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DOE OSTI2024

The circular bioeconomy: a driver for system integration

Background: Human and earth system modeling, traditionally centered on the interplay between the energy system and the atmosphere, are facing a paradigm shift. The Intergovernmental Panel on Climate Change’s mandate for comprehensive, cross-sectoral climate action emphasizes avoiding the vulnerabilities of narrow sectoral approaches. Our study explores the circular bioeconomy, highlighting the intricate interconnections among agriculture, forestry, aquaculture, technological advancements, and ecological recycling. Collectively, these sectors play a pivotal role in supplying essential resources to meet the food, material, and energy needs of a growing global population. We pose the pertinent question of what it takes to integrate these multifaceted sectors into a new era of holistic systems thinking and planning. Results: The foundation for discussion is provided by a novel graphical representation encompassing statistical data on food, materials, energy flows, and circularity. This representation aids in constructing an inventory of technological advancements and climate actions that have the potential to significantly reshape the structure and scale of the economic metabolism in the coming decades. In this context, the three dominant mega-trends—population dynamics, economic developments, and the climate crisis—compel us to address the potential consequences of the identified actions, all of which fall under the four categories of substitution, efficiency, sufficiency, and reliability measures. Substitution and efficiency measures currently dominate systems modeling. Including novel bio-based processes and circularity aspects might require only expanded system boundaries. Conversely, paradigm shifts in systems engineering are expected to center on sufficiency and reliability actions. Effectively assessing the impact of sufficiency measures will necessitate substantial progress in inter- and transdisciplinary collaboration, primarily due to their non-technological nature. In addition, placing emphasis on modeling the reliability and resilience of transformation pathways represents a distinct and emerging frontier that highlights the significance of an integrated network of networks. Conclusions: Existing and emerging circular bioeconomy practices can serve as prime examples of system integration. These practices facilitate the interconnection of complex biomass supply chain networks with other networks encompassing feedstock-independent renewable power, hydrogen, CO 2 , water, and other biotic, abiotic, and intangible resources. Elevating the prominence of these connectors will empower policymakers to steer the amplification of synergies and mitigation of tradeoffs among systems, sectors, and goals.

09 BIOMASS FUELS↗
DOE OSTI2023

Modular chip-integrated photonic control of artificial atoms in diamond waveguides

A central goal in creating long-distance quantum networks and distributed quantum computing is the development of interconnected and individually controlled qubit nodes. Atom-like emitters in diamond have emerged as a leading system for optically networked quantum memories, motivating the development of visible-spectrum, multi-channel photonic integrated circuit (PIC) systems for scalable atom control. However, it has remained an open challenge to realize optical programmability with a qubit layer that can achieve high optical detection probability over many optical channels. Here, we address this problem by introducing a modular architecture of piezoelectrically actuated atom-control PICs (APICs) and artificial atoms embedded in diamond nanostructures designed for high-efficiency free-space collection. The high-speed four-channel APIC is based on a splitting tree mesh with triple-phase shifter Mach–Zehnder interferometers. This design simultaneously achieves optically broadband operation at visible wavelengths, high-fidelity switching (>40dB) at low voltages, submicrosecond modulation timescales (>30MHz), and minimal channel-to-channel crosstalk for repeatable optical pulse carving. Via a reconfigurable free-space interconnect, we use the APIC to address single silicon vacancy color centers in individual diamond waveguides with inverse tapered couplers, achieving efficient single photon detection probabilities (∼15%) and second-order autocorrelation measurements g (2) (0)<0.14 for all channels. The modularity of this distributed APIC–quantum memory system simplifies the quantum control problem, potentially enabling further scaling to thousands of channels.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Functional All-Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President’s Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all-hazards perspective remains a difficult problem. In this paper we introduce the All-Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross-sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

02 PETROLEUM↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗
DOE OSTI2020

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗
DOE OSTI2021

Managing Solar Photovoltaic Integration in the Western United States: Resource Adequacy Considerations

This study examines the impact of reserve margin-based reliability assessment, as commonly used in capacity expansion models, on planning resource-adequate power systems under high penetrations of solar photovoltaics (PV). As a generation resource, PV is operationally different from the conventional dispatchable resources for which most capacity expansion models were designed. The question this study attempts to answer is whether large amounts of PV on a system (in this case, the Western Interconnection of North America) would bias the results of conventional reserve margin-based capacity expansion modeling towards an over- or under-provisioning of resource adequacy. This analysis used NREL’s Resource Planning Model (RPM) for capacity expansion modeling and NREL’s Probabilistic Resource Adequacy Suite (PRAS) for resource adequacy assessment. RPM uses a reserve margin requirement to enforce resource adequacy. PRAS, a collection of tools for studying the resource adequacy of power systems and the adequacy contributions of individual resources on a probabilistic basis, was used to compute multiple resource adequacy metrics across a number of simulated scenarios and system representations with differing regional detail. In all cases, including high PV penetrations (up to 33% annual generation from PV, interconnection-wide), RPM was able to produce resource-adequate systems as measured by normalized expected unserved energy and loss-of-load expectation results from PRAS. The accuracy of reserve margin approaches depends heavily on the underlying assumptions informing the capacity credit assigned to variable and energy-limited resources, particularly when such resources are abundant in the modeled system. RPM’s standard methodology for estimating variable and flexible resources’ capacity contributions, which is based on the top 100 hours of net load, did not appear to systematically undervalue or overvalue variable generation relative to a more rigorous equivalent firm capacity assessment using PRAS, although both over- and under-valuations were observed in specific scenarios. In the worst cases, the top 100 hour method underestimated the equivalent firm capacity of PV by two percentage points, and overestimated the equivalent firm capacity of PV by five percentage points. Calculating capacity contributions based on the top 10 hours of net load systematically underestimated equivalent firm capacities at more modest PV penetrations, but was often a better approximation of equivalent firm capacity than the existing 100-hour approach in scenarios with higher PV penetrations.

14 SOLAR ENERGY↗
DOE OSTI2021

Rapid QSTS Simulations for High-Resolution Comprehensive Assessment of Distributed PV

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

An Overview of Issues Related to IEEE Std 1547-2018 Requirements Regarding Voltage and Reactive Power Control

The revised Institute of Electrical and Electronics Engineers (IEEE) 1547 Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces (IEEE Std 1547-2018) was published in April 2018. This standard is one of the foundational documents in the United States needed for integrating distributed energy resources (DERs), including solar energy systems, with the electric distribution grid. The revised standard contains 11 chapters (clauses) and 8 annexes that comprise 136 pages. The revision is significantly different from the 2003 version, and it contains new concepts and new technical requirements. Each clause specifies information or requirements that apply to certain aspects important to the interconnection of DERs to the electric power system. Implementing the requirements necessitates a careful study of the underlying technical concept and requires the appropriate information required to calculate relevant settings and configurations. Various stakeholders have different roles in implementing the standard, and portions of the standard are directed toward a specific audience who must possess specialized information and technical training to use and apply the requirements. This document aims to (1) summarize a specific portion of the standard as concisely as possible and (2) provide the reader with introductory knowledge and information to support the utilization of the requirements.

14 SOLAR ENERGY↗
DOE OSTI2021

Background Information on the Power Quality Requirements in IEEE Std 1547-2018

The revised Institute of Electrical and Electronics Engineers (IEEE) 1547-2018, Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces, was published in April 2018. This standard is one of the foundational documents in the United States needed for integrating distributed energy resources (DERs), including solar energy systems, and energy storage systems with the electric distribution grid. The revised standard contains 11 chapters (clauses) and 8 annexes that comprise 136 pages. The revision is significantly different from the 2003 version, and it contains new concepts and new technical requirements. Each clause specifies information or requirements that apply to certain aspects that are important to the interconnection of DERs to the electric power system. Implementing the requirements necessitates a careful study of the underlying technical concepts and requires appropriate information to calculate relevant settings and configurations. This document provides informative material on the requirements related to electrical power quality in IEEE Std 1547-2018, with the intent to equip the reader with basic knowledge and background information to improve understanding and use of the requirements specified.

14 SOLAR ENERGY↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Machine Learning Guided Operational Intelligence from Synchrophasors (Final Report)

Schweitzer Engineering Laboratories (SEL) and Oregon State University (OSU) received over 27 terabytes of electrical power system phasor measurement unit (PMU) data for the Eastern, Western, and ERCOT interconnections. The dataset includes measurements spread across 446 PMUs from early 2016 to mid 2018 depending on the interconnect. The full dataset was split into a training and test (holdout) dataset by PNNL. All data was received in the Apache Parquet format. The overarching goal of this project is to develop and execute a strategy to mitigate data anomalies, perform analysis on the dataset, and detect anomalous events in the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Minimizing Cr-Evaporation from Balance Of Plant Components By Utilizing Cost-Effective Alumina-Forming Austenitic Steels

A solid oxide fuel cell (SOFC) is a clean and efficient energy conversion device. The development of intermediate-temperature SOFCs has made it preferable to use metallic interconnects (MICs) to greatly reduce the cost and significantly increase the efficiency compared to ceramic interconnect materials. However, gaseous chromium species will evaporate from the chromium-containing layer formed on the surface of commonly used MICs and balance of plant (BoP) components. Volatile chromium species have been shown to form solid deposits which poison the cathodes of SOFCs, causing drastic cell performance degradation and thereby limiting commercialization. In order to alleviate the Cr poisoning and achieve long-term high performance of SOFC stacks, various Al 2 O 3 -forming austenitic (AFA) stainless steels applied at different temperatures are evaluated in this project. Based on our Phase I results (500 h operation), it is shown that on the AFAs, an alumina-based protective layer forms under high temperature that is invulnerable to water vapor effects and suppresses the diffusion of chromium and manganese which can prevent the generation of spinels on the alloy surface. Therefore, 310S, OC4 and OC5 at 800 °C and 625, OC11 and OC11LZ at 900 °C are selected to be tested in the Phase II long-term operation. From the results, we find that besides the lower Cr evaporation rates and better oxidation resistance of AFAs than benchmark alloys after short-term (500 h) operation, AFAs also possess the sturdy and compact alumina layer after a long-term operation (5000 h). A protective oxide layer is of great importance for the long-term high-temperature operation of structural materials. The high-temperature oxidation behavior of AFAs and commercial 310S and 625 alloys in Air + 10% H 2 O at 800 °C and 900 °C is systematically investigated. Severe breakaway oxidation and minor spallation are observed for the 310S and 625 after the long-term operation, while the AFAs show high oxidation resistance. It is found that the formation of a continuous alumina layer could greatly prevent the volatilization of chromium vapors. In addition, the corresponding evolving models are discussed. Chromium evaporation from BoP components in high-temperature environment could severely deteriorate the electrochemical performance of SOFC. Several methods were applied to evaluate the Cr evaporation rates of BoP after 500 h exposure at 800 °C to 900 °C in air with 10% H 2 O. An optimal method was designed to exclude the effect of silicon (Si) deposits from quartz tube and sodium (Na) deposits from the sodium carbonate on the oxidation process and the chemical interaction between Cr gaseous species and alumina tube which could provide further quantitative correlation of the evaporated Cr species quantities and degradation rates of SOFC. Based on the great performance of AFAs after long-term operation, AFAs are assembled with Anode-supported cells (ASC) to investigate the the Cr deposition of anode-supported cell under a constant current density of 0.5 A cm -2 at 800 °C with AFA alloys compared with commercial alloys. In addition, the anodic and cathodic processes are deconvoluted by distribution of relaxation times (DRT) method which are comprehensively discussed. It is found out that the voltage exhibited a slight decrease of 5.09 % and 1.54 % in the presence of OC11 and OC11LZA alloy, respectively. However, a considerable decrease of 22.14 % and 12.06 % was determined in the presence of 310S and 625 alloy, respectively. AFAs possessing the low-cost, low Cr evaporation rates and the high oxidation resistance will make it of great potential to replace the existing BoP components.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization and Solder Joints in Photovoltaic Modules

Interconnect, solder, and metallization-related failures are the primary reason for premature Si-based photovoltaic panel failure [1, 2] and can lead to a cascade of other failures, including thermal events. This combination of high severity and high occurrence means that understanding interconnect-related failures is key to the long-term health of the photovoltaic industry, and it is crucial that the industry adopt efficient and effective tests that aid in design-for-reliability and manufacturing quality control.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluating the Impact of Residential Solar Contract Cancellations in the United States

The residential solar photovoltaic (PV) market in the United States is growing, despite frequent customer cancellations. Contract cancellations result in lost time and costs that must be borne by installers, often in the form of higher "soft costs" (i.e., non-equipment costs). These costs are often passed on to customers who successfully install PV systems. To date, few studies have attempted to estimate national cancellation rates or model the impacts on installed system costs. In this report, we utilize an installer-provided dataset of 199,665 residential PV-only projects representing about 10% of U.S. installs each year from 2017-2019. With this data, we evaluate cancellation rates and trends from contract signing to install. Next, by leveraging NREL's soft cost model for residential solar, we estimate installer spending through each phase of the pre-install process. Applying our findings on cancellation timelines and rates, we then estimate the potential cost impacts to successful installs from contract cancellations. Our work suggests that the rates and impacts of contract cancellations have been previously underestimated. Namely, we find that cancellations/unsuccessful project are; more common than previous estimates suggest; occur earlier in project timelines, though a significant number happen even after permit/ATB approvals; and contribute significantly to customer acquisition and other soft costs. We also find that cancellation do not appear to be driven by review delays in permitting and interconnection applications.

14 SOLAR ENERGY↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

14 SOLAR ENERGY↗
DOE OSTI2020

Regional Oscillation Source Localization: Implementation in the ESAMS Tool

In recent years, utilities have made great strides in the use of high-speed wide-area measurements collected from within their footprints. However, it is increasingly clear that analyses at wider scales are needed. On January 11, 2019 an oscillation with significant amplitude observable across the Eastern Interconnection persisted for approximately 18 minutes. The generator responsible for the oscillation was only identified after it tripped itself offline. This event, along with several other similar events, demonstrated the need for increased coordination among utilities to effectively mitigate system-wide oscillations and avoid unnecessary or detrimental operator actions. In response to this need, the Eastern Interconnection Situational Awareness Monitoring System (ESAMS) was upgraded to allow the region where an oscillation is originating to be identified. This report documents the algorithms developed for this task and describes how they were integrated into the ESAMS tool. Results from simulation- and measurement-based tests demonstrate the reliability of the approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2022

Alternative Approaches to Traditional Net Energy Metering

Most jurisdictions in the United States originally implemented net energy metering (NEM) tariffs to support the deployment and interconnection of distributed generation (DG) resources (e.g., rooftop solar photovoltaic systems). Since then, NEM has proven effective in promoting adoption of DG resources. Recently, due to concerns about sufficient recovery of utilities’ revenue requirements and cost-shifting, there is increasing interest in—or statutory requirements to pursue—alternative compensation approaches, especially in U.S. states and territories with robust growth in distributed solar. Recent increases in other forms of distributed energy resources (DERs) that can potentially send power to the distribution grid (e.g., distributed battery energy storage system (BESS)) are further driving compensation reforms. This brief provides an overview of design elements associated with alternative approaches to traditional NEM, summarizes common arguments for and against them, and identifies implementation issues that utilities may need to address. Although this brief may be most useful in jurisdictions that are interested in or required to move beyond NEM, it is also applicable to those jurisdictions that have already done so—and are looking to further implement reforms to their existing compensation mechanisms. In the broadest sense, there are three primary tariff-related components when interconnecting a DER onto the local utility’s distribution system (adapted from Zinaman et al., 2017): 1. Metering and Billing Arrangements: How utilities measure and bill electricity consumption and production. 2. DER Export Tariff Design: The structure under which utilities compensate customers for electricity they export to the grid. 3. Consumption Tariff Design: The structure under which customers pay for electricity they consume from the grid. When implementing changes to any of these primary tariff-related components, there are likely implications for a utility’s metering system, billing system, and other technology systems. Where applicable, this brief explicitly identifies such implementation challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗