Current Projects


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M-43: Integrating RTO and utility processes in planning and cost allocation (ending August 2023)

Summary We address resource and transmission (R&T) investment planning methods, focusing on four objectives: (1) Determine ways new software tools can facilitate long-term planning while exploring interdependencies within RTO interconnection queues, transmission expansion processes, capacity markets, and interregional transmission considerations; (2) Implement a reliability evaluation toll that coordinates with a long-term expansion planning application, explicitly accounting for utility-scale renewables, storage, and DER. (3) We will test an implementation of our tools using large-scale industry-size models, developing results to illustrate relationships between investment robustness, resource adequacy, and operational flexibility. (4) Develop a new cost allocation approach which incorporates investment robustness in identifying benefit.
Academic Team Members Project Leader: James McCalley (Iowa State,
Team members: Jacob Mays (Cornell,, Lizhi Wang (Iowa State,
Industry Team Members Wes Hall (GE Gas Power), Eduardo Ibanez (GE Gas Power), Yifan Li (MISO), Haifeng Liu (CAISO), Harvey Scribner (SPP), Casey Cathey (SPP), Greg Brinkman (NREL), Anthony Giacomoni (PJM), Patrick Panciatici (RTE), Renchang Dai (GEIRINA), Anish Gaikwad (EPRI), Parag Mitra (EPRI), Miguel Ortega-Vazquez (EPRI), Mohamed Osman (NERC)
Project Period July 1, 2021 to August 31, 2023

M-42: Modeling and Coordinating Distributed Energy Resources in Power Systems and Markets (ending August 2022)

Summary This project proposes appropriate DER aggregated modeling, low-voltage ride-through, output coordination, and aggregated market offer techniques to analyze/mitigate DER-induced voltage stability challenges as well as ensure DERs’ optimal market participation. The following technical tasks are proposed: 1) quantitative analysis of DER’s dynamic response to fault-induced delayed voltage ride-through (LVRT) capability, through electro-magnetic transient (EMT) modeling of DER-penetrated unbalanced distribution feeders; 2) optimization-based calibration of transmission-level ‘DER_A’ dynamic model (along with the dynamic composite load model), with an integration of physical laws and engineering justifications on the configurations of DER-penetrated distribution systems, for accurate representation of various voltage stability issues on the load site of the system; 3) optimal DER coordination control and aggregated offer strategies, for DER aggregators to optimally participate in the wholesale energy and regulation markets.
Academic Team Members Project Leader: Meng Wu (Arizona State,
Team members: Vijay Vittal (Arizona State,, Alejandro Domginguez-Garcia (UIUC,
Industry Team Members Deepak Ramasubramanian (EPRI), Bo Gong (SRP), Hung-Ming Chou (Dominion Energy), Qiang Zhang (ISO-NE), Di Shi (GEIRI North America), Miaolei Shao (GE Global Research), Yingchen Zhang (NREL), Jianzhong Tong (PJM), Aung Oo (CAISO), Yohan Sutjandra (TEA)
Project Period July 1, 2020 to August 31, 2022

M-41: The Stacked Value of Battery Energy Storage Systems (ending August 2021)

Summary The objective of this proposal is to develop and implement a comprehensive framework for assessing the impact of stacked battery energy storage systems (BESS) services on grid planning, operation, and market outcomes, considering the unique BESS characteristics, intermittency of wind and solar resources, demand shaping due to the high penetration of photovoltaics (PV) and increasing demand from electric vehicles (EVs). To achieve this objective, we propose 1) a stochastic planning approach for optimal placement of utility-scale BESSs; 2) quantitative analysis methods for assessing the impact of stacked BESS services on the grid/market operations; 3) optimal BESS operation strategies for profit maximization through stacked services provision. The proposed framework will provide the industry with a holistic approach for quantitatively assessing BESS impacts on grid planning, operation, and markets.
Academic Team Members Project Leader: Meng Wu (Arizona State,
Team members: Daniel Tylavsky (Arizona State,; Zhi Chen (Washington State,
Industry Team Members Liwei Hao (GE), Yazhou “Leo” Jiang (GE), Jesse Gantz (Centrica), Bernardo Orvananos (Centrica), Robert Hess (Industry Consultant), Tongxin Zheng (ISO-NE), Di Shi (GEIRI North America), Zhiwei Wang (GEIRI North America), Xiaohu Zhang (GEIRI North America), Zhe Yu (GEIRI North America), Yishen Wang (GEIRI North America), Harvey Scribner (SPP), Jay Caspary (SPP), Doug Bowman (SPP), Ben Kroposki (NREL), Yingchen Zhang (NREL), Santosh Veda (NREL), Akshay Korad (MISO), Michaela Flagg (MISO), Aditya Jayam Prabhakar (MISO), Mark Westendorf (MISO), Jau J Guo (AEP), Evan R. Wilcox (AEP), Yazan M. Alsmadi (AEP), Tom Weaver III (AEP), John H. Tucker (AEP), Cho Wang (AEP), Yohan Sutjandra (The Energy Authority)
Project Period July 1, 2019 to August 31, 2021

M-40: Market and Control Mechanisms Enabling Flexible Service Provision by Grid-Edge Resources within End-to-End Power Systems (ending August 2021)

Summary Our overall objective is to investigate the ability of Grid-Edge Resource (GER) aggregates, using innovative market and control mechanisms, to ensure market-based availability and provision of flexible services from GERs that facilitate the reliable and efficient operation of Integrated Transmission and Distribution (ITD) systems. Scalable market-based methods will be developed that permit flexible services to be harnessed from numerous locally-controlled GERs in return for appropriate compensation. These methods will be validated within an open source empirically-based ITD software platform that permits operational and financial performance to be carefully analyzed in advance of implementation. Test cases implemented via this platform will be used to verify method scalability as T&D grid sizes range from small to realistically large and as GER penetration ranges from low to realistically high.
Academic Team Members Project Leader: Leigh Tesfatsion (Iowa State,
Team members: Zhaoyu Wang (Iowa State,; Subhonmesh Bose (University of Illinois at Urbana-Champaign,
Industry Team Members Lorenzo Kristov (Former CAISO Market Design Principal), Haifeng Liu (CAISO), Jim Price (CAISO), Eugene Litvinov (ISO-NE), Yonghong Chen (MISO), Jessica Harrison (MISO), Akshay Korad (MISO), Kristin Swenson (MISO), Jianzhong Tong (PJM), Harbey Scribner (SPP), Hongyan Li (ABB), Jua J. Guo (AEP), Kwok Cheung (GE), Xian Guo (GE Global Research), Yazhou “Leo” Jiang (GE Global Research), Gary Gu (Geiri North America), Erik Ela (EPRI), Evangelos Farantatos (EPRI), Robin Hytowitz (EPRI), Nikita Singhal (EPRI), Dheepak Krishnamurthy (NREL)
Project Period July 1, 2019 to August 31, 2021


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S-96: Data driven control of DERs & hybrid PV plants for enhancing voltage stability with TSO-DSO interactions over multiple timescales (ending August 2023)

Summary This project addresses control & coordination of distribution system assets like DERs and hybrid PV plants with storage to provide real-time active/reactive power support to mitigate voltage instability in transmission systems over multiple timescales. A novel controller for hybrid PV plants will be developed that delivers the requested P/Q/V to the bulk grid. The control is robust to PV variability and disturbances in the grid and exploits the capabilities of DERs like storage. We will utilize physics-based data driven techniques to develop the control scheme of DERs and hybrid PV plants for mitigating the voltage instability in real-time using message-passing machine learning architecture. The control strategies will mitigate/contain inverter tip cascading during delayed voltage recovery events (seconds timeframe) and ensure a safe stability margin for credible Transmission-Distribution (TD) co-simulation platform (PSSE/GridLab-D [1] & OpenDSS) to implement proposed methodologies demonstrating their ability to mitigate voltage instability on practical systems while capturing TSO-DSO interactions. Further (HIL) real-time T&D test bed to ensure that controls are effective under noisy measurements & communication delay.
Academic Team Members Project Leader: V. Ajjarapu (Iowa State University,
Team members: Hugo N. Villegas-Pico (Iowa State University, and Anurag K.Srivastava (Washington State University,
Industry Team Members B. Leonardi (EPRI), F. Eladi (NRECA), C. Mishra (Dominion Energy), D. Ramasubramanian (EPRI), A.D. Rosso (EPRI), Y. Zhang (NREL), H. Scribner (SPP), K. Zhu (MISO), J. Tong (PJM), V. Krishnan (NREL), X. Zhang (GEIRINA), D. Shi (GEIRINA), B. Kroposki (NREL), P. Chongfuangprinya (Hitachi), W. Qiu (NERC), M. Parashar (GE)
Project Period July 1, 2021 to August 31, 2023

S-95: Reliable fault-ride through and protection of converter-dominated power systems under balanced conditions (ending August 2023)

Summary Power electronics interfacing renewables, storage, and novel transmission technologies are envisioned to be the cornerstone of tomorrow’s resilient and sustainable power systems. While state-of-the-art power converter control can replace grid-forming and grid-supporting functionalities of synchronous machines, their design typically neglects crucial aspects such as unbalanced faults and the interaction between converter protections (e.g., current limiting) and system-level protection. This jeopardizes system reliability and resilience and has already resulted in large-scale system outages and explicitly accounts for current limits, unbalanced conditions, and protection in the control design. The proposed approach will enable reliable and predictable fault-ride through capabilities that do not rely on heuristics, avoid adverse interactions with system protection, and retain the positive impact of grid-forming control on system-level stability when feasible.
Academic Team Members Project Leader: Dominic Gross (University of Wisconsin-Madison,
Team members: Maryam Saeedifard (Georgia Tech,
Industry Team Members David Till (NERC), Hongtao Ma (NERC), Ben Kroposki (NREL), Kumaraguru Prabkar (NREL), Hung-Ming Chou (Dominion Energy), Aditya Korad (MISO), Evangelos Farantatos (EPRI), Deepak Ramasubramanian (EPRI), Wenzong Wang (EPRI), Harvey Scribner (SPP), Thibault Prevost (RTE), Guillaume Denis (RTE)
Project Period July 1, 2021 to August 31, 2023

S-94G: Explore the Efficacy of Machine Learning Based Preventive and Corrective Controls to Prevent Cascading Failures Following Large Disturbances (ending August 2022)

Summary This project applies advanced machine learning methods in conjunction with detailed time domain simulations to develop preventive and corrective controls to prevent cascading failures following large disturbances. The project will utilize detailed time domain simulations interfaced with multi-agent reinforcement learning to determine appropriate control actions.
Academic Team Members Project Leader: Vijay Vittal (Arizona State,
Industry Team Members Di Shi (GEIRINA)
Project Period June 1, 2020 to August 31, 2022

S-91: Generating Value from Detailed, Realistic Synthetic Electric Grids (ending August 2022)

Summary The goal of this project is to work closely with the industrial team to generate value from large-scale, detailed and realistic synthetic electric grids. The project builds on recent ARPA-E work by the PIs to develop grids that can be used for research, education, commercial development and public engagement. This four project tasks are 1) developing customized grids, 2) developing specific grid scenarios, 3) develop scenarios to explore decision making with uncertainty, and 4) expanding the scope of synthetic grids for coupling with other infrastructures.
Academic Team Members Project Leader: Thomas J. Overbye (Texas A&M,
Team members: Kate Davis (Texas A&M,, Bernie Lesieutre (Wisconsin,, Line Roald (Wisconsin,
Industry Team Members Harvey Scribner (SPP), Jim Price (CAISO), Bryan Palmintier (NREL), Di Shi (GEIRI), Evangelos Farantatos (EPRI), Cho Wang (AEP), Steven Judd (ISO-NE), Melvin Schoech (CenterPoint), Al Engelmann (ComEd), Yazhou Jiang (GE), Anil Jampala (GE), Mahesh Morjaria (First Solar), Patrick Panciatici (RTE), Jianzhong Tong (PJM), Baj Agrawal (APS), Felicia Ruiz (MISO), Casey Cathey (SPP), Atena Darvishi (NYPA), Paul Myrda (EPRI)
Project Period July 1, 2020 to August 31, 2022

S-90: Grid Supporting Controllers for Enabling 100% Penetration of Inverter Based Resources (ending August 2022)

Summary Future power systems are envisioned to transition to significant amount of renewable energy. For example, California has mandated 100% renewable energy supply by 2045 and Minnesota has passed laws to move to full renewable energy supply by 2050. New technologies such as HVDC systems will be also integrated into the conventional AC systems forging hybrid AC-DC systems. To assure reliable integration of such a massive amount of renewable inverter-based resources (IBRs), the IBRs should be augmented with grid support capabilities and compatibility features. To this end, this project proposes a multi-pronged research effort as follows: i) Grid Firming Converters: proposing methods to achieve desired voltage and frequency supports from IBRs such as wind and PV farms in the future grid, ii) Grid Forming Converters: developing controls to enable cold start capability of a system with high penetration of renewables, and iii) Resilient Converters: Designing fault ride through capability and post-fault recovery control strategies to avoid massive dropouts of renewables following a disturbance such as what happened in the Western system.
Academic Team Members Project Leader: Maryam Saeedifard (Georgia Tech,
Team members: Sakis Meliopoulos (Georgia Tech,, Saeed Lotfifard (Washington State,
Industry Team Members Deepak Konka (GE Renewable Energy), Harvey Scribner (SPP), Kwok Cheung (GE Renewable Energy), Mehmet Cintuglu (ABB), Nivad Navid (PARS), Melvin Schoech (CenterPoint), Miaolei Shao (GE Power), Evangelos Farantatos (EPRI), Deepak Ramasubramanian (EPRI), Wenzong Wang (EPRI), Doug Bowman (SPP), Mahesh Morjaria (First Solar), Alan Engelmann (Exelon), Mohit Singh (ConEd)
Project Period July 1, 2020 to August 31, 2022

S-89G: Nonlinear Analysis of Power System Oscillations Using Models and Measurements (ending July 2021)

Summary With the growth of renewable energy sources, the transmission flow patterns are changing in power grids all over the world. Moreover, fast dynamic devices that are built into the newer power electronic based energy interfaces and advanced control systems are interacting with the traditional power grid controls in unknown ways. These complex and nonlinear dynamic mechanisms are impacting on the small-signal and transient stability properties of the power system. This project will focus on the study of nonlinear phenomena in the presence of poorly damped oscillatory modes for understanding the impact of the nonlinearities on transient stability properties. Specifically, the analysis will develop methods to distinguish between supercritical (nonlinear stable) versus subcritical (nonlinear unstable) types of oscillatory behavior. Model based simulation and bifurcation analytic studies will be used to derive insight and theoretical understanding, and the project will then extend the analysis to measurement based algorithms to the extent possible. The project will also work on speeding up time-domain ambient modal analysis methods for reducing their computational burden, and develop efficient methods for processing real-time alarms by combining the estimation results from multiple ambient and ringdown analysis algorithms.
Academic Team Members Project Leader: Vaithianathan (Mani) Venkatasubramanian (Washington State University,
Industry Team Members Patrick Panciatici (RTE), Florent Xavier (RTE), Gilles Torresan (RTE), and Charles Faure-Llorens (RTE)
Project Period July 16, 2019 to July 15, 2021

S-88G: Risk-Sensitive Security-Constrained Economic Dispatch via Critical Region Exploration (ending August 2021)

Summary System operators (SOs) regularly solve security constrained economic dispatch (SCED) problems at various time-scales. SCED formulations often sacrifice cost considerations to prioritize reliability of power delivery. Additionally, for algorithms used to solve SCED problems on practical electric power networks, scalability remains a challenge. We propose a risk-based SCED (R-SCED) formulation that allows a SO to tradeoff between cost and reliability. Finally, we propose to develop an engineering prototype to solve R-SCED problems via the recently developed critical region exploration (CRE) algorithm.
Academic Team Members Project Leader: Subhonmesh Bose (University of Illinois at Urbana-Champaign,
Industry Team Members Tongxin Zheng
Project Period January 14, 2019 to August 31, 2021

S-87: Machine Learning Approaches for Real-time integration of Synchrophasor Data (ending August 2021)

Summary Integration of phasor measurement units (PMUs) into power system operations has the potential to revolutionize the efficiency, resiliency,  and security of the grid by offering operators an extremely detailed viewpoint into the system state. However, this integration has been slowed by the big-data challenges inherent in the use of PMU data. This proposal addresses these challenges by building data drive, machine learning (ML) models for the spatio-temporal dependencies in PMU data. these models will be used to develop advanced bad data detectors (BDDs) compression algorithms for long-term storage of PMU data, and to generate synthetic PMU data sets for use in an integrated energy management system (EMS) platform.
Academic Team Members Project Leader: Lalitha Sankar (Arizona State University,
Team Members: Le Xie (Texas A&M University,, Anamitra Pal (Arizona State University,
Industry Team Members Ruisheng Diao (GEIRI North America), Xiaohu Zhang (GEIRI North America), Phil Hart (GE), Liwei Hao (GE), Matthew Rhodes (SRP), Alan Engelmann (ComEd), Evangelos Farantatos (EPRI), Mahendra Patel (EPRI,, George Stefopoulos (NYPA), Qiang Zhang (ISO-NE), Harvey Scribner (SPP), Yingchen Zhang (NREL,, Santosh Veda (NREL), Mark Westendorf (MISO)
Project Period July 1, 2019 to August 31, 2021

S-86G: Input-Output Metrics for the Power-Grid's Swing Dynamics (ending December 2020)

Summary New technologies are providing new opportunities for wide-area control of poorly damped oscillations in the bulk power grid. A foundational need in wide-area controller design is the assessment of input-output channel properties of the power system dynamics, in addition to its internal or modal properties. The purpose of this project is to undertake a comprehensive analysis of a suite of input-output metrics for the system dynamics, from both numerical and graph-theoretic perspectives. The metric analysis will then be used to develop model-reduction techniques that maintain key input-output properties, and to support estimation of channel properties from ambient synchrophasor data.
Academic Team Members Project Leader: Sandip Roy (Washington State,
Team Members: Vaithianathan (Mani) Venkatasubramanian (Washington State,
Industry Team Members Florent Xavier (RTE-France), Patrick Panciatici (RTE-France), Thibault Prevost (RTE-France), Denis Guillaume (RTE-France)
Project Period January 1, 2019 to December 31, 2020

S-84G: Hybrid Simulation for Large-scale MMC-MTDC Embedded Power Systems (ending May 2021)

Summary Load modeling is important to the planning, operation and control of power systems. Although many studies have focused on load modeling, it is still a challenging task due to complexity and time variability of loads, and the increasing integration of distributed energy resources and power electronic loads. This project will leverage data-driven techniques and a multitude of measurement data from diverse sources to develop static and dynamic load models. The expected outcomes include load identification algorithms, validation cases, and an open-source software tool for load modeling.
Academic Team Members Project Leader: Zhauoyu Wang (Iowa State)
Industry Team Members Di Shi (GEIRINA), Yishen Wang (GEIRINA), Zhehan Yi (GEIRINA)
Project Period  May 1, 2018 to May 31, 2021

Transmission and Distribution Technologies (T&D):

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T-65: Identification and Mitigation of T&D Operational Security Vulnerabilities in Inverter Dominated Power Systems (ending August 2023)

Summary This project seeks to identify operational security vulnerabilities in T&D assets that grid integration of massive amounts of inverter-based resources (e.g. wind and solar generations, HVDC systems and inverter-interfaced energy storages) creates. We will investigate how the likely cascading outages in inverter-dominated power systems will look like as the result of new dynamics and fault response of inverter-based resources (IBRs). The project will focus on the following three main factors (1) Stability of inverters: we will analyze grid synchronization instability of IBRs and identify conditions tat controllers of IBRs negatively affect the stability of the power systems and different operating modes of IBRs (i.e. grid following, grid supporting, and grid forming modes). (2) Protection systems response: we will identify the root cause of protection system vulnerabilities versus penetration levels of IBRs and develop mitigation methods, (3) Dynamic response of loads: we will identify impacts of distinct dynamics of active power distribution systems on transmission systems as the result of large integration of IBRs in distribution systems. According to the identified vulnerabilities, we will propose mitigation strategies. Investigations and mitigation methods will be verified using commercial software such as PSS/E, CAPE, ETAP, PSCAD and realistic power system models.
Academic Team Members Project Leader: Saeed Lotfifard (Washington State University,
Team members: Venkataramana Ajjarapu (Iowa State University, and Sakis Meliopoulos (Georgia Tech,
Industry Team Members Evangelos Farantatos (EPRI), Deepak Ramasubramanian (EPRI), Parag Mitra (EPRI), Aboutaleb Haddadi (EPRI), MAsoud Abbaszadeh (GE Research), Kwok Cheung (GE Renewable Energy), Deepak Konka (GE Renewable Energy), Miaolei Shao (GE Research), Bishnu Sapkota (GE), Doug Bowman (SPP), Harvey Scribner (SPP), Wisam Alsudani (WAPA), Jianzhong Tong (PJM), Di Shi (GEIRINA), Yonghong Chen (MISO), Felicia Ruiz (MISO), Aftab Alam (CAISO), Adarsh Nagarajan (NREL), Venkat Krishnan (NREL), Murali Baggu (NREL), Ben Kroposki (NREL), Yingchen Zhang (NREL), Mehmet Cintuglu (Hitachi), Bo Yang (Hitachi), Panitarn Chongfuangprinya (Hitachi), Yohan Sutjandra (The Energy Authority), Atena Darvishi (NYPA), Mingguo Hong (ISO-NE)
Project Period July 1, 2021 to August 31, 2023

T-64: Who controls the DERs? Increasing DER hosting capacity through targeted modeling, sensing, and control (ending August 2022)

Summary Distributed energy resources (DERs) offer many opportunities for providing grid services but may also cause issues in distribution grid operations. This project develops modeling and analysis techniques for systems with many DERs that have potentially conflicting control objectives. Using DER models developed by applying machine learning to actual device data, this project will first identify a small number of locations for placing sensors relevant to monitoring key distribution grid constraints and then develop a framework for analyzing and comparing the impacts of various DER controllers on distribution grid operations.
Academic Team Members Project Leader: Line Roald (Wisconsin,
Team members: Daniel Molzahn (Georgia Tech,, Mojdeh Hedman (Arizona State,
Industry Team Members Melvin Schoech (CenterPoint), Venkat Banunarayanan (NRECA), Yohan Sutjandra (TEA), George Stefopoulos (NYPA), Patrick Panciatici (RTE-France), Jim Price (CAISO), Philip J. Hart (GE Global Research), Yazhou Jian (GE Global Research), Rajni Burra (First Solar), William Kouam (AEP), Evangelos Farantatos (EPRI), Adarsh Nagarajan (NREL), Jenny Zhao (AEP), Jesse Gantz (Centrica), Kwok Cheung (GE Renewable Energy), Bo Yang (Hitachi), Oluwaseyi Akinbode (MISO), Deepak Ramasubramanian (EPRI), Nikita Singhal (EPRI), Heng Chen (ComEd)
Project Period July 1, 2020 to August 31, 2022

T-63: Harnessing the Power of Artificial Intelligence (AI) for Transmission & Distribution Operations (ending August 2021)

Summary This project aims to harness the potential of artificial intelligence (AI) and machine learning (ML) technology for several applications in power systems that are either impossible or extremely costly to solve by the state of the art. To this end, we stress the importance of embedding power system physical characteristics in AI/ML solution frameworks. The specific topics that we focus on are (1) machine learning tools for congestion and contingency forecasting, and (3) deep learning for state estimation and control of systems with limited observability and unmodeled dynamics.
Academic Team Members Project Leader: Yang Weng (Arizona State University,
Team members: Lang Tong (Cornell University,, Anamitra Pal (Arizona State University,
Industry Team Members Evangelos Farantatos (EPRI), Mahendra Patel (EPRI), Yingchen Zhang (NREL), Santosh Veda (NREL), Mark Westendorf (MISO), Matthew Rhodes (SRP), Di Shi (GEIRI), Zhe Yu (GEIRI), Jianzhong Tong (PJM), Harvey Scribner (SPP), Bajarang Agrawal (APS), George Stefopoulos (NYPA), Atena Darvishi (NYPA), Honggang Wang (GE), Ming Jin (GE), Phil Hart (GE), Zhongyu Wu (MISO), Chetan Mishra (Dominion Energy)
Project Period July 1, 2019 to August 31, 2021

T-61: Optimal Model Coordination for Integrated Transmission and Distribution Systems (ending December 2020)

Summary As distributed energy resources (DERs) are more widely deployed, mainly within the distribution system, traditional models for T&D networks may not be satisfactory. Historically, distribution and transmission have been loosely coupled because the majority of energy resources were interconnected at the transmission system level. The emerging changes to distribution systems are driving the industry to consider modeling greater detail of the distribution system, and potentially co-simulation between transmission and distribution models. The challenge is determining how much of the distribution system needs to be modeled and whether all three phases (with unbalance) need to be included in the models. The goal is to provide planners with sufficient data to form an accurate view of the system with increasing penetrations of DER. This project will identify the necessary details that are required to provide a reasonably accurate picture of future T&D systems. The following questions will be addressed in this work: (a) With more generation at the distribution level, do all, or only a subset, of distribution feeders need to be modeled? (b) How effective are composite load models, as increasing levels of DER are connected? (c) What is the incremental value of co-simulation? (d) To what extent can existing proprietary applications such as GE PSLF and Cyme be used to support future needs? Separate models for transmission that include critical amount of information of distribution, and for distribution that include relevant transmission characteristics, in place of a fully combined model will be investigated. Existing models will be evaluated, and new models will be developed as needed, by comparing with traditional separated T&D framework and a fully combined T&D co-simulation.
Academic Team Members Project Leader: Visvakumar Aravinthan (Wichita State University)
Team members: Lindsay Anderson (Cornell University); Judith Cardell (Smith College); Ward Jewell (Wichita State University)
Industry Team Members Jay Caspary (SPP), Mirrasoul Moussavi (Energy & Automation ABB), Jim Price (CAISO), Deepak Ramasubramanian (EPRI), Jens Boemer (EPRI), Anish Gaikwad (EPRI), Parag Mitra (EPRI), Evangelos Farantatos (EPRI), Miaolei Shao (GE Power), Michael Swider (New York ISO), Devin Van Zandt (GE), Rui Yang (NREL),  Tongxin Zheng, (ISO New England)
Project Period July 1, 2018 to December 31, 2020