The Photovoltaic (PV) Analysis and Response Support (PARS) platform is a digital twin for power grids, offering real-time situational awareness and optimal response plan selection.

●        Real-time Transmission-Distribution System Co-simulation Platform: This platform operates on either a 4-core or 8-core OPAL-RT real-time simulation system. This real-time, hardware-in-the-loop co-simulation platform enables closed-loop testing of power system protection, control, and energy management algorithms. The transmission and distribution network model and data are developed from actual utility data sets, including over 50 power distribution feeders in North Carolina and 15-minute load profiles from 12,000 smart meters. Use of the feeder data is protected under Non-disclosure Agreements. The platform was developed in collaboration with Total SA and Pacific Northwest National Laboratory. Since 2016, the team has advanced real-time co-simulation technologies with funding from Total SA and DOE SETO through the SUNLAMP and ASSIST programs, with total project funding exceeding $6 million and infrastructure costs over $200,000.

●        Access to Distribution Network Models, Smart Meter, and SCADA Data: Our industry partners include major vendors such as ABB, Siemens, and General Electric; Total SA; utilities, cooperatives, and municipal electric companies, including NYPA, Duke Energy, ElectriCities, Wilson Energy, New River Electrical Corporation, and Roanoke Electric Cooperative; and national labs, such as Pacific Northwest National Lab and the National Renewable Energy Laboratory. These collaborations provide us with access to real utility transmission and distribution network models and extensive operational data sets, including over 20,000 smart meter data sets at 1-minute, 5-minute, 15-minute, and 30-minute intervals for up to four years, data from more than 20 residential, commercial, and rural feeders, outage management data, and high-resolution data (from 1-second to 5-minute intervals) from over 100 PV farms.

●        Smart Meter and Feeder Load Disaggregation Tools: Developed in MATLAB/Python at NCSU, this tool assigns load profiles from feeder tops to load nodes. Using selected features, it can assign a one-year load profile to each load node and determine the number of buildings per node based on load composition and load type.

●        Distribution Feeder Volt-Var Control Tools: Our team has developed reinforcement learning-based approaches to regulate voltage in distribution feeders with high penetration of renewable energy sources, electric vehicles, controllable loads, and diesel generators.

●        GitHub repository: We have developed a suite of tools for generating synthetic data and topologies, all of which are available on Githu: https://github.com/SyntheticDataGenerationAndSharing/SDG_Algorithms-Data

Major Presentations

1. Overviews of the PARS platform

   • 1. 1. • 2021. DOE workshop. An Overview of the PARS Platform by Dr. Ning Lu.

           • 2022. Invited Talk at the EESG MIT seminar series. available on Youtube: Development of the Photovoltaic (PV) Analysis and Response Support (PARS) platform

           • 2022. Bigdata Seminar. Available on Youtube: Machine learning based methods used to develop the PARS platform.

           • 2023, Invited Talk at Stanford University, CA. available on Youtube: Power System Digital Twins for Distribution Systems | Ning Lu | Smart Grid Seminar

           • 2024 UNIFI Seminar. available on Youtube:   Unlocking the Potential of Power System Digital Twins with Synthetic Data  : Slides

           • 2024 NREC meeting: Leveraging Machine Learning / Artificial Intelligence to Unlock the Potential of Measurement Data

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2. PARS Energy Management Systems

   • 1. 1. • Energy management systems by Ashwin Shirsat, Valliappan Muthukaruppan, Rongxing Hu. at the FREEDM seminar series.

           • demo 1 by Rongxing Hu: MW microgrid: https://youtu.be/1kIBTnE8V24; Demo link 2: A recording of one complete run: https://youtu.be/b5sv8SozFSk

           • demo 2: mobile storage by Valliappan Muthukaruppan: Presentation Link: https://youtu.be/Sr-OC075gZo; Demo link: https://youtu.be/I885azl3hpU

           • demo 3: community microgrid by Ashwin Shirsat:  https://youtu.be/z1D5T1R9abI

3. PARS Situation Awareness:

   • 1. 1. • FeederGan presentation: https://www.youtube.com/watch?v=r8cmSDyxIJ8. By Dr. Ming Liang at PES General meeting

           • ProfileSR-GAN: https://www.youtube.com/watch?v=nBkwTqHplh8&t=30s By Lidong Song at the FREEDM seminar series.

           • Meta-learning based load forecasting tool by Dr. Yiyan Li at the FREEDM seminar series.

           • Bigdata Seminar about the machine learning based methods used to develop the PARS platform: https://www.public.asu.edu/~kghosh10/Tutorial5/56_talk_lu_li_song.html

4. PARS Hardware-in-the-loop Team

   • Fuhong Xie: Battery Parameterization at FREEDM Tech Seminar Series

   • Victor Paduani: Maximum Power Reference Tracking Algorithm for Power Curtailment of PV Systems, 21PESGM0055 - Best paper session

   • Jiyu Wang: “A data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method”, 21PESGM0790

Major Publications By Budget Period

Budget Period 3 (March 2021 - March 2023)

1. Victor Paduani, Qi Xiao, Bei Xu, David Lubkeman, and Ning Lu. "Optimal Control Design for Operating a Hybrid PV Plant with Robust Power Reserves for Fast Frequency Regulation Services", submitted to IEEE Transactions on power systems. https://arxiv.org/abs/2212.03803 

2. Li, Yiyan, Lidong Song, Yi Hu, Hanpyo Lee, Di Wu, P. J. Rehm, and Ning Lu. "Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation." arXiv preprint arXiv:2211.16332 (2022). 

3. Yi Hu,  Yiyan Li, Lidong Song, Han Pyo Lee, PJ Rehm, Matthew Makdad, Edmond Miller, and Ning Lu, "MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations," submitted to IEEE Transactions on Smart Grid (2022). Available online at: https://arxiv.org/abs/2210.01167

4. Han Pyo Lee, PJ Rehm, Matthew Makdad, Edmond Miller, Ning Lu, "A Novel Data Segmentation Based Approach for Meter Topology Identification using Smart Meter Voltage and Power Measurements" submitted to IEEE Transactions on Smart Grid (2022), Available online at: http://arxiv.org/abs/2210.00155 

5. Mingzhi Zhang, Xiangqi Zhu, and Ning Lu. "A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources", submitted to IEEE Transaction on Sustainable Energy. 2021. Available online at: https://arxiv.org/abs/2110.07406.

6. V. Muthukaruppan, A. Shirsat, et. al., “Feeder Microgrid Management on an Active Distribution System during a Severe Outage”, submitted to IEEE Trans. on Power System, 2022 (available: arXiv:2208.10712).

7. Alrushoud, Asmaa, and Ning Lu. "A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources." Available online at: arXiv e-prints (2021): arXiv-2105, submitted to IEEE Transactions on Smart Grid.

8. Shirsat, Ashwin, Valliappan Muthukaruppan, Rongxing Hu, Victor Paduani, Bei Xu, Lidong Song, Yiyan Li et al. "A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using a Community Microgrid." IEEE Transactions on Sustainable Energy (2023).  

9. V. Muthukaruppan, A. Shirsat, et. al., “Feeder Microgrid Management on an Active Distribution System during a Severe Outage”, submitted to IEEE Trans. on Power System, 2022 (available: arXiv:2208.10712).

10. Hyeonjin Kim, Kai Ye, Han Pyo Lee, Rongxing Hu, Di Wu, PJ Rehm, and Ning LU, “An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data” submitted to 2023 ISGT. Available online at: https://arxiv.org/abs/2209.09165 

11. Rongxing Hu, Kai Ye, Hyeonjin Kim, Hanpyo Lee, Di Wu, P.J. Rehm, Ning Lu "Coordinative Demand Charge Mitigation Strategies," submitted to 2023 IEEE PES General Meeting, Available online at: https://arxiv.org/abs/2212.08535. 23PESGM1367

12. Kai Ye,  Hyeonjin Kim, Di Wu, PJ Rehm, and Ning Lu, "A Modified Sequence-to-point HVAC Load Disaggregation Algorithm," submitted to 2023 IEEE PES General Meeting, Available online at: https://arxiv.org/abs/2212.04886. 23PESGM1248  

13. Han Pyo Lee, Lidong Song, Yiyan Li, Ning Lu, Di Wu, PJ Rehm, Matthew Makdad, Edmond Miller, "An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation" 23PESGM0022, submitted to 2023 IEEE PES General Meeting, Available online at: http://arxiv.org/abs/2211.03733. 23PESGM0022  

Budget Period 2 (March 2021 - March 2022)

1. Yiyan Li, Lidong Song, Si Zhang,  Laura Kraus, Taylor Adcox, Roger Willardson, Abhishek Komandur, and Ning Lu, “TCN-based Spatial-Temporal PV Forecasting Framework with Automated Detector Network Selection,” submitted to IEEE Trans. Sustainable Energy. https://arxiv.org/abs/2111.08809.

2. Lidong Song, Yiyan Li, and Ning Lu. "ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles." Submitted to IEEE Trans. Smart Grid, under 2st round review. https://arxiv.org/abs/2107.09523

3. Mingzhi Zhang, Xiangqi Zhu, and Ning Lu, “A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources,” Submitted to IEEE Trans. Smart Grid. https://arxiv.org/abs/2110.07406.

4. Bei Xu, Victor Paduani, David Lubkeman, and Ning Lu, “A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources,” submitted to 2022 PES General meeting. https://arxiv.org/pdf/2111.09464.pdf

5. Victor Paduani, Bei Xu, David Lubkeman, Ning Lu, “Novel Real-Time EMT-TS Modeling Architecture for Feeder Blackstart Simulations,” submitted to 2022 IEEE PESGM. https://arxiv.org/pdf/2111.10031.pdf

6. Hanpyo Lee, Han Pyo Lee, Mingzhi Zhang,Mesut Baran, Ning Lu, PJ Rehm, Edmond Miller, Matthew Makdad P.E., “A Novel Data Segmentation Method for Data-driven Phase Identification,” submitted to 2022 PES General Meeting. http://arxiv.org/abs/2111.10500

7. Si Zhang, Mingzhi Zhang, Rongxing Hu, David Lubkeman, Yunan Liu, and Ning Lu, “A Two-stage Training Strategy for  Reinforcement Learning based Volt-Var Control,” submitted to 2022 PES General Meeting. https://arxiv.org/abs/2111.11987

8. Xinda Ke, Ahmad Tbaileh, Quan Nguyen, Tamara Becejac, Mallikarjuna R. Vallem, Nader Samaan, “A Solar-assisted Voltage Optimization Method for Transmission Solar Network Power System,” submitted to 2022 PES General Meeting.

9. Xie, F., McEntee, C., Zhang, M., Lu, N., Ke, X., Vallem, M.R. and Samaan, N., 2021, April. Networked HIL Simulation System for Modeling Large-scale Power Systems. In 2020 52nd North American Power Symposium (NAPS) (pp. 1-6). IEEE. 

Budget Period 1 (March 2020 - March 2021)

10. Li, Yiyan, Si Zhang, Rongxing Hu, and Ning Lu. "A meta-learning based distribution system load forecasting model selection framework." Applied Energy 294 (2021): 116991. 

11. F. Xie, C. McEntee, M. Zhang, B. Mather and N. Lu, Development of an Encoding Method on an Co-simulation Platform for Mitigating the Impact of Unreliable Communication, IEEE transactions on smart grid

12. Q. Long, H. Yu, F. Xie, N. Lu and D. Lubkeman, Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm, IEEE Transactions on Smart Grid

13. M. Liang, Y. Meng, J. Wang, D. Lubkeman and N. Lu, FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets, IEEE Transactions on Smart Grid

14. H. Yu, M. A. Awal, H. Tu, I. Husain and S. Lukic, Comparative Transient Stability Assessment of Droop and Dispatchable Virtual Oscillator Controlled Grid-Connected Inverters, IEEE Transactions on Power Electronics

15. Wang, Jiyu, Xiangqi Zhu, Ming Liang, Yao Meng, Andrew Kling, David L. Lubkeman, and Ning Lu, A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method., IEEE Transactions on Smart Grid

16. Rongxing Hu, Yiyan Li, Si zhang, Valliappan Muthukaruppan, Ashwin Shirsat, Mesut Baran, Wenyuan Tang, David Lubkeman, Ning Lu, MW-level PV powered microgrid energy management, 2021 PES General Meeting

17. Victor Paduani, Lidong Song, Bei Xu, Dr. Ning Lu, PV power tracking control for providing ancillary services, 2021 PES General Meeting

18. Ashwin Shirsat, Valliappan Muthukaruppan, Rongxing Hu, Ning Lu, Mesut Baran, David Lubkeman, Wenyuan Tang, Community-level PV powered dynamic microgrid energy management, 2021 PES General Meeting

19. Quan Nguyen, Mallikarjuna R. Vallem, Bharat Vyakaranam, Ahmad Tbaileh, Xinda Ke, Nader Samaan, Controlling PV hybrid system for Black start, 2021 PES General Meeting

20. Ahmad Tbaileh, Mallikarjuna R. Vallem, Quan Nguyen, Xinda Ke, Nader A. Samaan, George Stefopoulos, Xia Jiang, Optimal Power System Black start using Inverter-Based Generation, 2021 PES General Meeting

21. H. Yu, M. A. Awal, H. Tu, Y. Du, S. Lukic and I. Husain, A Virtual Impedance Scheme for Voltage Harmonics Suppression in Virtual Oscillator Controlled Islanded Microgrids, 2020 IEEE Applied Power Electronics Conference and Exposition

22. Asmaa Alrushoud, Catie McEntee, and Ning Lu, A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems, 2021 PES General Meeting

23. Long Qian, Hui Yu, Fuhong Xie, Wenti Zeng, Srdjan Lukic, Ning Lu, and David Lubkeman, Microgrid Power Flow Control with Integrated Battery Management Functions, 2020 PES General Meeting, 

24. A. Alrushoud and N. Lu, Impacts of PV Capacity Allocation Methods on Distribution Planning Studies, 2020 T&D Meeting

Prior Works the PARS Platform was developed upon.

25. Jiyu Wang, Ning Lu, Sen Huang, and Di Wu, A Data-driven Control Method for Operating the Commercial HVAC Load as a Virtual Battery, 2019 PES General Meeting

26. F. Xie, C. McEntee, M. Zhang and N. Lu, "An Asynchronous Real-time Co-simulation Platform for Modeling Interaction between Microgrids and Power Distribution Systems," 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019

27. F. Xie, H. Yu, Q. Long, W. Zeng and N. Lu, "Battery Model Parameterization Using Manufacturer Datasheet and Field Measurement for Real-Time HIL Applications," in IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2396-2406, May 2020, doi: 10.1109/TSG.2019.2953718.

28. Q. Long, H. Yu, F. Xie, N. Lu and D. Lubkeman, "Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.3026617.

29. Wang, Jiyu, Xiangqi Zhu, Ming Liang, Yao Meng, Andrew Kling, David L. Lubkeman, and Ning Lu. "A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method." IEEE Transactions on Smart Grid 11, no. 6 (2020): 5396-5406.

30. M. Liang, Y. Meng, J. Wang, D. Lubkeman and N. Lu, "FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.3025259.

31. Ming Liang, Jiyu Wang, Yao Meng, Ning LU, David Lubkeman, and Andrew Kling. "A Sequential Energy Disaggregation Method using Low-resolution Smart Meter Data, " Proc. of IEEE Innovative Smart Grid Technologies, Washington DC, 2019.

32. Jian Lu, Modeling and Controller Design of a Community Microgrid, PhD Thesis, 2018. https://repository.lib.ncsu.edu/bitstream/handle/1840.20/35168/etd.pdf?sequence=1

33. L. Sun and D. Lubkeman, "Agent-Based Modeling of Feeder-Level Electric Vehicle Diffusion for Distribution Planning," in IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 751-760, Jan. 2021, doi: 10.1109/TSG.2020.3013641.

34. Technical Reference Document Dynamic Load Modeling, NERC, Dec. 2016. Available online: https://www.nerc.com/comm/PC/LoadModelingTaskForceDL/Dynamic%20Load%20Modeling%20Tech%20Ref%202016-11-14%20-%20FINAL.PDF

35. N Lu, F Puyleart, S Yang, Y Xie, and Z Huang. 2008. “Load Component Database of Household Appliances and Small Office Equipment.” In: Proc. of the 2008 IEEE PES General Meeting, Pittsburg, Pennsylvania.

36. N Lu, B Yang, and Z Huang. 2008. Evaluation of Southern California Edison Air-Conditioner Stalling Solutions. CEC/LMTF Load Research Program. PNNL-17686, Pacific Northwest National Laboratory, Richland, Washington.

37. N Lu, YL Xie, and Z Huang. 2008. Air Conditioner Compressor Performance Model. CEC/LMTF Load Research Program. PNNL-17796, Pacific Northwest National Laboratory, Richland, Washington.

38. N Lu, A Qiao, Composite load model evaluation. PNNL-16916, Pacific Northwest National Laboratory, Richland, WA. 2007

39. DP Chassin and N Lu. 2007. “Load Composition Data.” PNNL-SA-57496, Pacific Northwest National Laboratory, Richland, Washington.

Data Sets

Real-time Simulation Test Systems