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DOE OSTI2024

A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques

Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.

42 ENGINEERING↗

DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Midwest Nuclear-based Net-Zero Carbon Steelmaking Demonstration

Globally, the iron and steel industry accounts for around a quarter of GHG emissions from the manufacturing sector , which is about 7% of the 33 gigatonnes (Gt) of global CO 2 emissions . Thus, globally, the steel industry is a 2-3 Gt CO 2 per year emissions challenge. The steel industry is important to the U.S. economy with output equal to $0.5 trillion and domestic production capacity is critical to national defense, infrastructure, energy production, and transportation. President Biden’s executive order on climate identifies actions and policies to put the Nation on a path to achieve net-zero carbon emissions, economy-wide, by no later than 2050. To achieve this, deep carbon reductions in the power, transportation, buildings and capital-intensive industrial manufacturing segments such as steel, cement and chemicals need to be addressed. In its recent “Accelerating Decarbonization of the U.S. Energy System” report , the National Academy of Sciences (NAS) stated that, “while technology exists to decarbonize all parts of the energy system, some sectors remain at precommercial or first-of-a-kind demonstration stages and require significant improvement in cost and performance to become commercially viable.” The NAS goes on to state that these hard to decarbonize sectors include “aviation, shipping, and industrial subsectors such as steel, cement, and chemicals manufacturing .” The Administration has proposed a timeline to decarbonize energy, transportation and then industry and manufacturing. From a life-cycle approach, decarbonizing steel and other large industrial inputs are necessary to decarbonize transformations in energy and transportation. DOE’s TRI-Lab Consortium’s Integrated Energy Systems (IES) program is a collaboration amongst DOE’s three applied energy laboratories: the National Energy Technology Laboratory; National Renewable Energy Laboratory and Idaho National Laboratory. The IES approach looks at more tightly integrating low-carbon nuclear energy, renewable electricity, energy storage, load/demand balancing, and biomass (including plastics and other wastes) with one or more industrial processes that utilize heat and/or power from these clean and waste energy sources to produce a low-carbon commodity-scale product.

08 HYDROGEN↗

DOE OSTI2022

Achieving American Leadership in the Hydrogen Supply Chain Factsheet

Hydrogen has been identified as a key energy option to enable full decarbonization of the energy system. A secure, resilient supply chain will be critical to achieving emissions reductions and capturing the economic opportunity inherent in the energy sector transition. Electrolyzers and fuel cells are two critical components of the hydrogen supply chain that today are largely nascent industries with limited data on supply chain needs and constraints. This fact sheet summarizes findings from an accompanying report that is one in a series of deep dive assessments of the energy industrial base called for in Executive Order 14017 on America’s supply chains. The report identifies key considerations for the development of water electrolyzer and fuel cell supply chains and materials, focusing on polymer electrolyte and solid oxide technologies, to meet future demand for hydrogen produced by electrolysis and achieve U.S. decarbonization goals.

Source record↗

DOE OSTI2022

Wind Energy Supply Chain Deep Dive Assessment

The Wind Energy Supply Chain Deep Dive Assessment finds that the U.S. wind supply chain is nascent for offshore wind and mature for land-based wind, but U.S. competitiveness is declining in key wind turbine components. Continued decarbonization of the U.S. grid and economy with domestically supplied wind technology will require innovation to increase U.S. manufacturing competitiveness; investment in logistics and infrastructure, such as offshore wind ports and vessels; and policies to provide a strong and stable demand signal to support accelerated investment in the wind supply chain.

17 WIND ENERGY↗

DOE OSTI2022

Deep Electrification Analysis: The Role of the U.S. Power Grid for Sustainable Transportation

This project attempts to quantify the size of electric generation for the entire nation to transition from a fossil fuel based transportation sector to a zero GHG emission-based energy source. The scope of this analysis is limited to decarbonizing the transportation sector, leaving the remaining sectors, such as power (for those that are still fossil based), industry, and building sectors, for later phases of study. The study year for this analysis is 2050, with expected escalation in transportation services and naturally occurring evolutions in the electric power sector and the entire economy. This analysis uses the projections of the Energy Information Administration’s (EIA’s) Annual Energy Outlook (AEO 2020) Reference Case for study year 2050 [EIA/AEO2020] as a base-case. The transportation sector is disaggregated by the following modes and classes: (1) on-road (divided into light-duty, medium-duty, heavy-duty vehicles), (2) aviation, (3) maritime, and (4) rail. The decarbonization case was based on only 2 pathways: (1) electrification of on-road transportation except for 30% of heavy-duty vehicles, and (2) power-to-liquid for the remaining transportation modes. The study estimated for 11 US regions what the additional wind and storage capacities requirements are to replace the fossil-based fuels with renewable wind capacity. Considered were the utilization of the existing idle capacity particularly during the load valley at night and any additional new generation capacity in EIA projections for the reference case. To balance the additional wind capacity required significant energy storage capabilities which were estimated in terms of power capacity (GW) and energy capacity (GWh). The paper further characterizes the energy requirements by a relation of power capacity to duration, allowing the analyst to gain insights into what the best technology portfolio might be to meet the new balancing or flexibility needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Experimental Evaluation of Thermal Storage Performance of a Dual-Purpose Underground Thermal Battery

A geothermal heat pump (GHP) system is an energy-efficient building heating and cooling technology with great potential for reducing energy consumption and decarbonization. However, applications of GHP are still limited due to the high cost, of which 30% is related to the cost of installing the conventional vertical bore ground heat exchangers, which are usually installed in boreholes 60 meters deep. A dual-purpose underground thermal battery (DPUTB) has been developed to offer a low-cost ground heat exchanger with a built-in thermal storage capacity. The DPUTB innovatively integrates a shallow-bore ground heat exchanger (the outer tank), which can be installed in a borehole less than 6 m deep, with thermal energy storage (TES) (the inner tank). DPUTB has the potential to reduce the cost of a ground source heat pump system while allowing shifting the electric demand of the building served by the GHP system from peak to off-peak hours of the electric grid by charging and discharging the thermal storage. A lab-scale (1:125 in volume) DPUTB prototype was built. Phase change material (PCM) was added to increase the thermal storage capacity and maintain the supply water temperature from the TES within the desired range for direct cooling operation during the discharge period. As PCMs are critical to the TES performance of the DPUTB, this study compared the influence of different PCMs (including salt hydrate and organic PCMs) on the discharge performance of the DPUTB. The thermal State of Charge (SoC) of the DPUTB was used to compare the performance resulting from using different PCMs. Test results indicate that the organic PCM (Methyl Laurate) outperforms salt-hydrate PCMs due to a lower melting temperature and a narrower melting temperature range during the phase change process. The results of this study provide a guide for PCM selection and the optimal design of DPUTB.

Wang, Lingshi↗

DOE OSTI2024

A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO 2 , H 2 , and Air Storage Systems

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO 2 , H 2 , air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO 2 , H 2 , and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.

08 HYDROGEN↗

DOE OSTI2022

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Gas Technology Institute (Abstract)

Hydrogen has the potential to play an important role in decarbonizing energy-intensive sectors, and credible and open-source protocols are needed to vet the environmental credibility of a specific production pathway, given the variability even among the same technologies. Stakeholders and markets lack consistent, transparent, technical tools and protocols to assess the carbon intensity of hydrogen production at the asset level. Gas Technology Institute (GTI) and S&P Global Platts have launched the Open Hydrogen Initiative (OHI), a new collaboration focused on brining industry within the hydrogen marketplace together to provide further transparency into the environmental greenhouse gas (GHG) impact of hydrogen production. As part of this initiative, GTI has invited NETL to participate in the effort because of NETL’s deep expertise and capabilities in Life Cycle Analysis. Therefore, NETL is joining forces with the Gas Technology Institute (GTI) to build a measurement tool, accompanying protocols, and confidence score of the quality of the measurement. This CRADA effort provides the technical research collaboration support with GTI that will be used in the broader Open Hydrogen Initiative that can provide stakeholders the technical tools and protocols to decarbonize energyintensive sectors.

08 HYDROGEN↗

DOE OSTI2020

Addressing the challenge of carbon-free energy

This century will witness a major transformation in how energy is acquired, stored, and utilized globally. The impetus for this change comes from the deep impacts that both developed and developing societies have had on our planet's environment during the past century, and the projections going forward of what will happen if we do not act transformativelywithin the next 2 decades. This paper describes the basis for a meeting held in October 2018 on the need for decarbonization in our energy landscape, and specifically the status and challenges of the science that provides the foundation for such technology. Within the realm of decarbonization in energy generation lies the science of solar energy conversion using new or improved photovoltaic materials and artificial photosynthesis for water splitting and other energy-storing reactions. The intimately related issue of renewable energy storage is being addressed with new strategies, materials, and approaches under current investigation and development. The need to improve the interactions between scientists working on these connected but separately considered challenges and on the transition of scientific achievement to practical application was also addressed, with specific efforts enumerated.

climate change↗

DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗

DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Semiconductor: Supply Chain Deep Dive Assessment

In February 2021, President Biden signed Executive Order (EO) 14017, “America’s Supply Chains,” directing seven executive agencies to evaluate the resilience and security of the nation’s critical supply chains and to craft strategies for six industrial bases that underpin America’s economic and national security. See Sec. 3(b) of E.O. 14017. As part of the one-year response to EO 14017, the U.S. Department of Energy (DOE), through the national laboratories, conducted evaluations of the supply chains that encompass the Energy Sector Industrial Base, with a particular focus on technologies required to decarbonize the U.S. by 2050. This report focuses on one of these technologies, semiconductors, both conventional and wide bandgap (WBG) power electronics (PE). As noted in the 100-Day report, conventional semiconductors are a keystone technology that are essential for the operation of nearly every electronic device, including those that are critical to decarbonization, such as electric vehicles (EVs), industrial and building applications, and electricity generation and end use. In addition, power electronics have been applied to industrial equipment to improve energy efficiency and enhance controllability. Power electronics have also been used to improve the reliability of data centers and critical infrastructure, including the stabilization of the electric grid while subjected to disturbances. Increasingly, power electronics are being used to integrate renewable energy and battery storage systems, enabling new grid services and the development of microgrids. It is estimated that 30% of the electricity used in the U.S. passes through power electronic devices, and studies suggest that this number could reach 80% as power electronics are deployed in more markets. Nearly all forms of electrified transportation will depend on power electronics. Power electronic systems utilize high-capacity semiconductor devices at their core. The continued growth in power electronics depends on sustained innovation in the semiconductor industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Transforming Energy Through Computational Excellence: Artificial Intelligence and Machine Learning

The National Renewable Energy Laboratory's (NREL's) expertise accelerates a decarbonized energy economy through the most advanced computational techniques, including artificial intelligence (AI) and Machine Learning (ML). AI is the simulation of human intelligence processes by machines, while ML encompasses the study of computer algorithms to imitate the way humans learn.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Platinum Group Metal Catalysts: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. This report focuses on the supply chain for catalysts, specifically platinum group metal (PGM) catalysts, used for decarbonizing energy technologies. Catalysts are substances that increase the rate, conversion, and selectivity of chemical reactions and are used in a variety of applications such as chemical manufacturing, petroleum refining, and catalytic converters. Catalysts containing PGMs (“PGM catalysts”) are particularly useful in widespread industrial applications, including the production of high-volume chemicals such as ammonia, acetic acid, nitric acid, and the refining of crude oil into petroleum products. The PGM metals possess extraordinary properties such as being active oxidation and hydrogenation catalysts; excellent electrical conductors and electrodes; and outstanding adsorbers of oxygen and hydrogen. Within the energy industrial base, PGM catalysts improve the energy and materials efficiency of petroleum refining and chemical industry processes and reduce energy consumption in manufacturing. In addition to their use in catalytic converters, PGM catalysts are important to maximizing the efficiency of emerging decarbonization technologies, specifically in proton exchange membrane (PEM) electrolyzers for green hydrogen production from water and PEM fuel cells for transportation and stationary energy storage. Green hydrogen is expected to play a significant role in decarbonization scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Reducing the cost of home energy upgrades in the US: An industry survey

Decarbonizing the US residential building stock requires a substantial acceleration in home energy upgrades. Numerous barriers exist to accelerating adoption of efficient and electric building technologies, but foremost among these is high upfront costs. This study uses an industry survey delivered to a sample of home energy professionals to examine promising cost reduction strategies across a range of project types, including HVAC, water heating, and envelope/insulation projects. The survey included quantitative and qualitative questions to collect evidence on the estimated cost reduction potential of these strategies and their likelihood of use in the construction industry. The 167 survey respondents included contractors, energy consultants, architects, manufacturers, and others with experience in delivering energy upgrades in single-family and multifamily buildings in the US. Results show that significant cost reductions are achievable by minimizing additional infrastructure costs (such as replacing electric panels), streamlining project planning/management, and deploying innovations that simplify installation. We find that for a typical deep retrofit project, including heat pumps for space and water heating in addition to envelope upgrades, the strategies could result in a total installed cost reduction of nearly 50%, dramatically improving the customer economics of such a project. This research makes a novel contribution to the literature on strategies to reduce the costs of residential retrofits. We discuss how our study's insights on the highest-value cost reduction strategies for home energy upgrades can further accelerate their uptake in the US housing stock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A hub and spoke approach to optimizing energy wheeling of renewable resources

The deployment of zero carbon renewable energy sources needs to increase significantly to support the goal of net zero greenhouse gas emissions by 2050. At the same time energy end use needs to decarbonize. This will change both energy supply and energy demand patterns, requiring the energy delivery infrastructure (grid-based transmission circuits) to become increasingly flexible to maintain security of supply everywhere and always. The integration of zero carbon renewable energy requires cross-border and cross energy system coupling and a fit-for-purpose design. Nowadays, energy systems are planned, designed and operated in silos with a strong national focus. However, large-scale offshore wind production needs to be transported to deep inland locations, across country borders. The increased peak generation capacity of renewable energy sources will, at times, significantly exceed demand (Matthew Langholtz, 2020). The traditional solution of continuously reinforcing and extending the electricity grid is not sustainable from a cost and societal perspective. This paper will, however, propose a deterministic approach on how networked (interconnected grid) Points of receipt (POR) to Points of Delivery (POD) can be optimized for wheeling renewable energy resources while minimizing energy cost with a hub and spoke approach. The statistical approach will be done via using existing daily energy market clearing prices, available transmission capacity and firm daily transmission prices in open access energy markets. Renewable energy targets, including specific offshore wind targets, need to be in line with the ramp-up as implied by the Paris Agreement. These targets are required to provide industry with a secure market outlook that allows them to build up supply chains accordingly. Optimizing wheeled energy paths from carbon neutral resources such as renewables make them not only cost competitive on the unit commitment stack, but also more accessible on the dispatch stack to other carbon heavy forms of generation such as coal and natural gas turbines (Matthew Langholtz, 2020). This correlates to maximizing renewable resource inertia (wind, solar, biomass) within an interconnected grid without having to consider additional expansion of resources via land purchases and de-forestation.

Mukherjee, Srijib↗

DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗