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DOE OSTI2021

Detecting Electrical Anomalies via Overlapping Measurements

As cyber-attacks against critical infrastructure become more frequent, it is increasingly important to be able to rapidly identify and respond to these threats. Therefore, we are investigating using multiple independent systems with overlapping electrical measurements to more rapidly identify anomalies. While prior research has explored the benefits of fusing measurements, the possibility of overlapping measurements from an existing electrical system has not been investigated. To that end, we explore the potential benefits of combining overlapping measurements both to improve the speed/accuracy of anomaly detection and to provide additional validation of collected measurements.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Avista’s Shared Energy Economy Model Pilot: A Techno-economic Assessment

As part of the second round of the Washington Clean Energy Fund, Avista Corp received a $3.5 million matching grant in support of a shared energy economy project to test the integration of energy assets–from rooftop solar and battery storage to building energy management systems–that can be shared and used for multiple purposes. The goal of this project is to demonstrate how both the customer and the utility can benefit from this shared energy economy model and demonstrate that the electric grid can become more reliable, efficient, resilient, and flexible. Pacific Northwest National Laboratory was engaged by the U.S. Department of Energy and the Washington State Department of Commerce to work with Avista in assessing the benefits of the shared energy economy model. This report documents the techno-economic assessment of the shared energy economy model, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, and case studies and analysis results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Rosario Strait Tidal Energy plus Energy Storage — Preliminary Economic Assessment

The Orcas Power and Light Co-op (OPALCO) is a non-profit utility that provides energy services to approximately 11,700 customers across 20 islands in San Juan County, Washington. OPALCO is developing a diverse set of local renewable energy resources to reduce dependence on mainland Washington State for energy and to reduce the regional need for fossil-fueled power. OPALCO’s load doubles in winter but solar production is roughly 20% of levels reached in summer periods. Tidal energy is strong year-round, night and day, and is predictable, requiring much less storage to firm it. To assess the technical and economic feasibility of tidal power, working in combination with multiple other distributed energy resources (DERs), Argonne National Laboratory (Argonne) employed an optimization model to evaluate several economic benefits associated with varying scales of tidal power of between 2.4 and 9.6 megawatts (MW) and other DERs. In addition to existing photovoltaic (PV) and battery energy storage system (BESS) resources located on Decatur Island, Argonne also evaluated the addition of a BESS on southern Orcas Island with power and energy capacities ranging from 1-4 MW and 2-4 hours in storage duration.

25 ENERGY STORAGE↗

DOE OSTI2024

DEReliction: A Cybersecurity Vulnerability Assessment Methodology for Distributed Energy Resources

With the increasing integration of Distributed Energy Resources (DER) into the electric grid, maintaining grid reliability and resilience requires that these devices remain secure. This paper discusses a cybersecurity vulnerability assessment methodology that incorporates best practices from Sandia National Laboratories, SANS Institute, OWASP Foundation, and other web and Internet of Things (IoT) penetration testing (“pen testing”) programs, courses, and frameworks for assessing the security posture of devices. The methodology involves five sequential steps: (1) Collect Public Information, (2) Extract Hardware Details, (3) Inventory Software Components, (4) Identify Vulnerabilities, and (5) Test Vulnerabilities. Each step uncovers potential weaknesses in both hardware and software components of DER devices, considering adversary tactics, techniques, and procedures (TTPs), and potential attack vectors along the way. The results from the execution of this method on multiple residential- and small commercial-scale photovoltaic (PV) inverters reveled hardware and software vulnerabilities, which highlight the benefit of taking a methodical approach to discover vulnerabilities. While the specific vulnerability details are not shared here, a generalized overview of findings underscore the importance of robust security assessments for DER devices. Adoption of an assessment framework of this kind will identify and mitigate cybersecurity threats and bolster the resilience of DER-integrated electric grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2

This study documents clouds simulated by the Energy Exascale Earth System Model (E3SM) version 2 (E3SMv2) and attempts to understand what causes the model behavior change in clouds relative to E3SMv1. This is done by analyzing the last 30-year (1985–2014) data from the 165-year historical simulations using E3SMv1 and v2 and four sensitivity tests to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (CLUBB), microphysical parameterization (MG2), and deep convection scheme (ZM), as well as model physics changes in convective triggering. It is shown that E3SMv2 significantly improves the simulation of subtropical coastal stratocumulus clouds (Sc) and clouds with optical depth larger than 3.6 over the stratocumulus to cumulus transition regimes, where Shortwave Cloud Radiative Effect (SWCRE) is also improved, and the Southern Ocean (SO) while seeing an overall slight degradation in low clouds over other tropical and subtropical oceans. The better performance in E3SMv1 over those regions is partially due to error compensation between its simulated optically thin and intermediate low clouds for which E3SMv2 actually improves simulation of optically intermediate low clouds. Sensitivity tests indicate that the changes in low clouds are primarily due to the tuning made in CLUBB. The impact of the ZM tuning is mainly on optically intermediate and thick high clouds, contributing to improved SWCRE and Longwave Cloud Radiative Effect (LWCRE). The impact of the MG2 tuning and the new convective trigger is primarily on the high latitudes and the Southern Ocean (SO). They have a relatively smaller impact on clouds than do the CLUBB and ZM tunings. This study offers additional insights about clouds simulated in E3SMv2 by utilizing multiple data sets and the COSP diagnostic tool as well as through sensitivity tests. The improved understanding will benefit the future E3SM developments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

History of Ethanol Fuel Adoption in the United States: Policy, Economics, and Logistics

Ethanol has the achieved the greatest market share of all the alternative transportation fuels that have been researched, developed, and deployed in the US. There are multiple lessons to be learned from the history of ethanol adoption that can be applied to future fuels and products. Ethanol has replaced portions of gasoline in three main blend levels, with corresponding vehicles, equipment, benefits, and policies. The first is E10, which has replaced nearly all pure gasoline (E0) sold in the United States today (U.S. Energy Information Administration 2015). This was brought about through generations of policies that were motivated by multiple factors related to engine performance, energy security, health, air quality, and climate protection. Ethanol's high octane has been a consistent driver of the fuel because this enables higher performance engines. Early policies (1973-1979) were largely motivated by the desire to reduce dependence on petroleum sourced from members of the Organization of the Petroleum Exporting Countries, or OPEC. Fuel policy in the 1980s was largely in pursuit of promoting octane number enhancers that could replace lead. Criteria pollutant reduction was the overarching goal of actions taken from 1990 to 2005. From 2005 to the present, fuel policy has been largely motivated by energy security and climate protection goals. These policies were usually technology-agnostic and promoted multiple fuels and additives during each period. However, ethanol is the only fuel identified that is cost effective with qualities that enabled it to prosper in the policy environments of all four of these periods.The second market for ethanol is via 85% ethanol (E85). This fuel has the advantage of a greater concentration of ethanol but the disadvantage of not being compatible with regular gasoline vehicles. Instead, it can only be used in flexible-fuel vehicle (FFVs), which can use every blend level from E0 to E85. Therefore, much of the effort to increase E85 consumption has been aimed at incentivizing automakers to manufacture FFVs, drivers to purchase FFVs, and fueling stations to equip themselves to sell E85.The third, and newest, market through which ethanol is consumed is 15% ethanol (E15), which builds upon the E10 market to increase ethanol consumption by common gasoline vehicles. Efforts to create the E15 market consisted largely of testing vehicles and refueling equipment for compatibility, creating waivers to allow the use of E15, placing requirements on E15 retailers, and incentivizing retailers to equip themselves to purchase E15.

09 BIOMASS FUELS↗

DOE OSTI2021

Winter roosting ecology of tricolored bats ( Perimyotis subflavus ) in trees and bridges

Abstract Tricolored bats (Perimyotis subflavus) that roost in subterranean hibernacula have experienced precipitous declines from white-nose syndrome (WNS); however, understudied populations also use during winter non-subterranean roosts such as tree cavities, bridges, and foliage. Our objectives were to determine winter roost use by tricolored bats in an area devoid of subterranean roosts, determine roost microclimates to relate them to growth requirements of the fungal causal agent of WNS, and determine habitat factors influencing winter tree selection. From November to March 2017–2019, we used radiotelemetry to track 15 bats to their day roosts in the upper Coastal Plain of South Carolina and recorded microclimates in accessible tree cavities and bridges. We also characterized habitat and tree characteristics of 24 used trees and 153 random, available trees and used discrete choice models to determine selection. Roost structures included I-beam bridges, cavities in live trees, and foliage. Bridges were warmer and less humid than cavities. Roost temperatures often were amenable to fungal growth (< 19.5°C) but fluctuated widely depending on ambient temperatures. Bats used bridges on colder days (8.7°C ± 5.0 SD) and trees on warmer days (11.3°C ± 5.4). Bats selected low-decay trees closer to streams in areas with high canopy closure and cavity abundance. Bats also appeared to favor hardwood forests and avoid pine forests. Our results suggest that access to multiple roost microclimates might be important for tricolored bats during winter, and forest management practices that retain live trees near streams and foster cavity formation in hardwood forests likely will benefit this species. Our results also suggest tricolored bats using bridge and tree roosts might be less susceptible to WNS than bats using subterranean hibernaculum roosts. Thus, forests in areas without subterranean hibernacula in the southeastern United States that support bats during winter might represent important refugia from WNS for multiple species.

Zoology↗

DOE OSTI2022

Analysis of Distributed Energy Storage as a Core Grid Infrastructure via Production Cost Modeling

Energy storage plays a pivotal role in enabling power system operation with more flexibility and resilience. Unlike current practice that considers energy storages as attached ancillary devices, this paper focuses on storages as a core infrastructure by looking at their spatial distribution in the system. A sensitivity analysis based on production cost modeling is conducted to demonstrate the benefits of distributed energy storages. First, the modeling of energy storages in production cost modeling is presented. Second, potential optimal locations of distributed energy storages in a power system are discussed. Finally, multiple scenarios with various numbers and locations of additional distributed energy storages in the WECC 2030 model are created. The production cost modeling results of these scenarios show that distributed energy storages have higher utilization compared to the centralized ES units and therefore provide significantly more benefits in terms of reduction in generation cost, emission cost, and volatility of location marginal prices. A saturation effect is observed suggesting the selection of optimal locations will further improve the benefits.

Nguyen, Quan H.↗

DOE OSTI2020

Application of Numerical Atmospheric Models

In this study, a numerical modeling framework for simulating extreme storm events was established using the Weather Research and Forecasting (WRF) model. Such a framework is necessary for the derivation of engineering parameters such as probable maximum precipitation that are the cornerstone of large water-management infrastructure design. Here, this framework was built based on a heavy storm that occurred in Nashville, Tennessee (U.S.), in 2010, and verified using two other extreme storms. To achieve the optimal setup, several combinations of model resolutions, initial/boundary conditions (IC/BC), cloud microphysics, and cumulus parameterization schemes were evaluated using multiple metrics of precipitation characteristics. The evaluation suggests that WRF is most sensitive to the IC/BC option. Simulation generally benefits from finer resolutions up to 5 km. At the 15 km level, NCEP2 IC/BC produces better results, whereas NAM IC/BC performs best at the 5 km level. The recommended model configuration from this study is: NAM or NCEP2 IC/BC (depending on data availability), 15 km or 15–5 km nested grids, Morrison microphysics, and Kain-Fritsch cumulus schemes. Validation of the optimal framework suggests that these options are good starting choices for modeling extreme events similar to the test cases. This optimal framework is proposed in response to emerging engineering demands of extreme storm event forecasting and analyses for design, operations, and risk assessment of large water infrastructures.

Chen, Xiaodong↗

DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗

DOE OSTI2026

What Can We Learn from US National Transmission Studies?

Multiple US regions have announced new electricity transmission plans, are actively revisiting the future role of transmission, and could benefit from findings from recent national-scale transmission studies. A review of seven national studies shows that significant transmission expansion is cost-optimal for serving new demand, integrating additional resources, and supporting grid reliability. A majority of the reviewed scenarios, which span a range of policy and technology assumptions, include at least a doubling of the transmission system by 2050. The national studies find substantial net system cost savings from transmission expansion and high benefit-cost ratios for transmission investments. The results are derived through multi-value, nationally coordinated, co-optimized, multi-scenario modeling, which is an approach that could yield lower-cost pathways compared to current industry planning practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Antiproton bounds on dark matter annihilation from a combined analysis using the DRAGON2 code

Abstract Early studies of the AMS-02 antiproton ratio identified a possible excess over the expected astrophysical background that could be fit by the annihilation of a weakly interacting massive particle (WIMP). However, recent efforts have shown that uncertainties in cosmic-ray propagation, the antiproton production cross-section, and correlated systematic uncertainties in the AMS-02 data, may combine to decrease or eliminate the significance of this feature. We produce an advanced analysis using the DRAGON2 code which, for the first time, simultaneously fits the antiproton ratio along with multiple secondary cosmic-ray flux measurements to constrain astrophysical and nuclear uncertainties. Compared to previous work, our analysis benefits from a combination of: (1) recently released AMS-02 antiproton data, (2) updated nuclear fragmentation cross-section fits, (3) a rigorous Bayesian parameter space scan that constrains cosmic-ray propagation parameters.We find no statistically significant preference for a dark matter signal and set strong constraints on WIMP annihilation tobb̅, ruling out annihilation at the thermal cross-section for dark matter masses below ∼ 200 GeV. We do find a positive residual that is consistent with previous work, and can be explained by a ∼ 70 GeV WIMP annihilating below the thermal cross-section. However, our default analysis finds this excess to have a local significance of only 2.8σ, which is decreased to 1.8σwhen the look-elsewhere effect is taken into account.

Astronomy & Astrophysics↗

DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗

DOE OSTI2024

Intermediate Scale Test Platform Conceptual Design and Hybrid Resin Performance Evaluation Recommendations

Recent laboratory evaluations demonstrated that further testing of alternative hybrid resins is warranted for their potential use as part of the 200 West Area (200W) pump-and-treat (P&T) remedy. 200W P&T has been treating technetium (Tc) since 2012 and uranium (U) since 2015 via two separate ion exchange (IX) treatment units: a single treatment train (i.e., a three-vessel system) for the removal of uranium utilizing a gel strong base anion (SBA) exchange resin, Dowex 21K; and two parallel treatment trains for the removal technetium using a gel SBA resin, Purolite® A532E. While these resins have been removing these contaminants effectively, new hybrid resins can provide additional benefits of further optimizing the IX treatment unit capacities and addressing additional treatment needs (e.g., Cr(VI)) by removing multiple contaminants simultaneously and/or through specific design configurations. Some of these resins also demonstrated some level of removal for I-129 which would be significant for expansion of the P&T into the 200 East Area. This document provides design recommendations for an intermediate scale test platform (ISTP) to evaluate alternative hybrid IX resin performance under conditions approaching field scale and using 200W P&T process groundwater. Results from the ISTP will provide the technical basis required to inform alternative IX resin selection in future 200W P&T facility operations where the treatment of multiple contaminants of concern may be required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

Guo, Longxiang↗

DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗