The Grid Optimization Competition Benchmark Algorithm [Slides]
Abstract not provided.
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Blockchain for Fault-Tolerant Grid Operations Version 2.0
This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.
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Discovery of False Data Injection Attacks on Power Grid Frequency Controllers with Reinforcement Learning [Poster]
While inverter-based DER (distributed energy resources) are instrumental to integrating renewable energy into the power grid, they reduce the grid's mechanical inertia, thereby increasing the risk of frequency instabilities. To compensate for frequency instability risks, the grid must also undergo a transformation to include digital technologies that allow for two-way communication between the utility and customers. The current and future state of the power grid allows for building a cleaner energy landscape. However, the grid may also become vulnerable to novel cyber threats. To preemptively protect the power grid against elaborate cyber-attacks, we propose to discover potential threats via reinforcement learning. In this work, the focus is on studying false data injection attacks that target the control logic of frequency controllers. We show that a reinforcement learning agent can successfully discover how to best inject false data into linear droop controllers.
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Hybrid Interior-Point/Active-Set SCOPF Algorithms Exploiting Power System Characteristics
These protected data were produced under agreement no. with the U.S. Department of Energy and may not be published, disseminated, or disclosed to others outside the Government until 5 years after development of information under this agreement, unless express written authorization is obtained from the recipient. Upon expiration of the period of protection set forth in this Notice, the Government shall have unlimited rights in this data. This Notice shall be marked on any reproduction of this data, in whole or in part.
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Supply Chain Management in Cyber Grid Guard Framework
Grid modernization has impeded innovative power grid applications and energy resources that are increasingly distributed. Blockchain/distributed ledger technology (DLT) has the potential to enhance the resilience of the electric infrastructure, particularly in a decentralized and distributed environment. The benefits of blockchain are to ensure asset information and lifecycle events are secure and traceable and identify potential malicious modification of data. Oak Ridge National Laboratory (ORNL) has developed a framework, Cyber Grid Guard (CGG), incorporating blockchain. The system implements a low-energy, fast, and robust enhancement to system trustworthiness within and across electric grid systems, including substations, control centers, and metering infrastructures. Currently, one of the major concerns is supply chain attacks. There have been several recent attacks that have significantly impacted critical infrastructures and organizations around the world. This document focuses on how CGG can be used to address the supply chain issue.
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Sienna Modeling Framework [Slides]
NREL's Sienna modeling framework effectively builds, solves, and analyzes the scheduling problems and dynamic simulations of quasi-static infrastructure systems. It uses a modular framework to answer different questions about future energy systems, fundamentally advancing the nation's ability to model individual and integrated infrastructure systems at a range of spatial and temporal scales. This presentation will include NREL power grid researcher Clayton Barrows.
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Accelerated Data Analytics for Power-System Time-Series (ADAPT) [Final Project Report]
Over the past decade, electric utilities have made significant progress in deploying networks of phasor measurement units (PMUs). The high-speed measurements provided by PMUs are valuable for offline analysis, but the sheer volume of data presents a challenge for utilities. The Accelerated Data Analytics for Power-System Time-Series (ADAPT) project was created to address this challenge by developing SciSync, an open-source software tool that enables utilities to rapidly read, process, analyze, and review grid measurements. SciSync was designed to provide the capabilities of Archive Walker, a research tool developed by PNNL, along with the reliability and performance of commercial-grade software.
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Machine Learning Solutions for a Stable Grid Recovery
Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.
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Renewable Energy and Storage Cybersecurity Research (RESCue) Pilot Final Report
The Renewable Energy and Storage Cybersecurity Research (RESCue) project is a collaborative effort aimed at securing the rapidly growing deployment of distributed energy resources (DERs) and transmission-connected hybrid renewable energy systems against escalating cyber threats. This project brings together major original equipment manufacturers (OEMs) of wind, solar, and energy storage, along with major asset owners and DOE National Laboratories, to collectively identify cyber threats, assess risks, and develop robust cybersecurity strategies and solutions. This publication the final report for the first year of the project.
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Cyber-Informed Engineering Research and Development Guide
This document provides guidance on incorporating Cyber Informed Engineering (CIE) principles into the research and development (R&D) of operational technology systems and tools, facilitating the creation and adoption of innovative technologies that are secure and resilient by design. As technological innovation and research are becoming pivotal for economic and national security, cybersecurity has emerged as a paramount concern across industries and sectors. The challenge of integrating robust cybersecurity measures is imperative to safeguard critical infrastructure, protect sensitive data, and preserve national security interests.
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WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset
To satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have, we aimed at developing an updated version of the meteorological WIND Toolkit, named WIND Toolkit Long-term Ensemble Dataset (WTK-LED), which is a meteorological dataset providing time series every 5 min and 2 km, including model uncertainty of wind speed at every modeling grid point so that users are provided with a range of possible wind speeds every 2 km. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: 1) Numerical simulations covering the continental United States, Alaska, and Hawaii, with high-resolution data being available for 3 years (2018-2020). 2) Climate simulations from Argonne National Laboratories covering the North American continent, including Alaska, Canada, and most of Mexico and the Caribbean Islands. These simulations complement the new WTK-LED to offer a 4-km dataset covering 20 years, from 2001-2020. 3) Specific long-term,high-resolution offshore simulations have been conducted separately for the US coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind data set. This report focuses on a description of the land-based WTK-LED for CONUS, Hawaii, and Alaska, for the 3-year 2-km/5-min dataset and the 20-year 4-km/hourly dataset, as well as the uncertainty quantification method. We also provide limited validation results. Based on our results to date, we suggest use cases and applications for each dataset of the WTK-LED.
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Application Deconfliction Characterization and Alternatives Analysis
This report provides an overview of the domain space and solution techniques that could be used to create a robust, flexible app deconfliction service. Three approaches are reviewed with summaries of the characteristics, elements, and results from preliminary demonstrations of solution techniques based on each approach: 1) rules and heuristics, 2) cooperation, and 3) optimization. The strengths and weaknesses of each solution technique were explored through a set of numerical demonstrations on modified IEEE 123 node and 9500 node test feeders. An alternatives analysis of individual deconfliction elements was performed with each solution technique element evaluated against criteria reflecting the dynamic app environment, need to balance app objectives, and scalability issues versus the number of applications, setpoints, and distributed control areas. It is anticipated that a combined solution for a GridAPPS-D Deconfliction Service can be formulated using a combination of elements from each solution technique. The combined solution would combine 1) device control budgets to reduce the size of the solution space by constraining system setpoints to those will not result in accelerated degradation of physical assets, 2) system operations rules to constrain the solution space by eliminating setpoints that result in violations of system limits or operational best practices, 3) contextual status signals shared with or among apps such that they could update their desired setpoints based on the evolving context, 4) a mediator that incentivizes apps to come to a cooperative solution, and 5) Setpoint-informed optimization as a fallback mechanism if a cooperative solution cannot be agreed upon by applications.
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Quantum Sensing for Energy Applications
Quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. A growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. NETL is leveraging experimental and computational quantum tools to enhance sensitivity of hybrid quantum-classical ultrasensitive sensors for the detection of hydrocarbons and rare earth elements (REEs).
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Wholesale Electricity Analysis via Simulation & Learning Experiments (WEASLE): Platform Development and Pilot Competition
This document reports the development of the Wholesale Electricity Analysis via Simulation and Learning Experiments (WEASLE) platform and the pilot competition that was conducted to test the platform. Due to the increasing reliance on variable renewable energy resources for bulk power, the pilot competition, called the Energy Storage Participation Algorithm Competition (ESPA-Comp), was used to test the effect of various market designs on storage utilization and market efficiency. Basic details of the platform are provided, including an overview of the market clearing engine, the battery dispatch and degradation models, electric grid topology and resource mix, and software architecture. Two market designs were tested: a two-settlement market analogous to typical ISO design today, and a multi-settlement market that allows additional forward-trading periods during the real-time market. Results from the pilot competition show that the storage bidding problem is nontrivial and is well suited for future challenges. We find that: 1) all four teams utilized different approaches to the bidding problem, 2) different methodological approaches led to substantially different offer behaviors, 3) resource profits are clustered by team and methodological approach, 4) simulated offers reduced market surplus by about 0.5%, 5) substantially different prices between two-settlement and multi-settlement markets albeit minimal difference in overall market surplus.
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Digital Twin + AI: Control Room of the Future [Slides]
The control room functions as the central brain of the grid, essential for balancing supply and demand and ensuring moment-to-moment grid reliability. Like the human brain, which processes sensory data to make decisions, control room operators analyze operational data from power generation, transmission, and distribution to make informed decisions. Currently, decision-making primarily rests with operators due to hardware and software limitations. However, with technological advancements, Digital Twins and AI are becoming high interest points in the control room's decision-making pilot programs. NREL is developing a comprehensive decision-making platform that integrates Digital Twins, AI, and advanced visualization techniques. As this integration progresses, the role of Digital Twins will evolve from conducting automated simulations to serving as a Trustworthy AI enabler, offering verification and validation of AI-generated response for power systems or providing physics-aware synthetic data of AI pre-training.
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Generative AI for Power Grid Operations
Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.
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Overview and Commentary on Applying the Coordinated Vulnerability Disclosure Process to Photovoltaic System Devices
The rapid expansion of photovoltaic (PV) systems, particularly inverters, has introduced new cybersecurity challenges that threaten both local operations as well as the broader electrical grid’s stability. PV inverters, integrated into critical energy infrastructure are potential targets for cyber attacks due to vulnerabilities in firmware, remote access systems, and communication protocols. The Coordinated Vulnerability Disclosure (CVD) process, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), provides a framework for identifying, reporting, and addressing these vulnerabilities in a transparent and collaborative manner. This report outlines the CVD process as it applies to PV systems, detailing the roles of key stakeholders, such as manufacturers, grid operators, and security researchers. The report also highlights specific challenges in managing vulnerabilities for new and legacy PV systems, which includes those introduced by insecure communications and third-party supply chain components. By adhering to the CVD process, the PV industry can mitigate cybersecurity risks, ensure regulatory compliance, and maintain consumer trust, while safeguarding the operational resilience of the energy grid. Ultimately, the effective coordination of vulnerability management is crucial for securing the future of PV systems within the critical electric grid infrastructure landscape.
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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.
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