Markets:
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M-49: Facilitating Efficient Storage Operations and Investment in U.S. Electricity Markets (ending July 2028)
| Summary | Developers of energy storage often cite market and regulatory barriers to deployment as key factors preventing technology maturation. This project investigates market design questions raised by entry of significant storage and describes potential reforms to markets for energy, ancillary services, and capacity that are needed to efficiently value storage and facilitate its contracting. The project comprises three components: (1) we will investigate incentives for investment in customer-sited storage by invoking a standard optimization model, REopt, a National Renewable Energy Lab-developed tool for least-cost distributed energy generation; (2) we will examine the potential for intraday markets to improve scheduling and profitability in short-term operations; and, (3) we will investigate methods to improve the treatment of long-duration storage in production cost models, with implications for accreditation and the cost of new entry. |
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| Academic Team Members | Project Leader: Jacob Mays (Cornell, jacobmays@cornell.edu) Team members: Shane Henderson (Cornell, sgh9@cornell.edu), Alexandra Newman (Co. School of Mines, anewman@mines.edu) |
| Industry Team Members | Paul Dockery (CAISO), Scott Benner (PJM), Shubhrajit Bhattacharjee (PJM), Alexander Zolan (NREL), Tongxin Zheng (ISO-NE), Hareekaanth Ravikumar (EPRI), Waleed Aslam (EPRI), Nitin Padmanabhan (EPRI), Ryan Schoppe (SPP), Pradip Kumar (NYISO) |
| Project Period | August 1, 2026 to July 31, 2028 |
M-48: Market Enhancements to Improve Performance and Reduce Uplift: Real-Time Ramp Procurement and Resource Adequacy Enhancements (ending July 2027)
| Summary | The proposed effort will focus on three key decision-making time-stages in the electric energy markets, split into two key efforts: (1) the real-time markets and ramping products and (2) the day-ahead market models and capacity market / resource adequacy models. The first effort will examine how existing pricing strategies incentivize ramping participation in the real-time markets (or lack thereof of adequate incentives) combined with the impacts on uplift payments. The work will propose a new pricing strategy that provides better incentives for ramp product offering and reduces uplift requirements tied to single period pricing and ramp procurement. The second effort will investigate day-ahead security constrained unit commitment (SCUC) formulations and how variations can influence uplift payments, primarily connected to security constraint modeling. This effort then will propose enhancements for capacity market / resource adequacy models by including better representation of energy, ancillary services, time periods, and probabilistic scenarios. The team will then assess to what extent an enhanced capacity market structure can influence day-ahead market efficiency and uplift payments. |
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| Academic Team Members | Project Leader: Kory Hedman (Arizona State, khedman@asu.edu) Team members: Lang Tong (Cornell, lt35@cornell.edu) |
| Industry Team Members | Hong Chen (PJM), Yang Chen (PJM), Yongchong Chen (NREL), Bruce Fardanesh (NYPA), Anthony Giacomoni (PJM), Bo Gong (SRP), Chuck Hansen (MISO), Pradip Kumar (NYISO), Gregory Labbe (TEA), Ajay Lakshmanan (TEA), Clyde Loutan (CAISO), Beth Massey (TEA), Nikki Militello (PJM), Ryan Schoppe (EPRI), Nikita Singhal (EPRI), Anupam Thatte (MISO), Leilei Xiong (TEA), Tongxin Zheng (ISO-NE) |
| Project Period | August 1, 2025 to July 31, 2027 |
M-47: Enhancing Resource Adequacy Accreditation and Operational Incentives for Energy-Limited Resources (ending July 2026)
| Summary | Resource adequacy has been a central concern of liberalized electricity markets since their inception, with concern only growing due to greater frequency of extreme weather events and ambitious goals for decarbonization. Increasingly, it is expected that systems will rely on energy-limited resources, such as storage, to help ensure reliable and resilient grid operations during emergency events. However, existing methods for resource adequacy accreditation are inconsistent with the market incentives provided for energy-limited resources during scarcity events, potentially leading to inconsistencies between system needs and private profit maximization and, therefore, challenges in ensuring reliable operations. This project aims to address the practical and theoretical challenges raised by the incorporation of energy-limited resources in resource adequacy mechanisms, including consideration of factors related to operational uncertainty, financial risk, and market power mitigation. |
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| Academic Team Members | Project Leader: Jacob Mays (Cornell, jacobmays@cornell.edu) Team members: Elnaz Kabir (Texas A&M, ekabir@tamu.edu) |
| Industry Team Members | Jessica Kuna (NREL), Akshay Korad (MISO), Eduardo Ibanez (MISO), Dustin Grethen (MISO), Anthony Giacomoni (PJM), Nikita Singhal (EPRI), Jo Ann Rañola (EPRI), Mike Swider (NYISO), Kurt LaFrance (CMS Energy), Sara Walz (CMS Energy), Becky Robinson (CAISO) |
| Project Period | August 1, 2024 to July 31, 2026 |
Systems:
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S-118: AI Data Center Dynamic Modeling, Design Optimization, and Grid Stability Studies (ending July 2028)
| Summary | The rapid growth of AI is driving steep increases in data center electricity demand and introducing fast, power-electronics–dominated dynamics that threaten system stability. Existing static and simplistic dynamic load models fail to capture these behaviors, creating an urgent need for validated, high-fidelity dynamic models and stability-oriented design methods for AI data centers. To address this critical need, this project pursues three thrusts: (1) Dynamic Modeling: develop high-fidelity electromagnetic transient (EMT) models of AI data center electrical infrastructure, validated using real-world data; (2) Design Optimization: develop systematic approaches to optimize the electrical configuration of AI data centers to improve power quality, promote grid interaction, and enhance stability metrics such as effective inertia and disturbance ride-through capability; and (3) Grid Stability Studies: analyze the system-wide stability impacts of AI data centers under various operational scenarios and use the developed models to identify grid stability risks. The outcomes will provide transferable, high-fidelity models and tools for grid planning and operations. The resulting models and guidelines will also inform industry standards for the design, planning, and interconnection of AI data centers. |
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| Academic Team Members | Project Leader: Xin Chen (Texas A&M, xin_chen@tamu.edu) Team Members: Mike Ranjram (Arizona State, mranjram@asu.edu), Prasad Enjeti (Texas A&M, enjeti@tamu.edu) |
| Industry Team Members | Rui Yang (NREL), Ali Yazdanpanah (ERCOT), Prashant Kansal (ERCOT), Hong Chen (PJM), Michael Mattox (MISO), Bo Gong (SRP), Mostafa Sedighizadeh (SPP) |
| Project Period | August 1, 2026 to July 31, 2028 |
S-117: Improving grid strength and flexibility using grid-enhancing technologies (ending July 2028)
| Summary | This project aims to improve the strength and flexibility of the electric grid by leveraging Grid-Enhancing Technologies (GETs) such as advanced conductors, power flow controllers, phase-shifting transformers, and dynamic line rating. While GETs are known for enhancing transmission capacity, their broader system-level impacts, especially on grid strength improvement in systems rich in inverter-based resources (IBRs) are not well understood. The proposed research will develop new metrics, scalable algorithms, and integration strategies to better characterize and optimize GETs for modern IBR-rich grids. The overarching goal is to broaden the understanding of GET’s system-level impact and capabilities, and enable effective planning, operation, and integration of GETs into system planning and operation tools including energy management systems (EMS), thereby supporting a more resilient and adaptable power grid. |
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| Academic Team Members | Project Leader: Manish Singh (Univ. of Wisconsin-Madison, manish.singh@wisc.edu) Team Members: Bai Cui (Iowa State, baicui@iastate.edu), Venkataramana Ajjarapu (Iowa State, vajjarap@iastate.edu) |
| Industry Team Members | Tongxin Zheng (ISO-NE), Clyde Loutan (CAISO), Shubo Zhang (NYISO), Hong Chen (PJM), Florent Xavier (RTE-France), Tari Jung (MISO), Bo Gong, (SRP), Chetan Mishra (Dominion Energy), Bruce Fardanesh (NYPA), Dongbo Zhao (Eaton), Swaroop Guggilum (EPRI), Alberto Del Rosso (EPRI), Jin Tan (NREL) |
| Project Period | August 1, 2026 to July 31, 2028 |
S-116G: Analysis of Low Frequency Oscillations Induced by Inverter Based Resources (IBRs): Origin, Identification, Detection, and Implications on Time Domain Response (ending August 2026)
| Summary | This project develops techniques to identify low frequency oscillations in IBR dominated systems and will characterize their implication on the time domain response of the system. |
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| Academic Team Members | Project Leader: Vijay Vittal (Arizona State, vijay.vittal@asu.edu) |
| Industry Team Members | Sudipta Dutta (EPRI), Deepak Ramasubramanian (EPRI), Parag Mitra (EPRI), Anish Gaikwad (EPRI) |
| Project Period | September 1, 2025 to August 31, 2026 |
S-115G: Monitoring oscillations caused by inverter-based resources (ending March 2027)
| Summary | There is a growing presence of inverter-based resources (IBRs) in power grids worldwide. These include renewable energy sources, bulk energy storage devices, and high-voltage DC transmission networks. Fast dynamic controls and switches built into these inverter-based energy interfaces interact with each other and with traditional power grid controls in unpredictable ways. Such interactions have resulted in subsynchronous oscillations observed in several recent events worldwide. They have also caused forced oscillations with frequencies less than 1 Hz that have resonated with interarea modes. An urgent need is understanding how these oscillations can be monitored and analyzed for operator insight using online algorithms. This project will focus on the monitoring and source location analysis of IBR-related oscillations in the subsynchronous and electromechanical frequency ranges. The roles and limitations of synchrophasor and point-on-wave measurements will be studied in this context, and appropriate oscillation estimation and analysis algorithms will be developed in the project. These will be tested using representative model simulations of the RTE power system. |
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| Academic Team Members | Project Leader: Vaithianathan (Mani) Venkatasubramanian (Washington State, mani@eecs.wsu.edu) |
| Industry Team Members | Florent Xavier (RTE), Gilles Torresan (RTE), Adrien Guironnet (RTE) |
| Project Period | April 1, 2025 to March 31, 2027 |
S-114G: Robust DER Allocation with Data-driven Control and Generative Methods (ending December 2026)
| Summary | Grid edge DER allocation requires managing bulk power and ensuring grid edge charging and power constraints. We propose using both model predictive control and reinforcement learning techniques to design such algorithms. We will also use generative methods such as predictive-corrective diffusion models to generate much needed rare events to evaluate our work and data-driven control models. |
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| Academic Team Members | Project Leader: Lalitha Sankar (ASU, lsankar@asu.edu) |
| Industry Team Members | Andrea Pinceti (Dominion), George Stefopolous (NYPA), Evangelos Farantatos (EPRI) |
| Project Period | January 1, 2025 to December 31, 2026 |
S-113: Stochastic temperature-dependent models for evaluating flexible load dispatch (ending July 2027)
| Summary | The coordination of demand-responsive, flexible loads can offer extremely lowcost load balancing to offset the variability of renewable energy. However, the potential of flexible loads to provide these services is inherently uncertain and temperature dependent. This project develops stochastic multi-period models for aggregated flexible loads, yielding a load forecast with confidence intervals. To do this, we leverage physics-based models with data-driven parameter estimation for electric vehicles, thermostatically controlled loads, and water supply systems. |
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| Academic Team Members | Project Leader: Constance Crozier (Georgia Tech, ccrozier8@gatech.edu) Team members: Anna Stuhlmacher (MTU, annastu@mtu.edu) and Daniel Molzahn (Georgia Tech, molzahn@gatech.edu) |
| Industry Team Members | Florent Xavier (RTE), Oliver Lebois (RTE), Kurt LaFrance (CMS Energy), Jinye Zhao (ISO-NE), Mark Lauby (NERC), Peter Klauer (CAISO), Jeff Maguire (NREL), Prateek Munankarmi (NREL), Laura Walter (PJM), Anupam Thatte (MISO) |
| Project Period | August 1, 2025 to July 31, 2027 |
S-112: Structural risk screening and strategic vulnerability assessment in high-caliber optimal power flow under high-impact low-frequency grid events. (ending July 2027)
| Summary | This proposal explores enhancements to NERC CIP and NIST frameworks by introducing new threat models and tailored risk management strategies to advance critical infrastructure security. It focuses on advanced metrics for assessing system stability and operational risks through contingency analysis of substation outages and power flow modeling, using scenario trees and structural risk screening to evaluate cyber, physical, and human vulnerabilities. By addressing the gap in anomaly observability and correlation, which stems from insufficient strategic investment, the proposal promotes targeted investments to strengthen anomaly detection capabilities, including the installation of honeynets. Aligned with FERC incentives, such as a 2% ROE adder and cost deferrals, it encourages further investment in grid cybersecurity. Additionally, the seed grant request aims to improve multistage planning by developing metrics that quantify the impact of extended contingencies from physical and electronic sabotage, offering key insights into operational planning and resilience strategies. |
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| Academic Team Members | Project Leader: Katherine Davis (TAMU, katedavis@tamu.edu) Team members: Chee-Wooi Ten (MTU, ten@mtu.edu) and Alexandra Newman (CSM, anewman@mines.edu) |
| Industry Team Members | Kurt LaFrance (CMS Energy), Dongbo Zhao (Eaton), Tongxin Zheng (ISO-NE), Venkat Banunarayanan (NRECA), Pradip Kumar (NYISO), Clyde Loutan (CAISO) |
| Project Period | August 1, 2025 to July 31, 2027 |
S-111: Multiple Event Recordings for Grid Evaluation (MERGE) (ending July 2026)
| Summary | Complex electric grid disturbances require comprehensive assessment to determine whether the disturbances may lead to contingencies such as oscillations, instability, cascades, or relay misoperations. The typical utility substation equipment that captures recordings of field events are phasor measurement units (PMUs), digital protection relays (DPRs), digital fault recorders (DFRs), and sequence of event recorders (SoEs). This project focuses on the use of ML/AI to develop automated analysis of multiple event recordings for grid evaluation (MERGE) during disturbances. MERGE’s special emphasis will be on the use of Point-on-wave (PoW) triggered and/or streaming data to enhance the grid performance assessment. It is an extension of the ongoing DECODE project that uses ML/AI to automatically analyses PMU data. MERGE extends the application of ML/AI to the POW data and merges such results with the results of automated analysis using phasors for a comprehensive view of fast and slow changing waveforms reflecting evolving disturbance dynamics. The automated tracking of a combination of fast and slow changing transients is the MERGE’s novel feature not tackled before. MERGE Illustrates how such studies may be utilized for operations, planning, engineering, or protection applications. |
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| Academic Team Members | Project Leader: Mladen Kezunovic (TAMU, m-kezunovic@tamu.edu) Team members: Vijay Vittal (Arizona State, vijay.vittal@asu.edu), Mani Venkatasubramanian (Washington State, mani@wsu.edu), and Anamitra Pal (Arizona State, anamitra.Pal@asu.edu) |
| Industry Team Members | Evangelos Farantatos (EPRI), Lin Zhu (EPRI), Tongxin Zheng (ISO-NE), Rui Yang (NREL), Yang Chen (PJM), Gilles Torresan (RTE), Justin Lee (SRP), Matthew Rhodes (SRP) |
| Project Period | August 1, 2024 to July 31, 2026 |
S-110: Data-Driven Resilience Modeling, Prediction, and Enhancement (ending July 2026)
| Summary | New analytics that quantify the risk of extreme events and relate this to engineering decisions are needed to sensibly invest in grid resilience and to be able to advocate for these investments based on data. We propose a comprehensive framework to model and quantify grid resilience from historical data, and exploit it to predict resilience performance under various extreme events, evaluate DERs’ impacts on resilience, and optimize infrastructure investments to enhance resilience. Our framework leverages outage and restoration data that is already recorded by PSERC utilities as well as weather data to quantify resilience metrics and estimate probability distributions of these metrics with respect to weather variables for optimal resilience-oriented investments. Compared to existing work that relies only on detailed models, our framework will overcome difficulties of simulation of resilience based on detailed models by leveraging statistical models driven by real utility data, thus making it faster, more realistic, and easier to implement. |
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| Academic Team Members | Project Leader: Ian Dobson (Iowa State, dobson@iastate.edu)
Team members: Zhaoyu Wang (Iowa State, wzy@iastate.edu) and Anamika Dubey (Washington State, anamika.dubey@wsu.edu) |
| Industry Team Members | Hong Chen (PJM), Chris Callaghan (PJM), Laura Walter (PJM), Haifeng Liu (CAISO), Fei Ding (NREL), Atena Darvishi (NYPA), Venkat Banunarayanan (NRECA), Svetlana Ekisheva (NERC), Anupam Thatte (MISO), Ruchi Rajasekhar (MISO) |
| Project Period | August 1, 2024 to July 31, 2026 |
S-109G: Impact Analysis of Synchronous Generators and Inverter-Based Resources on System Modes (ending June 2025)
| Summary | With large-scale integrations of renewable energy sources, the dynamics of the power systems is becoming more complex in power grids all over the world. Fast dynamic controls and switches that are built into the newer power electronic-based energy interfaces are interacting with the traditional power grid controls in unpredictable ways. These complex and non-smooth dynamic mechanisms are impacting the small-signal stability properties of bulk power systems. This project will focus on the impact analysis of inverter-based resources (IBRs) and synchronous generators on the damping properties of system modes. The project will assess the overall contribution of the increased presence of IBRs in medium-scale power system models. The project will also develop novel signal theoretic algorithms for estimating the impact of synchronous machines and IBRs on the damping levels of interarea modes using ambient synchrophasor measurements. |
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| Academic Team Members | Project Leader: Vaithianathan (Mani) Venkatasubramanian (Washington State, mani@eecs.wsu.edu) |
| Industry Team Members | Patrick Panciatici (RTE), Gilles Torresan (RTE), Adrien Guironnet (RTE) |
| Project Period | June 16, 2023 to June 15, 2025 |
Transmission and Distribution Technologies (T&D):
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T-77: Characterizing Emerging Large Loads with LLM-Driven Models (ending July 2028)
| Summary | Managing large electrical loads, such as data centers, industrial campuses, and high-performance computing facilities, is becoming a critical challenge for modern power systems, with direct implications for grid reliability, market operations, and renewable integration. These tens-to-hundreds-of-megawatt facilities exhibit highly dynamic, non-stationary demand patterns driven by computing workloads, cooling cycles, and operational policies. However, confidentiality, security concerns, and the absence of standardized data-sharing limit access to detailed measurements. To address this challenge, we will create a statistically and operationally realistic synthetic dataset to complement the limited real data available. Building on this foundation, the project will develop Large Language Model (LLM)-driven large load classification, disaggregation, and anomaly detection tools that work seamlessly with ISO, utility, and vendor analytics platforms. These capabilities will enable more accurate facility characterization, behind-the-meter analysis, and reliability assessment, preparing the T&D system for the next generation of high-impact loads. |
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| Academic Team Members | Project Leader: Yi Hu (Michigan Tech, yhu6@mtu.edu) Team Members: Chee-Wooi Ten (Michigan Tech, ten@mtu.edu), Lang Tong (Cornell, lt35@cornell.edu) |
| Industry Team Members | Prashant Kansal (ERCOT), Sean Carr (ComEd), Mark Lauby (NERC), Aung Thant (NERC), JP Skeath (NERC), Latrice Harkness (NERC), Evangelos Farantatos (EPRI), Aboutaleb Haddadi (EPRI), Bajarang Agrawal (APS), Beth Massey (The Energy Authority), Clyde Loutan (CAISO), Rui Yeng (NREL), Kurt LaFrance (CMS Energy), Pradip Kumar (NYISO), Venkat Banunarayanan (NRECA), Ibukunoluwa Korede (Dominion Energy), Dongbo Zhao (Eaton), Younes Seyedi (Hubbell), Bruce Fardanesh (NYPA), Dr. Weigiang Chen (ABB) |
| Project Period | August 1, 2026 to July 31, 2028 |
T-76: Fast and Secure Hybrid Traveling Wave Protection for IBR-Dominated Transmission and Distribution Systems (ending July 2028)
| Summary | This proposal aims to develop innovative and reliable protection methods for inverter-based resource (IBR)-dominated power systems. Hybrid traveling-wave, high-frequency differential, and estimation-based schemes will be designed to address challenges from high IBR penetration and converter-dominated loads such as AI data centers. Leveraging advanced digital signal processing (real-time border stationary wavelet transform) and a state-of-the-art 3-km experimental platform with integrated renewables, the project targets distribution and AC transmission systems as the primary focus, and includes a bounded HVDC demonstration. The protection methods will ensure outstanding speed and precision in fault detection and isolation, enhancing selectivity in multi-terminal feeders, through-transformer networks, and safety-critical cases such as downed conductors and fire prevention. Preliminary experimental results show that first-arrival traveling-wave content remains localized even under high IBR penetration, supporting immunity to harmonics, low inertia, low fault currents, and reduced sequence components. This research directly contributes to grid reliability, resilience, and security, enabling robust operation across non-homogeneous infrastructures. Outcomes include deployable protection methods, software tools, and prototype hardware, providing industry members with advanced solutions that enhance grid protection and support the transition toward sustainable power systems. |
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| Academic Team Members | Project Leader: Flavio B. Costa (Michigan Tech, fbcosta@mtu.edu) Team Members: Sakis Meliopoulos (Georgia Tech, sakis.m@gatech.edu) |
| Industry Team Members | Evangelos Farantatos (EPRI), Aboutaleb Haddadi (EPRI), Kurt LaFrance (Consumers Energy), Bo Gong (SRP), James Stoupis (ABB), Mahmoud Abdelaal (ABB), Frankie Zhang (ISO-NE), Barry Mather (NREL), Clyde Loutan (CAISO) |
| Project Period | August 1, 2026 to July 31, 2028 |
T-75: Reconfigurable Power Electronics and Control for Fault-Tolerant Grid Operations (ending July 2028)
| Summary | Power electronics have become fundamental to the power grid, but their limited fault-current contribution remains a barrier to reliable inverter-based resource (IBR) integration. This project explores a reconfigurable modular inverter that can dynamically switch between series and parallel configurations to deliver synchronous-generator-comparable fault currents. By boosting current on demand, the project will investigate to what degree compatibility with existing protection infrastructure will be preserved and simplifies large-scale IBR adoption. The project will quantify protection benefits, develop safe protection-aware control frameworks, and prototype hardware to demonstrate practical fault-current support. |
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| Academic Team Members | Project Leader: Mike Ranjram (Arizona State, mranjram@asu.edu) Team Members: Yang Weng (Arizona State, yweng2@asu.edu), Sakis Meliopoulos (Georgia Tech, sakis.m@gatech.edu) |
| Industry Team Members | Prashant Kansal (ERCOT), Sean Carr (ComEd), Mark Lauby (NERC), Aung Thant (NERC), JP Skeath (NERC), Latrice Harkness (NERC), Evangelos Farantatos (EPRI), Aboutaleb Haddadi (EPRI), Bajarang Agrawal (APS), Beth Massey (The Energy Authority), Clyde Loutan (CAISO), Rui Yeng (NREL), Kurt LaFrance (CMS Energy), Pradip Kumar (NYISO), Venkat Banunarayanan (NRECA), Ibukunoluwa Korede (Dominion Energy), Dongbo Zhao (Eaton), Younes Seyedi (Hubbell), Bruce Fardanesh (NYPA), Dr. Weigiang Chen (ABB) |
| Project Period | August 1, 2026 to July 31, 2028 |
T-74G: Real-Time Fault Diagnosis in Modern Distribution Systems Using Traveling Wave Analysis and Advanced Digital Signal Processing (ending July 2027)
| Summary | The increasing integration of inverter-based resources (IBRs) into modern distribution networks presents significant fault diagnosis and protection challenges due to complex fault signatures and low fault currents. Traditional fault detection methods and protection systems based on overcurrent and low-frequency signals struggle to operate effectively in IBR-dominated environments. This project proposes a novel real-time fault diagnosis system based on traveling wave (TW) analysis and advanced real-time digital signal processing (RT-DSP). The proposed approach enables fast fault detection, classification, and location. The RT-DSP framework enhances TW-based fault location accuracy by addressing challenges such as multiple reflections, mode mixing, and nonhomogeneous lines. Furthermore, the proposed method is designed to identify subtle TW patterns associated with extreme weather events and high-impedance faults (HIFs). Machine learning algorithms will be integrated to classify fault types and predict incipient faults in events that generate overdamped TWs. A real-time simulator and a state-of-the-art laboratory setup with a 3 km low-voltage distribution line integrated with renewable energy sources will validate the algorithms. The outcomes of this research will enhance grid reliability, resilience, and stability in sustainable energy systems. This project will support future advancements in TW-based fault protection, ensuring adaptive and robust power distribution networks in an evolving energy landscape. |
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| Academic Team Members | Project Leader: Flavio B. Costa (Michigan Tech, fbcosta@mtu.edu) |
| Industry Team Members | Kurt LaFrance (CMS Energy) |
| Project Period | August 1, 2025 to July 31, 2027 |
T-73: Monitoring and Reinforcement of Operational Security in Inverter Dominant Integrated Transmission and Distribution Grids (ending July 2027)
| Summary | As power grids increasingly incorporate large-scale inverter-based resources (e.g., wind and solar generation, HVDC systems, and energy storage systems), ensuring the operational security of the power grid becomes more challenging. This project will focus on the following main tasks (1) Adequacy analysis of grid strength: Improper interactions among controllers of IBRs/DERs and their dependencies on the grid strength will be identified. The limitations of various white-box and black-box models will be investigated, and model exchange requirements will be defined to ensure such improper interactions are captured. Moreover, the critical value of grid strength indicators will be determined. (2) Monitoring and evaluation of grid strength: Limitations of various existing grid strength indicators for monitoring and evaluating IBR/DERs dominated T&D power grids will be investigated. Accordingly, proper indicators will be proposed to address these limitations. Specially methodologies for fusing PMU and SCADA measured data into physics-based information will be proposed (3) Adequacy analysis of inertial response: A required combination of synchronous inertia, fast frequency response from IBRs, and DER/EVs response will be assessed for a system to prevent excessive rates of change of frequency (RoCoF) and to keep the system frequency within acceptable limits following disturbances. (4) Monitoring of equivalent inertia: Inertia can vary significantly throughout the day due to the dependency of IBRs and DERs on weather conditions. Real-time monitoring of inertia provides a dynamic understanding of the system’s inertia, allowing operators to make informed decisions to maintain stability. This allows for preemptive actions, such as dispatching additional resources, adjusting generation schedules, or enabling synthetic inertia from renewable energy sources. This project will further advance the understanding of impacts of grid strength and inertia on operational security of inverter dominated power grids and propose solutions to the associated challenges. |
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| Academic Team Members | Project Leader: Saeed Lotfifard (Washington State, s.lotfifard@wsu.edu)
Team Members: Venkataramana Ajjarapu (Iowa State, vajjarap@iastate.edu) |
| Industry Team Members | Kurt LaFrance (CMS Energy), Ibukunoluwa Korede (Dominion), Gad Monga Ilunga (Dominion), Zhongxia Zhang (Dominion), Evangelos Farantatos (EPRI), Dongbo Zhao (Eaton), Deepak Konka (GE), Bin Wang (ISO-NE), Qiang Zhang (ISO-NE), Weiqing Jiang (MISO), Patrick Dalton (MISO), Mark Lauby (NERC), Venkat Banunarayanan (NRECA), Gab-Su Seo (NREL), Kumaraguru Prabkar (NREL), Pradip Kumar (NYISO), Bruce Fardanesh (NYPA), Yang Chen (PJM), Lucas Saludjian (RTE), Florent Xavier (RTE), Mostafa Sedighizadeh (SPP), Bo Gong (SRP) |
| Project Period | August 1, 2025 to July 31, 2027 |
T-72: Physics-Based Interpretable AI Foundation Model Approach to T&D System Monitoring and Protection (ending July 2027)
| Summary | This project develops a physics-based interpretable AI foundation model (FM) for power system monitoring, protection, control, and online stability assessment. Pre-trained using high-resolution continuous point-on-wave measurements combined with legacy sensor data, this physics-based FM is designed to adapt to various operational functions, providing operators with a versatile and powerful tool for enhancing system reliability. The project is organized into three main thrusts: (i) FM pre-training and validation, (ii) FM adaptation for estimationdriven protection and control, and (iii) FM adaptation for real-time stability assessment. The project incorporates several innovative analytical and computational techniques, including FM learning based on the Wiener-Kallianpur-Rosenblatt innovation representation of time series, AI-enhanced point-on-wave-based protection and control strategies, making it an interpretable AI solution, and stability assessments using Lyapunov exponents. Simulations and experiments will be conducted using both synthetic models and field-collected data. |
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| Academic Team Members | Project Leader: Lang Tong (Cornell, lt35@cornell.edu)
Team Members: Sakis Meliopolous (Georgia Tech, sakis.m@gatech.edu) and Amar Matavalam (Arizona State, amar.sagar@asu.edu) |
| Industry Team Members | Sid Suryanarayanan (Eaton), Kurt LaFrance (CMS Energy), Evangelos Farantatos (EPRI), Hong Chen (PJM), Nikki Militello (PJM), Pradip Kumar (NYISO), Xiaochuan Luo (ISO-NE), Venkat Banunarayanan (NRECA), Jing Wang (NREL) |
| Project Period | August 1, 2025 to July 31, 2027 |
T-71G: Realistic Injection Time Series for Node-Breaker Representations of Transmission Systems (ending December 2025)
| Summary | This project develops TS4PS (Time Series for Power Systems), a library of realistic injection time series for a node breaker representation of transmission systems (starting by the French available test case).TS4PS fills a significant gap in power system benchmarks, capturing temporal, topology, and fleet realities that are not available in PGLIB [1], the power systems library maintained and curated by the IEEE WG. These realities are increasingly important as the grid transitions to renewable energy sources, storage assets and the proliferation of distributed energy resources. Indeed, the research community is presently ill-equipped to deliver the next generation of planning and operation tools due to a fundamental lack of realistic data sets. TS4PS fills this gap, while preserving the confidentiality and privacy of the injections. TS4PS will be built from time-series of real SCADA snapshots from which injections have been removed. To recover the injection, TS2PS will use the Data-Driven Time Series Reconstruction Methodology (DDTSRM) from [2]. DDTSRM uses public zonal injection data that are then disaggregated at the bus level, leveraging correlations in zonal injections. This project will also extend DDTSRM to exploit static covariates (e.g., weather information) to further improve the accuracy of the disaggregation at the bus level, exploiting sub-zonal correlations. |
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| Academic Team Members | Project Leader: Pascal Van Hentenryck (Georgia Tech, pascal.vanhentenryck@isye.gatech.edu) |
| Industry Team Members | Lucas Saludjian (RTE) & Camille Pache (RTE) |
| Project Period | January 1, 2025 to December 31, 2025 |
T-69: Grid Integration and Interoperability Evaluation of Multi-Vendor IBRs and Protection Systems for Reliable Power Grid Operation (ending July 2026)
| Summary | Power grids hosting high shares of heterogeneous inverter-based resources (IBRs) equipped with grid following and grid forming controllers coming from variety of manufacturers will become more prevalent in the future. This trend is driven by the increasing adoption of large-scale IBRs such as solar and wind generation, and battery energy storage systems. The controllers in such a heterogeneous system of IBRs interact with protection systems that may also be supplied by different vendors. Ensuring verifiable grid integration and interoperability of the controllers in such a complex multi-vendor system is of crucial importance. Accordingly, standards and grid code requirements are introduced and updated to meet such needs. Notably, the recently approved IEEE Std 2800-2022 defines the IBR interconnection requirements.
The objective of this project encompasses two primary goals: (1) To devise a comprehensive approach for confirming performance requirements and assessing the configurations of IBR controllers in grid interconnection studies, while taking into account the interactions among heterogeneous IBRs and their interactions with protection systems, and (2) To put forth a methodology for evaluating and enhancing the interoperability, dependability, and security of protection systems in such heterogeneous IBR dominated power grids that comply with the IEEE Std 2800-2022 requirements. We will demonstrate and validate the proposed solutions and findings through use case scenarios defined and implemented using modeling and simulation, and further validate actual relay interactions using a system in the loop (SIL) platform. The platform will be used to: a) emulate IBR controllers in a Typhoon simulator interfaced in real-time to the power system model implemented in an RTDS. This will allow us to validate the IBR integration requirements b) use actual protective relays connected to RTDS with the Typhon IBR-emulated interface to study the interaction between IBRs and relays during power grids disturbances. The outcomes of this project can help the system operators and planners, as well as IBR and relay vendors to validate IBR grid integration studies and demonstrate how interoperability of IBRs and relays may affect power systems reliability. The outcomes may further inform development of IBR grid codes, as well as interconnection and interoperability standards, including future revisions of or supplements to IEEE Std 2800. |
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| Academic Team Members | Project Leader: Saeed Lotfifard (Washington State, s.lotfifard@wsu.edu) Team members: Mladen Kezunovic (TAMU, kezunov@ece.tamu.edu) |
| Industry Team Members | Kurt LaFrance (CMS Energy), Evangelos Farantatos (EPRI), Aboutaleb Haddadi (EPRI), Mahendra Patel (EPRI), Dongbo Zhao (Eaton), Kwok Cheung (GE), Deepak Konka (GE), Xiaochuan Luo (ISO-NE), Akshay Korad (MISO), Venkat Banunarayanan (NRECA), Ben Kroposki (NREL), Bruce Fardanesh (NYPA), Casey Cathey (SPP), Bo Gong (SRP), Ibukunoluwa Korede (Dominion Energy), Zhongxia Zhang (Dominion Energy), Gad Illunga (Dominion Energy), Christian Guibout (RTE), Marie-Sophie Debry (RTE), Florent Xavier (RTE) |
| Project Period | August 1, 2024 to July 31, 2026 |
T-68: Advancing Control, Protection, and Stability in Hybrid AC/DC Grids (ending July 2026)
| Summary | While DC and AC transmission have coexisted for decades, the emergence of hybrid AC/DC networks has introduced new challenges and opportunities in transmission and distribution. Hybrid AC/DC networks have the potential to improve resilience and efficiency but face the formidable task of maintaining stability while regulating AC frequency and DC voltages. The interactions between DC grids and host AC grids remain poorly understood. The absence of effective stability analysis tools and a clear understanding of root causes for instabilities in such hybrid systems exacerbates these complexities. Additionally, there is a lack of research on fault characteristics and protection strategies for these hybrid systems. This research aims to explore integrated control, resilience and reliability assessment, and protection of hybrid AC/DC systems, tackling the challenges they pose and developing innovative strategies for their smooth integration, stable operation, control, and protection. |
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| Academic Team Members | Project Leader: Maryam Saeedifard (Georgia Tech, maryam@ece.gatech.edu) Team members: Dominic Groß (UWisc, dominic.gross@wisc.edu) |
| Industry Team Members | Kwok Cheung (GE), Bo Gong (SRP), Alberto Del Rosso (EPRI), Geoff Love (EPRI), Benjamin Kroposki (NREL), Dongbo Zhao (Eaton), Thibault Prevost (RTE), Rambabu Adapa (EPRI), Philip Hart (GE), Ahmad Tbaileh (ISO-NE), Nathanael Martin-Nelson (MISO) |
| Project Period | August 1, 2024 to July 31, 2026 |