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DOE OSTI2015

Trustworthy Cyber Infrastructure for the Power Grid (TCIPG) Final Technical Report - November 20, 2015

The Trustworthy Cyber Infrastructure for the Power Grid project (TCIPG) was funded by DOE and DHS for a period of performance that ran from October 1, 2009 to August 31 2015. The partnership included the University of Illinois at Urbana-Champaign (lead institution) and partner institutions Arizona State University (replacing original partner UC Davis when faculty moved), Dartmouth College, and Washington State University. TCIPG was a unique public-private partnership of government, academia, and industry that was formed to meet the challenge of keeping our power grid secure. TCIPG followed from the earlier NSF-funded TCIP project, which kicked off in 2005. At that time, awareness of cyber security and resiliency in grid systems (and in control systems in general) was low, and the term “smart grid” was not in wide use. The original partnership was formed from a team of academic researchers with a shared vision for the importance of research in this area, and a commitment to producing more impactful results through early involvement of industry. From the TCIPG standpoint, “industry” meant both utilities (investor-owned as well as cooperatives and municipals) and system vendors (who sell technology to the utility sector). Although TCIPG was a university-led initiative, we have from the start stressed real-world impact and partnership with industry. That has led to real-world adoption of TCIPG technologies within the industry, achieving practical benefits. This report summarizes the achievements of TCIPG over its period of performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

SNNPG: Using Spiking Neural Networks to Detect Attacks in the Power Grid

We explore the potential of Spiking Neural Networks (SNN) to enhance the security of power grid operations by detecting False Data Injection (FDI) attacks. These attacks manipulate PMU readings, leading to erroneous control decisions and grid disruptions. We develop a method to convert Phase Measurement Unit (PMU) data into spike trains, capturing both temporal and spatial dimensions. Using an SNN model, we conduct evaluations with simulated power grid data, showcasing accuracy in detecting FDI attacks. SNN models rapidly identify anomalies in real-time PMU data, safeguarding grid operations by alerting operators to irregular readings and preventing incorrect decisions.

artificial intelligence↗

DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗

DOE OSTI2015

Power grid reliability and security

This project has led to the development of a real-time simulation platform for electric power grids called Grid Simulator or GridSim for simulating the dynamic and information network interactions of large- scale power systems. The platform consists of physical models of power system components including synchronous generators, loads and control, which are simulated using a modified commercial power simulator namely Transient Stability Analysis Tool (TSAT) [1] together with data cleanup components, as well as an emulated substation level and wide-area power analysis components. The platform also includes realistic representations of communication network middleware that can emulate the real-time information flow back and forth between substations and control centers in wide-area power systems. The platform has been validated on a realistic 6000-bus model of the western American power system. The simulator GridSim developed in this project is the first of its kind in its ability to simulate real-time response of large-scale power grids, and serves as a cost effective real-time stability and control simulation platform for power industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Dynamic Power Grid Simulation

GridDyn is a part of power grid simulation toolkit. The code is designed using modern object oriented C++ methods utilizing C++11 and recent Boost libraries to ensure compatibility with multiple operating systems and environments.

Top, Philip↗

DOE OSTI2015

Dynamic Power Grid Simulation

Top, Philip↗

DOE OSTI2016

Cyber-Physical System Security of a Power Grid: State-of-the-Art

Here, as part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: the structure of CPSs in a smart grid; cyber vulnerability assessment; cyber protection systems; and testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Secure Time Synchronization in Power Grids and Network HIL Synchrophasor Testing

Reliable and secure time synchronization underpins the monitoring and control functions of modern power grids. As GPS-based timing infrastructures grow more susceptible to spoofing and jamming, their vulnerabilities pose escalating risks to grid stability. This work investigates a secure, resilient timing framework that can serve as an alternative or redundant source for power grids, with a particular focus on synchrophasorbased applications. A candidate timing system architecture is evaluated to guarantee trustworthy time dissemination, even in degraded conditions. A network hardware-in-the-loop testing of two synchrophasors validates the concepts, demonstrating enhanced timing integrity, improved detection of timing anomalies, and sustained observability during adverse timing events.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗

DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗

DOE OSTI2023

Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios

Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.

electic grid, reinforcement learning, climate adap↗

DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grids

This paper investigates the impact of Sustainable Aviation Fuel production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aims to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies in power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Feedback and Oscillations: Constructing Feedback Systems for Root Cause Analysis of Oscillations in Power Grids

Dynamic phenomena linked to inverter-based resources (IBRs) have gained global attention. Several IBR-induced dynamics have caused bulk power system-connected wind or solar power plants to trip, and some have even led to widespread outages. In addition, many oscillations have been observed involving IBR power plants. In 2023, the IEEE Power & Energy Society (PES) IBR Subsynchronous Oscillations (SSO) task force published a journal article, “Real-World Subsynchronous Oscillation Events in Power Grids With High Penetrations of Inverter-Based Resources,” in which 19 IBR oscillation events were examined for their causation. Earlier in 2020, another PES task force article, “Definition and Classification of Power System Stability-Revisited & Extended,” authored by prominent academics, introduced converter-driven stability as a new category of stability. The international power grid industry community also took action by publishing the CIGRE Green Book, Power System Dynamic Modelling and Analysis in Evolving Networks (led by Babak Badrzadeh and Zia Emin) in 2024. In August 2024, the Energy Systems Integration Group (ESIG) released a practical guide led by Nick Miller, “Diagnosis and Mitigation of Observed Oscillations in IBR-Dominant Power System: A Practical Guide.” The goal of the guide is to assist practicing engineers in making initial judgments and conducting detailed analyses about oscillations. Finally, when addressing the classification of stability and oscillations, the guide emphasizes a causality-based taxonomy for grouping, such as voltage control-induced oscillations, synchronization-induced oscillations, and frequency or active power control-induced oscillations.

Fan, Lingling [Univ. of South Florida, Tampa, FL (↗

DOE OSTI2016

GridPACK™ : A Framework for Developing Power Grid Simulations on High-Performance Computing Platforms

This paper describes the GridPACK™ framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework makes extensive use of software templates to provide high level functionality while at the same time allowing developers the freedom to express whatever models and algorithms they are using. GridPACK™ contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors and using parallel linear and non-linear solvers to solve algebraic equations. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support IO and to mana

Palmer, Bruce J.↗

DOE OSTI2015

Design of a Large-scale Virtual Power Grid for Research Community

Due to security and liability concerns, the research community has limited access to realistic large-scale power grid models to test and validate new operation and control methodologies. It is also difficult for industry to evaluate the relative value of competing new tools without a common platform for comparison. This paper proposes to develop a large-scale virtual power grid model that retains basic features and represents future trends of major U.S. electric interconnections. The proposed model will include realistic power flow and dynamics information as well as a relevant geospatial distribution of assets. This model will be made widely available to the research community for various power system stability and control studies and can be used as a common platform for comparing the efficacies of various new technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Characterizing cyber tools for monitoring power grid systems: what information is available and who needs it?

Abstract—The power grid is a complex, interconnected system that requires the coordination of multiple entities ensure the reliability of the system. System operators must be aware of the impacts of various natural and man-made contingencies through telemetry from their control systems, and they must also be aware of the integrity and availability of the cyber assets on their systems. A team of researchers from PNNL and INL have conducted a series of observations and interviews at power utilities to identify what information needs to be available for both control room dispatchers and cyber security analysts to maintain situation awareness of cyber security to effectively monitor the cyber assets on utility control systems. The researchers interviewed and observed control room dispatchers and spoke with regional control system engineers and cyber security analysts to define the context in which such information would be used. The results of this work will inform the design of a tool that will enhance cyber situation awareness and help facilitate effective communication between the various roles and organizations with utility and other regional authorities during contingencies. Index Terms—

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid: Preprint

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aimto meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO 2 -to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aim to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO 2 -to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

capacity expansion model↗