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This is the final report from the project. The report presents results from data and simulation based analyses of hydropower resources' contributions to grid reliability and resilience.
To gain a better understanding of the complex transients in a utility grid with a large number of solar PV installations coupled by PWM inverters during the protection and restoration period of the power grid, in this project a kW-level experimental system with dominating inverter-based resources and a hardware-in-loop simulation system with transmission and distribution models, as well as several SEL protective relays have been built and used. The major research findings of the project are summarized below in two perspectives: Experimental research: A self-organized ultra-high frequency solitary waveform is discovered and demonstrated in the hardware experimental system. Despite the familiarities, such a solitary waveform is distinct from any harmonics, transient, or resonance waves in that, it is 1) non-dispersive over time and space, 2) not associated with any active source or the linear superposition of sources, 3) half-cycle asymmetric, 4) not responding to filters or change of system characteristic resonance frequency, 5) ubiquitous as it occurs simultaneously everywhere in the system from DC supply, power lines, and the utility grid, and 6) explosive through tripping the protection or damaging susceptible devices or circuits. Analytical study: The nature of such a new waveform and an analytical explanation of its formation are studied based on related physics and non-linear science principles, with the following highlights: 1) such a new waveform follows the solutions to the Non-linear Schrödinger equation, so the classic linear perturbation theory is unable to explain or predict such a unique waveform as confirmed with the research team of RTDS; 2) the critical condition of occurrence of such a waveform is derived, which indicates that the breakings of solitary wave is a system synchronous issue between the utility grid in the 60Hz phasor domain and duty-ratio modulation of DC sources in the inverter switching frequency domain; 3) such a waveform carries energy mass so it could be detrimental; 4) such a waveform is deceiving as it is not readily detectable in the energy propagation direction by the primary protection equipment, while it is detrimental in the perpendicular direction of energy propagation, i.e. voltage direction. Thus, it has more likely challenges to the second primary equipment and devices, particularly at the weakest link and point such as aging insulation and inappropriate setting of susceptible devices. Therefore, such a waveform can be easily ignored in the aftermath investigation. The intellectual merits: The experimental discovery reveals certain unfamiliar transient phenomena that could challenge the integration of large-scale grid-connected solar PV installation during the group ride-through period of solar PV installations, commissioning of large-solar farms, or dramatic change of solar radiation conditions. Particularly, the research findings suggest that the occurrence of the unfamiliar transient phenomena is uniquely associated with the inverter-based solar PV installation, which is less likely to happen for rotary energy systems. The physics and non-linear science-based research work laid down an analytical path to the challenging transient stability problems including those that have been observed currently and future calls. Both experimental and analytical research results explain the limitation of many current research effects including some DoE research undertakings as well as possible solutions. The broader impacts: The research outcome of the project advances the understanding of the possible transient problems for the integration of inverter-based solar PV installation. It also highlights the theoretical and technical barriers for applying the conventional theory and technology to design and implement countermeasures against their adverse impacts of the large-scale solar PV installation and operation on grid reliability and security. More broadly, the research outcomes have shed some light on the open, fundamental challenges in the integration of large-scale solar photovoltaic energy into current and next-generation power grids across the country, and worldwide. The advanced physics and non-linear science-based analysis open up a path to harmonization of the renewables for societal energy needs via building a resilient and sustainable electric power infrastructure.
Over the past 50 years, the Renewable Energy Program at Sandia has advanced research in the field with a focus on three key goals; 1) reduce the cost, 2) improve resilience and reliability and, 3) decrease the regulatory burden of renewable energy. Sandia’s expertise, coupled with the Village of Questa’s expanding renewable energy portfolio, presents the opportunity to deploy the Labs’ deep science and engineering capabilities towards the energy goals of KCEC and the Village of Questa. Preliminary research efforts by Sandia technical staff has broadly identified early opportunities for further research, development, and demonstration in the emerging renewable energy segment of agrivoltaics. Agrivoltaics is an emerging and promising area of photovoltaics which entails land use considerations as well as concerns regarding landscape transformation, biodiversity, and ecosystem well-being. In recent years, agrivoltaics systems have been the subject of numerous studies due to their potential in the food-energy (and water) nexus. This document is a preliminary evaluation of the projects performance opportunities of agrivoltaics as a renewable energy technology strategy in the region of Questa, NM.
This report summarizes the activities conducted under the DOE-OE funded project DEOE0000897, Cyber Attack Resilient Flexible AC Systems – XFACTS. Hitachi Energy (HE), in collaboration with ABB Inc. (ABB), Bonneville Power Administration (BPA), University of Illinois at Urbana-Champaign (UIUC), Iowa State University (ISU), and University of Idaho (UI) pursued the development of a system of defense for Flexible Alternating current Transmission Systems against cyber-attacks (XFACTS). A FACTS substation enhanced with XFACTS defense mechanisms will be capable of mitigating cyberattacks especially those that seek to control electrical parameters like voltage or current and interrupt the power flow in AC lines. It empowers existing FACTS controllers and associated intelligent electronic devices to detect and mitigate malicious intents to depress system voltages, destabilize power flows, trip AC circuit breakers, corrupt currents, and voltages, even if the malicious commands and the measurements have correct syntax. The XFACTS functions utilize the physics of active power electronic systems, control and protection, electric power engineering principles, and state estimation to bring more in-depth cyber defense closer to the protected FACTS substation devices.
The overall goal of the project is to leverage robust graphical learning and phase measurement unit (PMU) data to learn the dynamic interactions of electrical grid components in order to improve the power system resilience. Specifically, the learned dynamic interaction graphs are utilized to detect known and unknown anomalous patterns in power systems, identify high-risk operational conditions, and enable risk-based cascading mitigation.
Water reclamation gets water from a variety of sources then treats and reuses it for beneficial purposes. Water reuse can provide alternatives to existing water supplies and be used to enhance water security, sustainability, and resilience.
This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.
This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.
Large hydropower, connected to the transmission system and typically possessing significant ability to balance grid frequency, has long been central to black start plans in regions where it is present. Small hydropower possesses most of the attributes required for black start but is often connected to distribution or sub-transmission systems and has less ability to balance grid frequency. Integrating energy storage such as batteries or ultracapacitors increases the combined asset’s ability to balance frequency. This asset integration enables a bottom-up grid restoration paradigm in which critical electric loads on the local distribution system can be powered even when the regional transmission system is down. This report presents a field demonstration conducted with Idaho Falls Power that proved this concept. The contribution of energy storage in restoring the small hydro-dominated distribution grid and its operational sensitivity across different control settings are further analyzed using high-fidelity simulations. Readers will learn about technical details on the field demonstration setup, energy storage contribution to black start, detailed simulation steps of islanded distribution grid restoration, and usage of field demonstration measurements for transient model refinements. Collectively this report points to a great opportunity for small hydropower to enhance resilience of local electric grids.
Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the Western System Coordinating Council (WSCC) 9-bus test system using synthetic multi-device outage scenarios. Towards integrating our modeling framework with real threats and more realistic power systems, we explore applying hybrid dynamics to power systems. Our work is applied to basic RL circuits with the ultimate goal of using the methodology to model protective tripping schemes in the grid. Finally, we survey mitigation techniques for HEMP threats and describe a GIS application developed to create threat scenarios in a grid with geographic detail.
This document is created as the deliverable on the project titled Timing Intrusion Management Ensuring Resiliency (TIMER). Its purpose is to define Specification Requirements that guide the development of various timing intrusion (TI) detection modules, as well as describe their implementation, validation, and future commercialization.
A workshop co-hosted by Pacific Northwest National Laboratory and Seattle City Light was held at the Seattle Municipal Tower on November 8–10, 2022. Participants from research organizations, utilities, professional associations, consultants, and government organizations attended. The purpose of the workshop was to develop a vision for addressing climate extremes that include improving forecasting and characterization, infrastructure resilience modeling, and investment planning and decision support. The workshop also aimed to provide a platform for sharing approaches and information and identifying possible collaborations.
As nearly every aspect of the electric power grid undergoes rapid change, measurement technologies that support grid operation and planning must evolve as well. Recent large-scale deployments of inverter-based resources (IBRs) have brought to the forefront the critical need for new measurement technologies. Though these IBRs are vital to achieving the nation’s clean energy goals, their rapid deployment has in some cases led to negative impacts on the reliability and security of the bulk power system (BPS). Advanced power system measurements, including synchronized phasor and waveform measurements, are key to making IBR integration secure and reliable. To this end, the Department of Energy (DOE) initiated a project in 2022 to develop advanced measurement capabilities and analytics that will accelerate adoption of IBRs while improving the reliability and resilience of the BPS. This report discusses a portion of the findings from the project’s first year, which focused on surveying existing measurement capabilities of partner utilities and comparing these capabilities with the requirements of applications that support IBR integration. This report also discusses how these findings will guide the development and demonstration of a set of applications in the project’s second year.
Los Alamos Neutron Science Center (LANSCE) and Dual-Axis Radiographic Hydrodynamic Test facility (DARHT) play a pivotal role in advancing scientific research and national security initiatives. As a premier facility for neutron science, LANSCE and DARHT provide invaluable insights into fundamental research, materials science, nuclear physics, high-energy physics, material science, and medical imaging. Existing (DC) power supply and pulse modulation technologies used in LANSCE and DARHT face limitations in terms of reliability, maintainability, and diagnostics which hinder the progress of these cutting-edge facilities. By proposing improvements to LANL accelerator facilities’ performance and addressing component failure, we aim to enhance scientific outcomes and minimize disruptions, allowing for more efficient and productive research activities while ensuring the continuity of valuable contributions to the user community. This proposal aims to revolutionize (DC) power supply and pulse power systems for future particle accelerators via the development of a resilient GaN-based power module with improved diagnostic and redundant submodules.
The University of Missouri’s Interdisciplinary Plant Group held its 38th annual symposium on May 25–27. In short, it was a resounding success! The air was almost electric because of a confluence of events that were particular to this symposium. Not only was there great enthusiasm from students attending their first ever scientific meeting, but this was coupled with excitement and delight exuded by many of our senior invited speakers, who were attending their first in-person meeting since the Covid pandemic began. After a long planning period, and with the ongoing pandemic injecting uncertainty, it was gratifying to see the symposium unfold with such excitement and engagement among participants. The symposium was organized to address the grand challenge of “Enhancing the Resilience of Plant System to Climate Change”, by bringing together experts at the intersections of multiple disciplines to educate our community on the opportunities to develop innovative mitigation strategies and to adapt plants to climate change.
The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.
In the past, electricity was produced by power plants on the transmission system and distributed to customers through substations. Now, distributed energy resources and dynamic loads are penetrating distribution grids, with their own energy production, resilience, and reliability challenges separate from the traditional model.
The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This chapter focuses on housing weatherization and access to cooling as means to achieve more equitable resilience to heat waves during unplanned power outages. Specifically, NREL used weather, housing, and socioeconomic data to characterize LA's residential building stock. We?developed a residential building stock model to simulate the energy use of 50,000 dwellings representing the diversity of housing types, appliances, climate zones, and household incomes across Los Angeles. We then simulated and evaluated the impacts of 10 building envelope and cooling upgrades on indoor temperature - a main cause of heat-induced health risks-over a 4-day power outage during a heat wave. We examined occupant exposure to extreme heat and how heat exposure changes with each upgrade across income, tenure (renter/owner status), building type, and disadvantaged community (DAC) status. We also examined upgrade costs and utility bills. Based on the results of our analysis and community guidance, we identified building envelope upgrades and cooling strategies that could save lives and maintain safe home temperatures for LA's low-income households in the event of a planned or unplanned power outage during a summer heat wave. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.