Introduction to Resilience Hubs: Alachua County, Florida
This presentation introduces definitions, services, and examples of resilience hubs to technical assistance partners in Alachua County, Florida.
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SEARCH · Engineering Papers
Results for “resilient power systems”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Advanced Measurements for Resilient Integration of Inverter-Based Resources: PROGRESS MATRIX Year-1 Report
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.
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Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids
This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.
Leila Chebbo↗
Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk
The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.
Power systems investment planning↗
Dynamic Modeling of a Kaplan Hydroturbine Using Optimal Parametric Tuning and Real Plant Operational Data
To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general.
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A Proposed Study of Liquid Air Energy Storage (LAES) with Fossil Generation
Explore the source record for details and available documents.
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Optimal Operation for Resilient and Economic Modes in an Islanded Alaskan Grid
Legacy energy management systems for distribution system or microgrids are typically driven by economics. During extreme events, resiliency can be defined as ability of the system to keep supplying critical loads. Resilient operation during extreme events (e.g. avalanche in Alaska) may require decision variables to be different and conflicting with economic operation. Operational objectives are driven by a complex consideration of the economic, reliable and resilient operation of the system: economic and reliable in normal operating state and resilient during extreme events. Challenge is to move between economic and resilient operation in optimal manner and setting up problem formulation and constraints specially with Distributed Energy Resources (DERs). In this paper, we focus on striking a balance between optimal economic and resilient operation using novel formulation and developed tool called Resiliency Enabled Energy System Operation Toolbox (RE-ESOT). Simulation results are provided for a real islanded grid in Alaska with battery energy systems.
50 EE - Wind and Water Power Program - Water (EE-4↗
Building the Case for Hybrid Energy Developments: Webinar Recording Day 2
INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.
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Building the Case for Hybrid Energy Developments: Webinar Recording Day 2
INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.
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Game Theoretic Orchestration for Cooperation among Power Distribution System Applications
The evolving transformation with the proliferation of distributed energy resources and advanced metering, necessitates advanced distribution systems to integrate and orchestrate a large number of grid-edge devices while also serving multiple system-level objectives such as resilience, decarbonization, equity and other system mandates. The parallel deployment and control of resources towards achieving diverse objectives may lead to conflicts between applications that want to control overlapping sets of device setpoints, potentially leading to oscillatory behavior and suboptimal performance. This work aims at leveraging game theoretic framework to drive cooperative behavior among competitive applications. The work proposes a weighted-consensus based game design to facilitate conflict resolution through consensus-building iterations for modular platform. Simulation-based evaluation on a sample test system demonstrates the performance the proposed deconfliction strategy in resolving operational conflicts and achieving close-to-optimal trade off among the applications. Results also compare the proposed strategy with a distribution optimization approach and illustrate it effectiveness in diverse apps regardless of their design while also incentivizing apps with flexible design.
Advanced distribution operations, cooperation, app↗
Optimal Operation and Impact Assessment of Distributed Wind for Improving Efficiency and Resilience of Rural Electricity Systems
This project aims to empower rural utilities by developing advanced optimization models and algorithms for effectively integrating distributed wind energy alongside battery storage and other distributed energy resources (DERs). The primary objectives are to reduce peak demand, ensure reliable emergency power supply, and regulate voltage and frequency. To address operational challenges, the project introduces innovative mitigation strategies and ultrafast assessment frameworks to evaluate the impacts of distributed wind and DERs on rural grids, offering actionable solutions to potential issues. Economic viability is assessed through cost-benefit analysis using real rural utility data, ensuring the practical application of the project outcomes.
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A Framework to Design Consumer-Centric Operational Strategies for Resilience Enhancement
Extreme temperature-related events like heat waves or cold snaps can significantly stress the power distribution grid as electricity demand spikes leading to brownouts or blackouts if not managed properly. In addition, such extreme events can exacerbate inequity, with vulnerable populations (for example, houses with poor insulation, located in non-critical zones, and lack of local resources) at greater risk. There is a growing deployment of distribution systems automation technologies such as advanced metering infrastructure, sensors, and automated control systems to enhance the visibility of the entire distribution grid while meeting resilience objectives. Here, this paper proposes a framework for investigating how automation can improve the distribution system's resilience and ensure customers' health and safety during such extremes. The proposed framework's efficacy will be demonstrated for the Electricity Reliability Council of Texas (ERCOT) during winter storm Uri with varying levels of distribution automation technologies such as feeder isolation using smart switches, remote outage signals using advanced meters, and comfort-aware outage using advanced analytics and communication. Simulation results show that an outage strategy involving advanced grid technology and communication can reduce the occupant exposure to severe cold by 93% and, simultaneously, reduce expected energy not served by 73.2% when compared against feeder isolation.
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A Real-Time Implementation and Validation of Federated Learning for Grid Services
Grid-edge devices are becoming increasingly important in the energy transition. Preserving privacy was not previously considered an important aspect for power grid operations, but with the increased proliferation of customer-owned assets, it is now an essential consideration. Several mechanisms have been proposed to provide privacy for non-utility owned assets in the power grid. Federated learning (FL) is one method gaining prominence in this area. Although FL has been used for other applications, such as auto-complete in phones, there has not been much investigation into whether these approaches are feasible for grid applications. In this work, we use a research platform with real-time simulators and hardware-in-the-loop capabilities to investigate how FL can be applied to grid-edge devices, and we present the potential grid services that can be derived for these devices. We discuss the computational challenges with deploying complex FL approaches, and we explore several grid services, including participation in retail electricity markets, voltage control, and resilience-driven reconfiguration.
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Power System Waveform Datasets for Machine Learning
The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.
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Hybrid Power Plants for Energy Resilience: A Case Study
As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.
17 WIND ENERGY↗
New Jersey Transit Grid Distributed Generation Program. Cybersecurity Design Assurance Assessment
Superstorm Sandy caused a major disruption to passenger-rail and other commuter systems throughout New York and New Jersey. To address this issue, New Jersey Transit (NJT) established the NJ TRANSITGRID project, an effort designed to power bus, ferry, and limited passenger-rail service during natural or man-made disasters. Given the importance of these transportation systems, NJT partnered with Sandia National Laboratories (Sandia) to assess the cyber-resilience of the information systems that monitor and control the electrical systems within the microgrid. The Sandia “tabletop” assessment is based on the most recent 20% design packages. From this assessment, the Sandia team identified several security areas that were undefined or did not implement industry best practices. Finally, the Sandia team presented possible follow-on assessment activities and recommended investigating multiple hardening technologies. Addressing these findings and adding state-of-the-art detection and mitigation technologies will help ensure the NJ TRANSITGRID is built with more comprehensive cyber-resilience features.
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Cosimulating Integrated Energy Systems With Heterogeneous Digital Twins: Matching a Connected World
Energy system integration promises in-creased resiliency and the unlocking of synergies, while also contributing to our goal of decarbonization. It is enabled by both old and new technologies, glued together with data and digital services. Hydrolyzers, heat pumps, distributed renewable generation, smart buildings, and the digital grid edge are all currently the subject of integration with the power system and the energy sector at large. To plan and operate such a multidisciplinary and multisectoral system properly, insight, tools, and expertise are all needed. Further, this is exactly where the state of the art fails to deliver: tools for integrated energy systems (IESs) are still in their infancy, and many times, even academia treats these sectors separately, producing experts in each of them but not across.
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