Markets:
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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 |
M-46: Toward Large-Scale DER Aggregation: Competitive Aggregation, Operation Support, and ISO-Aggregator-Utility Coordination (ending August 2025)
| Summary | This project focuses on the practical challenges of DER aggregations for wholesale market participation under FERC Order 2222. We propose a bottom-up approach to address key technological barriers at the interfaces between (a) a DER aggregator (DERA) and its customers where generation, storage, and flexible demand resources are aggregated; (b) a DERA and a market operator (ISO) where DERAs participate in wholesale energy, reserve and regulation markets; (c) a DERA and a distribution utility where the utility provides planning and operation support for delivering the aggreged DER; (d) a utility and an ISO where the ISO maximizes net benefits of market participants and mitigates risk subject to system constraints. In particular, this project investigates large-scale, competitive, and profit-maximizing aggregation technologies that benefit DERA customers more than the prevailing net energy metering-based retail tariffs. The project examines the impacts of DER aggregation uncertainties at all interfaces and develops risk mitigation strategies. The project investigates distribution system planning and operation solutions supporting DER aggregation, DERA bidding strategies in the wholesale markets, and ISO market clearing and settlement solutions. The project delivers technical assessments and comparative studies on the feasibility and cost/benefit analysis. |
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| Academic Team Members | Project Leader: Lang Tong (Cornell, lt35@cornell.edu) Team members: Tim Mount (Cornell, tdm2@cornell.edu), Subhonmesh Bose (UIUC, boses@illinois.edu), and Meng Wu (Arizona State, mwu@asu.edu) |
| Industry Team Members | Mike Swider (NYISO), Tongxin Zheng (ISO-NE), Maria Belenky (PJM), Scott Baker (PJM), Hong Chen (PJM), Clyde Loutan (CAISO), Yonghong Chen (MISO), Anupam Thatte (MISO), Akshay Korad (MISO), Arezou Ghesmati (MISO), Patrick Dalton (MISO), Megan Pamperin (MISO), Patrick Panciatici (RTE) Ahmed Zamzam (NREL), Ryan Fox (GE), Harvey Scribner (SPP), Nikita Singhal (EPRI), Ibrahim Krad (EPRI), Nitin Padmanabhan (EPRI), Arun Sukumaran Nair (Eaton) |
| Project Period | July 1, 2023 to August 31, 2025 |
M-44: The Future of Markets and DERs: Providing Essential Grid Services and Managing Performance Risk (ending August 2025)
| Summary | FERC Order 2222 mandates incorporation of distributed energy resources (DERs) in wholesale markets. With this new requirement, one main power systems change is the push towards not just DER integration but also the reliance on these resources to be able to provide essential grid services. Electricity markets, grid operations, and reliability requirements are designed for classical assets, not emerging assets with distinct characteristics, i.e., higher probability of failure, lower capacity firmness, and lower impact of failure. This project addresses the concern of DER integration from multiple fronts: a) effective aggregation and management of DERs to ensure their profitability and the quality of services provided by these aggregated resources, b) essential market and operational reform, and c) coordination of transmission and distribution services provided by DERs. This project will develop a virtual power plant DER aggregation strategy to demonstrate that, while DERs possess new challenges in comparison to conventional assets, they can be optimally managed such that they perform similar to conventional assets. An intra-day DER evaluator module will be developed to assess whether the aggregated DER will be able to satisfy their offering to ensure a high level of real-time performance during both normal and outlier/extreme events. Moreover, this project provides an effective transmission and distribution coordination strategy by leveraging untapped flexibilities such as optimal mode of operation and setpoint identification of smart inverters within the allowable range of IEEE 1547. |
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| Academic Team Members | Project Leader: Kory W. Hedman (Arizona State, kory.hedman@asu.edu) Team members: Mojdeh Khorsand (Arizona State, mojdeh.khorsand@asu.edu) and Shmuel Oren (UC Berkeley, oren@berkeley.edu) |
| Industry Team Members | Oluwaseyi Akinbode (MISO), Guillermo Bautista Alderete (CAISO), Sumit Bose (GE), Hong Chen (PJM), Yonghong Chen (MISO), Kwok Cheung (GE), Richard Dillon (SPP), Erik Ela (EPRI), Jesse Gantz, (GE), Anthony Giacomoni (PJM), Bo Gong (SRP) Majid Heidarifar (EPRI), Avnaesh Jayantilal (GE), Akshay Korad (MISO), Sobia Naqvi (APS), Dane Schiro (ISO-NE), Nikita Singhal (EPRI), Yohan Sutjandra (The Energy Authority), Arun Nair (Eaton), Clyde Loutan (CAISO), Harvey Scribner (SPP) |
| Project Period | July 1, 2022 to August 31, 2025 |
Systems:
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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) |
| 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 |
S-107: Adaptive Transmission and Distribution Modeling and Simulation Tools for Supporting Integration and Operation of High Penetration of DERs (ending August 2025)
| Summary | As the implementation of FERC Order 2222 is coming and DERs proliferate, it is imperative to (i) adequately model and efficiently analyze the dynamic impacts of DERs on transmission and distribution (T&D) systems, (ii) capture and leverage full capabilities of DER to facilitate large-scale DER integration and T&D system operation. This project 1) Develops adaptive, multi-fidelity modeling and AI-accelerated co-simulation approaches to improve the computational efficiency of dynamic simulation for large-scale T&D systems; 2) Builds an automated tool to create a multi-fidelity model library for adequately representing distribution systems and DERs while maintaining consistency for different dynamic and transient studies; 3) Derives aggregated reactive power capability curves at T&D interfaces by considering DER control capabilities and leverages them to provide reactive power ancillary services. The main outcomes will be open-source tools that help industry members efficiently plan and reliably operate their systems with high penetration of DERs. |
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| Academic Team Members | Project Leader: Qiuhua Huang (Colorado School of Mines, qiuhuahuang@mines.edu) Team members: Venkataramana Ajjarapu (Iowa State, vajjarap@iastate.edu), Meng Wu (Arizona State, mwu@asu.edu) |
| Industry Team Members | Kwok Cheung (GE), Hung-Ming Chou (Dominion Energy), Bo Gong (SRP), Akshay Korad (MISO), Weijia Liu (NREL), Yi Liu (Eaton), Parag Mitra (EPRI), Alberto Del Rosso (EPRI), Deepak Ramasubramanian (EPRI), Harvey Scribner (SPP), Mohit Singh (ComEd), Sid Suryanarayanan (Eaton), Ibukunoluwa Korede (Dominion Energy), Zhongxia Zhang (Dominion Energy), Fernando Fachini (Dominion Energy), Andrea Pinceti (Dominion Energy) |
| Project Period | July 1, 2023 to August 31, 2025 |
S-106: Data-Driven Stability Analysis of IBR-rich Grids with hybrid EMT+Phasor models (ending August 2025)
| Summary | We propose to develop tools for stability analysis of inverter-based resources (IBR)-rich grids represented by hybrid electro-magnetic transient (EMT) and phasor models for planning and operations to (i) localize the cause of instabilities, and (ii) identify limits on transfer flows and IBR injection for secure grid operation. As a result, we directly address NERC recommendations that recognizes the importance of EMT modeling within stability studies. Conventional stability assessment tools currently used by the industry do not support hybrid multi-timescale models, necessitating this work. We will build upon our preliminary results that merge concepts from data-science and dynamical systems to perform transient-stability analysis purely from time-domain simulation results of the hybrid models. The analysis can be used to guide the development of a comprehensive stability assessment tool that will augment the intuition of the planner/operator in identifying critical grid assets. We will validate the proposed methods on network models developed by DOE/NREL. |
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| Academic Team Members | Project Leader: Amarsagar Reddy Ramapuram Matavalam (Arizona State, amar.sagar@asu.edu) Team members: Vijay Vittal (Arizona State, vijay.vittal@asu.edu), Subhonmesh Bose (UIUC, boses@illinois.edu) |
| Industry Team Members | C. Mishra (Dominion Energy), J.D.L. Ree (Dominion Energy), P. Mitra (EPRI), D. Ramasubramanian (EPRI), W. Baker (EPRI), E. Farantatos (EPRI), S. Akinbode (MISO), R. Rajasekhar (MISO), M. Pamperin (MISO), P. Dalton (MISO), B. Gong (SRP), H. Scribner (SPP), M. Parashar (GE), K. Cheung (GE), M. Singh (ComEd), R. Jain (NREL), I. Korede (Dominion Energy), Z. Zhang (Dominion Energy), F. Fachini (Dominion Energy), A. Pinceti (Dominion Energy) |
| Project Period | July 1, 2023 to August 31, 2025 |
S-105: Integration of Protection and Control Systems in Dynamic Security Assessment Methods (ending August 2025)
| Summary | The protection and control (P&C) system has been the top contributor to system disturbances as consistently shown in the annual NERC reports. This reality is expected to continue and worsen as the penetration level of inverter based resources (IBRs) increases. Specifically, IBRs change the characteristics of the system response during faults and disturbances: reduced fault currents, reduced negative and zero sequence components of fault currents and faster transients. The performance of many legacy protection schemes will deteriorate and mis-operations will increase. We propose to develop a framework for assessing the impact of P&C schemes on the security of the system as the presence of IBRs increases. An effective tool for this purpose, is our proposed co-model of the power system and the P&C system that will enable quantification of the interactions between these two systems. By necessity, the co-model will be a three-phase, breaker-oriented, physically-based, P&C inclusive system for analysis of system response during fault conditions. For post fault DSA, we propose the automatic extraction of the RMS-EMT co-model from the above described model. The approach will enable to assess the vulnerability of the system to common mode outages, cascading events, effects from circuit breaker failures, effects of hidden failures and cyber-attacks on the P&C system. Two novel dynamic security assessment methods are proposed based on the co-model: (a) a stability assessment method based on identification of the center of oscillations and the energy method and (b) Artificial Intelligent (AI) based approach to selectively identify critical outage scenarios for further analyses. The proposed work will also provide the framework for power system resiliency analysis by developing effective quantifiable resiliency metrics. |
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| Academic Team Members | Project Leader: A. P. Meliopoulos (Georgia Tech, sakis.m@gatech.edu) Team members: Mojdeh Khorsand Hedman (Arizona State, mojdeh.khorsand@asu.edu) |
| Industry Team Members | Evangelos Farantatos (EPRI), Paul Myrda (EPRI), Bruce Fardanesh (NYPA), Kumaraguru Prabakar (NREL), Bo Gong (SRP), Akshay Korad (MISO), Yonghong Chen (MISO), Cheung Kwok (GE), Harvey Scribner (SPP), David Till (NERC) |
| Project Period | July 1, 2023 to August 31, 2025 |
S-104G: Data Development & Composite Probabilistic Adequacy Evaluation (ending August 2025)
| Summary | The objective of this project is to develop and illustrate a functional system of data development and probabilistic adequacy assessment to enable planners in organizations such as the New York Power Authority to evaluate the adequacy of their generation and transmission systems and use this evaluation to inform planning-related decision-making. |
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| Academic Team Members | Project Leader: James McCalley (Iowa State University, jdm@iastate.edu) |
| Industry Team Members | Shayan Behzadirafi (NYPA) |
| Project Period | January 1, 2023 to December 31, 2024 |
Transmission and Distribution Technologies (T&D):
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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 |
T-67: Smart Meter-Driven Distribution Grid Visibility and Control (ending August 2025)
| Summary | To unlock the capabilities of smart meters for distribution system applications, this project will develop data-driven models for distribution systems and associated algorithms for real-time state estimation and control applications. The project will use actual smart meter data from Hubble and consider practical limitations on communication bandwidth in both developing and validating the proposed models and algorithms. |
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| Academic Team Members | Project Leader: Daniel Molzahn (Georgia Tech, molzahn@gatech.edu) Team members: Line Roald (UW Madison, roald@wisc.edu), Dan Fuhrmann (Michigan Tech, fuhrmann@mtu.edu) |
| Industry Team Members | David Rieken (Hubbell), Kurt LaFrance (CMS Energy), Venkat Banunarayanan (NRECA), Bo Gong (SRP), Dongbo Zhao (Eaton), Shakil Hossan (Eaton), Arun Sukumaran Nair (Eaton), Benjamin Kroposki (NREL), Jouni Peppanenlectric (EPRI), Mohit Singh (ComEd) |
| Project Period | July 1, 2023 to August 31, 2025 |