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Looking to expand your experience outside of the classroom? The Office of Energy Efficiency and Renewable Energy (EERE) at the U.S. Department of Energy (DOE) has a number of resources available for undergraduate students, including competitions, internships, and career planning information to help you navigate the education to employment pathway in energy.
Here, recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.
This presentation is for FEMP's second user support session for federal REopt users. These sessions cover topics such as: overviews of REopt capabilities, hands-on REopt trainings and demonstrations, discussion and feedback about the tool and user support, federal REopt case studies and success stories, and links to REopt resources. This particular session covers discussion about federal onsite energy projects and challenges, using REopt to evaluate air-source and geothermal heat pumps, a federal success story about using REopt for heat pump analysis, a summary of available resources, and open question and answer session.
State and local governments have a limited window of opportunity to leverage recently-extended federal tax credits to accelerate the deployment of clean, renewable energy. Federal tax credits have played an important role in the rapid growth of the U.S. renewable energy industry. This DOE guide outlines the wide array of resources available to state and local governments interested in leveraging federal tax credits to accelerate the deployment of clean, renewable energy.
Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.
Resource adequacy (RA) is an important component of bulk power system reliability that addresses whether there are sufficient resources available to meet electricity demand at all times. RA analysis is used to assess whether the planned power system will provide reliable electricity to consumers while accounting for equipment outages, weather variability, and load uncertainty. Coordination between regions can enable resource sharing to meet RA needs if sufficient transmission capacity exists. Transmission's role in RA coordination can be particularly pronounced for large power systems like the U.S. electricity grid which contains geographically diverse demand and weather-dependent resources. Depending on the level of coordination desired, existing inter-regional transmission capacity may not be sufficient. This study is designed to assess optimal pathways for the development of inter-regional transmission in the U.S. under varying levels of RA coordination. Results can inform long-term grid infrastructure planning and provide insights into potential benefits of greater coordination for generation and transmission planning between regional U.S. power systems.
Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multi-agent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.
The Office of Energy Efficiency and Renewable Energy (EERE) at the U.S. Department of Energy (DOE) has a number of resources available for graduate students, including research positions, internships, and career-planning information to help you navigate the education-to-employment pathway in energy.
SFCOMPO-2.0 is the new release of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) database of experimental assay measurements. These measurements are isotopic concentrations from destructive radiochemical analyses of spent nuclear fuel (SNF) samples. We supplement the measurements with design information for the fuel assembly and fuel rod from which each sample was taken, as well as with relevant information on operating conditions and characteristics of the host reactors. These data are necessary for modeling and simulation of the isotopic evolution of the fuel during irradiation. SFCOMPO-2.0 has been developed and is maintained by the OECD NEA under the guidance of the Expert Group on Assay Data of Spent Nuclear Fuel (EGADSNF), which is part of the NEA Working Party on Nuclear Criticality Safety (WPNCS). Significant efforts aimed at establishing a thorough, reliable, publicly available resource for code validation and safety applications have led to the capture and standardization of experimental data from 750 SNF samples from more than 40 reactors. These efforts have resulted in the creation of the SFCOMPO-2.0 database, which is publicly available from the NEA Data Bank. Our paper describes the new database, and applications of SFCOMPO-2.0 for computer code validation, integral nuclear data benchmarking, and uncertainty analysis in nuclear waste package analysis are briefly illustrated.
The mission of this project was to provide a preliminary feasibility assessment of powering different marine carbon dioxide removal (mCDR), marine carbon capture, and marine carbon sequestration strategies with marine energy. In this report, carbon capture refers to methods that can separate or capture carbon dioxide (CO 2 ) from the air or ocean; carbon sequestration refers to methods that store CO 2 obtained by capture methods out of the atmosphere for long periods of time; and carbon dioxide removal (CDR) refers to methods that do both. The project found that mCDR powered by marine energy and offshore wind energy available in the United States could meet global CDR scales needed by 2040 and 2050 to limit warming to 1.5 degrees C by 2100. Note that this preliminary estimate assumes that it is possible to harvest all the marine and offshore wind resources available in the United States with existing technology options, and it does not account for the power needed for monitoring these methods, as these power needs are not yet well defined and require further research. Additionally, these CDR scales will still require emissions reductions.
Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.
Regulatory agencies have long adopted a three-tier structure for risk assessment and risk management. We build on this structure to propose a tiered approach for resilience assessment that can be integrated in the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience is still lacking. Our proposed structure consists of tiers by which analysts can scale a resilience assessment and associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. We use an application of water allocation decisions in the Platte River Basin, Nebraska, USA to illustrate this approach.
Cyberattacks against industrial control systems have increased over the last decade, making it more critical than ever for system owners to have the tools necessary to understand the cyber resilience of their systems. However, existing tools are often qualitative, subject matter expertise-driven, or highly generic, making thorough, data-driven cyber resilience analysis challenging. The ADROC project proposed to develop a platform to enable efficient, repeatable, data-driven cyber resilience analysis for cyber-physical systems. The approach consists of two phases of modeling: computationally efficient math modeling and high-fidelity emulations. The first phase allows for scenarios of low concern to be quickly filtered out, conserving resources available for analysis. The second phase supports more detailed scenario analysis, which is more predictive of real-world systems. Data extracted from experiments is used to calculate cyber resilience metrics. ADROC then ranks scenarios based on these metrics, enabling prioritization of system resources to improve cyber resilience.
Atmospheric rivers (ARs) can significantly modulate surface hydrological processes through the extreme precipitation they produce. Previously, studies have investigated the impact of ARs on individual hydrological processes, but a comprehensive evaluation of ARs’ impact on surface hydrology is lacking. This study used a high-resolution regional climate simulation to quantify the impact of ARs on surface hydrological processes across the western U.S. watersheds. The model performance is evaluated through extensive comparison against observations. Our analysis indicates that ARs produce heavy precipitation but suppress evapotranspiration. Snowpack ablates more during ARs, which is mainly driven by warmer air temperature and increased downward longwave radiation. At the 0oC to 10oC temperature range, ARs increase the probability of snow ablation from 0.33 to 0.57. Soil moisture responds similarly to AR and non-AR precipitation events. The runoff-to-precipitation ratio almost doubles due to modulation by heavy precipitation in the southwestern watersheds or intensification of snow ablation in the northwestern watersheds during AR events compared to non-AR events. From analysis of the relationship between the hydrological responses and different meteorological factors, precipitation, temperature and radiation were identified as the key drivers that distinguish the hydrologic responses between AR and non-AR events. Lastly, analysis of ARs and total runoff at annual scale and April 1st snowpack and winter precipitation shows that ARs explain 30% to 60% of the interannual variance and sharpen the seasonality of water resources availability in the west coast mountain watersheds.
Assess the resource availability in liquid, high moisture, low moisture and inorganic waste stream in and around the Box Elder County in Utah.
In response to anticipated resource shortfalls related to the treatment and testing of COVID-19, many communities are planning to build additional facilities to increase capacity. These facilities include field hospitals, testing centers, mobile manufacturing units, and distribution centers. In many cases, these facilities are intended to be temporary and are designed to meet an immediate need. When deciding where to place new facilities many factors need to be considered, including the feasibility of potential locations, existing resource availability, anticipated demand, and accessibility between patients and the new facility. In this project, a facility location optimization model was developed to integrate these key pieces of information to help decision makers identify the best place, or places, to build a facility to meet anticipated resource demands. The facility location optimization model uses the location of existing resources and the anticipated resource demand at each location to minimize the distance a patient must travel to get to the resource they need. The optimization formulation is presented below. The model was designed to operate at the county scale, where patients are grouped per county. This assumption can be modified to integrate other scales or include individual patients.
Scaling up variational quantum eigensolver (VQE) algorithms to practical applications utilizing quantum advantage with noisy intermediate-scale quantum (NISQ) devices is challenging. The expansive cost-function evaluation and demanding optimization quickly exhaust the precious quantum resource available on NISQ devices. In this work, we discuss the appropriate VQE strategy suitable for multi-site interacting boson systems, for example, the Holstein model and the Rabi lattice model. We investigate the cost-function setup and the optimization tactics to utilize the limited quantum resource efficiently. Our strategy illustrates that the scalable VQE algorithms of interacting bosons have a promising future.
Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.