free production of HMF and furfurals without lowering the yield of total fermentable sugars from Saccharum bagasse. Hydrothermal pretreatment at 210 °C for 15 min yielded approximately 10%, 12%, and 46% of HMF, furfurals, and fermentable sugars per gram of dry biomass, respectively. Additionally, the study proposes a consolidated bioprocess model to produce and recover four high-value bioproducts i.e., HMF, furfurals, ethanol, and acetic acid based on the experimental results and evaluates its technoeconomic feasibility considering HMF as the main product. The minimum selling price (MSP) of HMF was estimated to be 930.6 USD/ t which is competitive with its petroleum-derived precursor alternative p-xylene (1,113 USD/t). The sensitivity analysis performed for the process parameters suggests that pretreatment cost and revenues from coproducts immensely influence the MSP of HMF. The preliminary technoeconomic analysis performed on the consolidated bioprocess design indicates that additional revenue streams from diversified coproducts in biorefineries aid in lowering the MSP of high-value bioproducts.

09 BIOMASS FUELS↗

Dynamic Building Load Control to Facilitate High Penetration of Solar Photovoltaic Generation (Final Technical Report)

Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.

14 SOLAR ENERGY↗

High-Resolution Southeast Asia Wind Resource Data Set

Well informed decision-making is a key part of integrating variable renewable energy into the global energy marketplace. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to critical resource data by providing free, high-fidelity time-series wind resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights the development of the Southeast Asia wind resource data set and discusses the impacts of this data.

Advanced Energy Partnership for Asia↗

Large-Eddy Simulation of a wind turbine using a Filtered Actuator Line Model

When dealing with multirotor devices such as quadcopters or wind farms, the cost of blade-resolved large-eddy simulation (LES) becomes prohibitive. Combining LES with a family of lower-fidelity models, called actuator line models (ALMs), has grown in popularity in the past decade. ALM replaces full blade resolution with an array of actuator points or lines parameterized by aerodynamic lift/drag polar plots along the blades. Body forces computed based on these actuator points are then projected onto the LES flow mesh, mimicking the effect of rotating blades on the flow. However, the optimal projection radius and the associated LES grid size is often too restrictive for multirotor simulations. Recently, a new tip-correction-based filtered ALM (F-ALM) was proposed by Martinez-Tossas and Meneveau (2019), which allows coarser-than-optimal grids by avoiding the associated overprediction of thrust. In this work, F-ALM is implemented into a high-order, in-house LES code to simulate National Renewable Energy Laboratory Phase VI wind turbine. It is then followed by a comparison between the baseline ALM and the newly implemented F-ALM in terms of instantaneous and time-averaged flow fields and blade loads, revealing the advantages of F-ALM in preventing the overprediction of power on coarse grids. Finally, this encourages accurate and affordable simulations of multirotor devices in the future.

17 WIND ENERGY↗

A renewable double plasma mirror for Petawatt-class lasers

Exceptional pulse contrast can be critical for ultraintense laser experiments, particularly when using solid density targets, and their use is becoming widespread. However, current plasma mirror technology is becoming inadequate for the new generation of high repetition rate, high power lasers now available. We describe a novel double plasma mirror configuration based on renewable, free standing, ultrathin liquid crystal films tested at the BELLA Petawatt Laser Center. Although operating at a repetition rate of several shots per minute, this system can be scaled to a high repetition rate exceeding 1 Hz and represents an important step towards enabling sustained, continuous operation of plasma mirrors. We demonstrate an improvement of two to three orders of magnitude in contrast and a total throughput of 80%. We present the first measurements of a beam reflected from a single or double plasma mirror system using a wavefront sensor, showing a well preserved wavefront and spatial mode. Finally, we introduce a model that predicts the total throughput through this double plasma mirror. This is the first model that accurately predicts the peak reflectivity of a plasma mirror when given the laser temporal profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Research progress on organic acid pretreatment of lignocellulose

Lignocellulosic biomass is a naturally occurring, renewable resource that is utilized to produce a variety of high–value-added products, such as fuels, acids, and building block chemicals. The pretreatment of lignocellulosic biomass is a crucial step in the deconstruction and fractionation of its components. Organic acids, such as formic, acetic, lactic, and maleic acids, have been widely studied for their effectiveness in lignocellulose pretreatment. Organic acid–based pretreatment techniques are gaining increased attention due to their ability to selectively separate hemicellulose and cellulose, promote oligomer formation, and minimize byproducts. This paper presents a comprehensive review of the various advancements in the science and application of organic acids for the pretreatment of lignocellulose. Furthermore, the significant challenges of organic acid recovery after pretreatment are highlighted, and different recovery methods are discussed. In conclusion, the future challenges related to utilizing organic acids for lignocellulose pretreatment are summarized, with a strong emphasis on adopting a sustainable approach to converting valuable bioresources into renewable products.

59 BASIC BIOLOGICAL SCIENCES↗

Integration of cryogenic energy storage with renewables and power plants: Optimal strategies and cost analysis

Energy storage is critical for overcoming challenges associated with the intermittency and the variable availability of renewable sources for decarbonizing the energy sector. Cryogenic energy storage (CES) is of interest due to its high technology readiness level, no geographical limitations, and moderate round-trip efficiency. The time-varying nature of demands and renewable availability needs to be considered at the design and integration stages of energy storage. We develop a mixed-integer nonlinear program (MINLP) model to obtain the energy storage costs on a daily basis for different scenarios that typically arise over an entire year. Using this optimization-based framework, we address key decision-making questions towards energy transition: What is the energy cost when CES is integrated with renewables and power plants? How does each scenario affect the overall energy cost? How much storage is needed for complete transition to renewables? What is the optimal integration towards 100% renewable energy? What are the optimal storage designs for both renewables and fossil-based power generation with current and future energy demands? Here, we discuss different scenarios and solutions to these questions.

25 ENERGY STORAGE↗

Operando XAS and DFT Uncover Structure-Performance Relationships in Re/TiO 2 for Selective CO 2 Hydrogenation to Methanol

The conversion of CO 2 into value-added chemicals, such as methanol, offers a promising pathway toward a renewable energy future. However, a precise kinetic control and a highly selective catalyst are necessary to overcome the thermodynamic preference for CO 2 hydrogenation to methane. Rhenium-based catalysts, particularly Re/TiO 2 , demonstrate high activity and selectivity for methanol under high-pressure conditions. For example, at 100 bar and 200 °C, a methanol selectivity of 97−99% was obtained. Catalysts with 1 wt % Re and 5 wt % Re/ TiO 2 were used to study the effect of cluster sizes. At 250 °C, the 1 wt % catalyst achieves 97% selectivity at 23% conversion, whereas 5 wt % Re/TiO 2 achieves 74% selectivity at 40% conversion, corresponding to a drop in space-time yield from 65 to 16 g CH 3 OH ·g Re −1 ·h −1 , respectively. X-ray absorption spectroscopy provided insights into the structure of the active sites, while density functional theory calculations revealed the effects of cluster size on the energy barriers for H 2 activation, CH 3 OH dissociation, and CH 3 OH desorption, all of which directly influence conversion and selectivity. These results underscore the importance of balancing cluster size for optimal catalyst performance and provide insights into the design of efficient and selective catalysts for renewable methanol production.

XAS↗

Exploring the potential of high entropy perovskite oxides as catalysts for water oxidation

Electrochemical water splitting is a promising technology to renewably generate hydrogen from water with no carbon footprint. However, the catalytic efficiency is hindered by the sluggishness of the oxygen evolution reaction (OER) at the anode. Therefore, developing earth-abundant, stable, and efficient OER catalysts is of keen interest. Recent studies have demonstrated that high entropy perovskite oxides (HEPOs) can exhibit superior OER activity and excellent electrochemical stability due to the synergistic effects of the multiple cations and the high entropy configuration effect. In this opinion paper, we point out the strengths and discuss the challenges and future prospects for HEPO-based electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗