I have RA/TA Scholarships and Postdoc positions available at New Mexico State University. Please check this letter if interested: [Link].
Dr. Di Shi is an associate professor with the Klipsch School of Electrical and Computer Engineering at New Mexico State University (NMSU).
Before joining NMSU, Dr. Shi founded AInergy, a startup providing AI and IoT driven solutions for power systems. Prior to AInergy, he was a Researcher, Director of Fundamental R&D Center and Department Head of the AI & System Analytics Group at GEIRINA. Prior to GEIRINA, he held various research positions at NEC Laboratories America (NECLA), Electric Power Research Institute (EPRI), and ASU. He also worked as (part-time) Principal/Senior Consultant for two consulting firms.
Dr. Shi serves as Editor of IEEE Transactions on Power Systems, IEEE Power Engineering Letters, IET Generation, Transmission & Distribution, Journal of Modern Power Systems and Clean Energy, and Chair of IEEE San Francisco Section Chapter PES. He also serves as Chair of IEEE TF of IoT for Power Systems, and Secretary of IEEE Working Group of Machine Learning for Power Systems. He served as editor of IEEE Transactions on Smart Grid during 2017-2021. He is a Fellow of IET.
Dr. Shi authored/co-authored over 180 journal and conference papers, and holds 42 patents/patent applications. He and his team have received 11 Best Paper Awards from various journals and conferences. He led his team to win the Championship of the 2019 Power System AI competition L2RPN. Two technologies and solutions he and his team developed have been commercialized into products that help customers solve real-world problems with significant energy savings. Several software packages and platforms he developed have been adopted by utilities to run at multiple grid dispatch centers.
Here is a link showcasing the research outcomes and broader impacts from an NSF project I am leading: [Link].
PhD in Electrical Engineering, 2012
Arizona State University
MS in Electrical Engineering, 2009
Arizona State University
BS in Electrical Engineering, 2007
Xi'an Jiaotong University
[GRANTED: 28]
P1. D. Shi, R. Sharma, and F. Guo, “Wide Area Measurement System (WAMS) Based Control of Grid-scale Storage (GSS) for Power System Stability Enhancement,” US Patent 9,964,572.
P2. D. He, D. Shi, and R. Sharma, “Consensus-based Distributed Cooperative Control for Microgrid Voltage Regulation and Reactive Power Sharing,” US Patent 9,882,386.
P3. D. Shi, and R. Sharma, “Enhancing Power System Voltage Stability Using Grid Energy Storage for Voltage Support,” US Patent 9,866,029.
P4. J. Zhao, D. Shi, and R. Sharma, “Microgrid Reactive Power Management for Voltage Regulation During and Subsequent to Islanding,” US Patent 9,812,870.
P5. D. Shi, R. Sharma, and Y. Luo, “Synchronization Control for Reconnecting Microgrid to Main Grid After Islanding,” US Patent 9,720,395.
P6. Y. Ye, R. Sharma, and D. Shi, “Adaptive Control of Hybrid Ultracapacitor-battery Storage System for Photovoltaic Output Smoothing,” US Patent 9,536,205.
P7. D. Shi, R. Sharma, and Y. Ye, “Distributed Generation Control for Microgrid During Islanding,” US Patent 9,411,389.
P8. R. Sharma, D. Shi, B. Asghari, and R. Patil, “Management of Grid Scale Energy Storage System for Multiple Services,” US Patent 10,205,317.
P9. X. Lu, X. Wang, D. Shi, Z. Wang, “Data Mining based Approach for Online Calibration of Phasor Measurement Unit,” US Patent 10,551,471.
P10. Z. Yi, Y. Wang, B. Huang, D. Shi, Z. Wang, “Model Predictive Controller for Autonomous Hybrid Microgrids”, US Patent 10,651,654.
P11. Z. Yi, H. Li, Z. Wang, R. Diao, X. Zhao, and D. Shi, “MPC-Based PV Maximum Power Point Tracker for Transformerless H5 Inverter with Leakage Current Reduction,” US Patent 10,770,904.
P12. Z. Yu, Y. Meng, D. Shi, X. Lu, et al., “Oscillation Source Location Method, Device, Terminal and Readable Storage Medium,” Chinese Patent CN108574290.
P13. G. Li, Y. Lei, C. Xu, X. Zhang, and D. Shi, “A Clustering of Power System Operation Mode Based on Sparse Autoencoder,” Chinese Patent CN109711483.
P14. D. Shi, X. Lu, X. Chen, et al., “Method and Device for Online Calibration of Synchrophasor Measurement Unit,” Chinese Patent CN107132500.
P15. D. Shi, Y. Xiang, D. Bian, G. Zhao, et al., “Method and System for Control of Energy Block for Flexible Load”, Chinese Patent CN109768555.
P16. J. Duan, Z. Yi, X. Lu, D. Shi, and Z. Wang, “Optimal Charging and Discharging Control for Hybrid Energy Storage System based on Reinforcement Learning,” US Patent 10,985,572.
P17. X. Zhang, D. Shi, X. Lu, et al., “Sensitivity Based Thevenin Index for Voltage Stability Assessment Considering N-1 Contingency,” US Patent 11,029,344.
P18. X. Lu, D. Shi, Y. Xiang, H. Li, C. Xu, et al., “Smart outlet system with fast frequency tracking for power system frequency control using distributed appliances,” US Patent 11,114,893.
P19. H. Li, X. Lu, Y. Xiang, C. Xu, D. Shi, Z. Yu, S. Xu, et al., “An Adaptive Method for Aggregation of Distributed Loads to Provide Emergency Frequency Support,” US Patent 11,178,610.
P20. Z. Yu, Y. Wang, H. Li, C. Fu, Z. Wang, and D. Shi, “Bayesian Estimation Based Parameter Estimation for Composite Load Model,” US Patent 11,181,873.
P21. Y. Wang, H. Li, D. Shi, Q. Zhang, C. Xu, and Z. Wang, “Submodular Load Clustering with Robust Principal Component Analysis,” US Patent 11,264,799.
P22. Y. Gu, G. Tian, C. Xu, Q. Zhang, R. Diao and D. Shi, “Systems and Methods of Power System State Estimation,” US Patent 11,320,492.
P23. R. Diao, D. Shi, B. Zhang, S. Wang, et al., “Multi-objective Real-time Power Flow Control Method Using Soft Actor-Critic,” US Patent 11,336,092.
P24. D. Shi, X. Zhao, F. Xue, H. Peng, J. Qin, C. Jing, and Z. Wang, “Generalized Equivalent Circuit Model of MMC-HVDC for Power System Simulation,” US Patent 11,404,973.
P25. Y. Gu, G. Tian, C. Xu, H. Wu, Z. Yu, and D. Shi, “Systems and Methods of Bad Data Identification and Recovery for Electric Power Systems,” US Patent 11,480,594.
P26. D. Bian, X. Zhang, D. Shi, R. Diao, S. Wang, and Z. Liang, “Deep Reinforcement Learning Based Real-time scheduling of ESS in Commercial Campus,” US Patent 11,610,214.
P27. Y. Wang, X. Wang, H. Li, C. Xu, D. Shi, Y. Lin, S. Wang, et al., “Systems and Methods of Composite Load Modeling for Electric Power Systems,” US Patent 11,900,031.
P28. R. Diao, D. Shi, B. Zhang, S. Wang, and J. Duan, “A Method for Constructing a Power Grid Dispatch Control Model and a Power Grid Dispatch Control Strategy,” Chinese Patent CN111864743.
2023 Best Paper of Journal of Modern Power Systems and Clean Energy (MPCE) for year 2022 (Title of paper: Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties)
2023 Best Paper Finalist, 2023 IEEE Innovative Smart Grid Technologies (ISGT) (Title of paper: Edge-Computing Based Dynamic Anomaly Detection for Transmission Lines; the top 5 papers were selected as finalists)
2022 Best Papers Award, 2022 IEEE PES General Meeting (Title of paper: Coordinated Frequency Control through Safe Reinforcement Learning)
2021 Best Paper of Journal of Modern Power Systems and Clean Energy (MPCE) for year 2020 (Title of paper: A data-driven method for fast AC optimal power flow solutions via deep reinforcement learning)
2021 IEEE PES Outstanding Engineer Award, San Francisco Power and Energy Chapter
2020 Best Paper of Journal of Modern Power Systems and Clean Energy (MPCE) for year 2019 (Title of paper: Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources)
2020 Best Papers Award, 2020 IEEE PES General Meeting (Title of paper: Extended Prony analysis on power system oscillation under a near-resonance condition)
2019 Two Best Papers Awards, IEEE Sustainable Power and Energy Conference (iSPEC) (Titles of papers: Community microgrid planning considering building thermal dynamics, Electromechanical transient modeling of modular multilevel converter based HVDC network)
2019 Championship, International AI Competition Hosted by RTE France “Learning to Run a Power Network (L2RPN)”
2019 Best Track Paper Award, IEEE APPEEC 2019 (Title of paper: Improvement of Electromechanical Mode Identification from Ambient Data with Stochastic Subspace Method)
2019 Best Papers Award, IEEE PES General Meeting 2019 (Title of paper: Autonomous voltage control for grid operation using deep reinforcement learning)
2018 Outstanding Researcher Award, GEIRI North America
2017 Researcher of the Year, GEIRI North America
2017 Best Papers Award, IEEE PES General Meeting 2017 (Title of paper: PMU assisted power system parameter calibration at Jiangsu Electric Power Company)
2016 Outstanding Performance Award, GEIRI North America
2016 President Award, GEIRI North America
2015 Best Reviewer, IEEE Transactions on Smart Grid
2014 NEC Labs Technology Commercialization Award
2014 NEC Spot Recognition Award for “Contribution in Bringing NECLA Grid Scale ESS Expertise”
2013 NEC Spot Recognition Award for “Success of Proof-of-Concept Project at MPI Indonesia”
2013 NEC Spot Recognition Award for “Success of Proof-of-Concept Project at Celcom Maylasia”
2012 University Graduate Fellowship, Arizona State University
2011 DEED Scholarship ($4,000 Research Fund), American Public Power Association
2009 University Graduate Fellowship, Arizona State University
2007 Champion of Asia-Pacific Robot Contest 2007 (representing China)
2007 Champion of China Domestic Contest for Robocon 2007
2007 Outstanding Graduate of Shaan Xi Province (top 1%)
2004-2007 A total of five Scholarships from XJTU (all for top 1%) (TangWenZhi Scholarship, BMW Scholarship, ZhongJiaoTongLi Scholarship, SiYuan Scholarship 1st Prize, Freshman Scholarship 1st Prize)
Google scholar: https://scholar.google.com/citations?user=3gfrp9EAAAAJ&hl=en&oi=ao
[Journal Papers]
[91] D. Shi, R. Hu, Y. Wu, et al., “Towards AI-Empowered Smart Power Grid: Forecasting, Dispatch and Control,” IEEE Power & Energy Magazine, vol. 22, no. 6, Nov/Dec 2024. DOI: 10.1109/MPE.2024.3408111.
[90] H. Davoudi, F. Wang, Y. Chen, D. Shi, and A. Xavier, and F. Qiu, “Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets,” IEEE Trans. Energy Markets, Policy and Regulation, 2024.
[89] Y. Zhou, L. Zhou, Z. Yi, D. Shi, and M. Guo, “Leveraging AI for Enhanced Power Systems Control: An Introductory Study of Model-Free DRL Approaches,” IEEE Access, 2024.
[88] S. Nematshahi, D. Shi, F. Wang, and B. Yan, “Deep Reinforcement Learning Based Voltage Control Revisited,” IET Generation, Transmission & Distribution, 2023.
[87] Y. Lin, X. Zhang, J. Wang, D. Shi, and D. Bian, “Voltage Stability Constrained Optimal Power Flow for Unbalanced Distribution System Based on Semidefinite Programming,” Journal of Modern Power Systems and Clean Energy, 2022.
[86] R. Ma, Z. Yi, Y. Xiang, D. Shi, C. Xu, H. Wu, “A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning,” IEEE Transactions on Industrial Electronics, 2022.
[J85] Z. Liang, D. Bian, X. Zhang, and D. Shi, “Risk-constrained Energy Management Strategy for a Commercial Campus Considering Comprehensive Reserves Against Islanding Conditions,” CSEE Journal of Power and Energy Systems, 2021.
[J84] M. Kamruzzaman, J. Duan, D. Shi, and M. Benidris, “A Deep Reinforcement Learning-based Multi-agent Framework to Enhance Power System Resilience using Shunt Resources,” IEEE Transactions on Power Systems, 2021.
[J83] M. Kamruzzaman, X. Zhang, M. Abdelmalak, D. Shi, and M. Benidris, “A Data-driven Accurate Battery Model to Use in Probabilistic Analysis of Power Systems,” Journal of Energy Storage, 2021.
[J82] Y. Zhou, W. Lee, R. Diao, and D. Shi, “Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties,” Journal of Modern Power Systems and Clean Energy, 2021.
[J81] Z. Zhou, Y. Xiang, H. Xu, Y. Wang, and D. Shi, “Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network,” Journal of Modern Power Systems and Clean Energy, 2021.
[J80] Y. Lin, Y. Wang, J. Wang, D. Shi, “Tensor-based Parameter Reduction of Dynamic Load Models with Variable Frequency Drive,” IEEE Transactions on Power Systems, 2021.
[J79] M. Cui, F. Li, H. Cui, S. Bu, D. Shi, “Data-driven Joint Voltage Stability Assessment Considering Load Uncertainty: A Variational Bayes Inference Integrated with Multi-CNNs,” IEEE Transactions on Power Systems, 2021.
[J78] Y. Xiang, Z. Yi, X. Lu, Z. Yu, D. Shi, C. Xu, “Distributed Frequency Emergency Control with Coordinated Edge Intelligence,” Electric Power Systems Research, 2021.
[J77] G. Tian, Y. Gu, Z. Yu, Q. Zhang, D. Shi, Q. Zhou, Z. Wang, “Enhanced Denoising Autoencoder Aided Bad Data Filtering for Synchrophasor-based State Estimation,” CSEE Journal of Power and Energy Systems, 2021.
[J76] P. Xu, J. Duan, J. Zhang, Y. Pei, D. Shi, Z. Wang, X. Dong, and Y. Sun, “Active Power Correction Strategies Based on Deep Reinforcement Learning-Part I: A Simulation-driven Solution for Robustness,” CSEE Journal of Power and Energy Systems, 2021.
[J75] S. Chen, J. Duan, Y. Bai, J. Zhang, D. Shi, Z. Wang, X. Dong, and Y. Sun, “Active Power Correction Strategies Based on Deep Reinforcement Learning-Part II: A Distributed Solution for Adaptability,” CSEE Journal of Power and Energy Systems, 2021.
[J74] S. Li, D. Tylavsky, D. Shi, and Z. Wang, “Implications of Stahl’s Theorems to Holomorphic Embedding Pt. I: Theoretical Convergence,” CSEE Journal of Power and Energy Systems, 2021.
[J73] A. Dronamraju, S. Li, Q. Li, Y. Li, D. Tylavsky, D. Shi, and Z. Wang, “Implications of Stahl’s Theorems to Holomorphic Embedding Pt. II: Numerical Convergence,” CSEE Journal of Power and Energy Systems, 2021.
[J72] G. Tian, Y. Gu, D. Shi, J. Fu, Z. Yu, and Q. Zhou, “Neural Network-based Power System State Estimation with Extended Observability,” Journal of Modern Power Systems and Clean Energy, 2021.
[J71] Y. Gu, Z. Yu, R. Diao, and D. Shi, “Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation,” Journal of Modern Power Systems and Clean Energy, 2020.
[J70] Y. Zhou, B. Zhang, C. Xu, T. Lan, R. Diao, D. Shi, Z. Wang, W. Lee, “A Data-driven Method for Fast AC OPF Solutions via Deep Reinforcement Learning,” Journal of Modern Power Systems and Clean Energy, 2020. [Best Paper]
[J69] Y. Meng, Z. Yu, N. Lu and D. Shi, “Time Series Classification for Locating Forced Oscillation Sources,” IEEE Transactions on Smart Grid, 2020.
[J68] M. AlAshery, Z. Yi , D. Shi, X. Lu, C. Xu, Z. Wang and W. Qiao, “A Blockchain-Enabled Multi-Settlement Quasi-Ideal Peer-to-Peer Trading Framework,” IEEE Transactions on Smart Grid, 2020.
[J67] W. Li, M. Yi, M. Wang, Y. Wang, D. Shi, and Z. Wang, “Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation,” IEEE Trans. Power Syst., 2020.
[J66] S. Wang, R. Diao, C. Xu, D. Shi and Z. Wang, “On Multi-Event Co-Calibration of Dynamic Model Parameters Using Soft Actor-Critic,” IEEE Transactions on Power Systems, 2020.
[J65] Y. Wang, W. Zhang, H. Sun, Y. Xiang, D. Shi, and Z. Wang, “Research on Fast Response Criterion of Power Grid Distributed Loads after HVDC Block Fault,” IET Generation, Transmission & Distribution, 2020.
[J64] Z. Zhou, Y. Xiang, H. Xu, Z. Yi, D. Shi and Z. Wang, “A Novel Transfer Learning based Intelligent Non-intrusive Load Monitoring with Limited Measurements,” IEEE Transactions on Instrumentation & Measurement, 2020.
[J63] Z. Zhou, Y. Xiang, H. Xu, Y. Wang, D. Shi and Z. Wang, “Self-organizing Probability Neural Network Based Intelligent Non-Intrusive Load Monitoring with Applications to Low-cost Residential Measuring Devices,” Transactions of the Institute of Measurement and Control, 2020.
[J62] Y. Hashmy, Z. Yu, D. Shi and Y. Weng, “Wide-area Measurement System-based Low Frequency Oscillation Damping Control through Reinforcement Learning,” IEEE Transactions on Smart Grid, 2020.
[J61] Y. Xiang, X. Zhang, D. Shi, R. Diao and Z. Wang, “Robust Optimization for Transmission Defense against Multi-Period Attacks with Uncertainties,” International Journal of Electrical Power and Energy Systems, 2020.
[J60] K. Yu, W. Guo, X. Chen, D. Shi, J. Wang, L. Gan, “Research on Dynamic Control of Low-voltage Distribution Network with High Penetration of Electric Heat Pumps based on uPMU Measurements and Data-driven Approach,” IET Generation, Transmission & Distribution, 2020.
[J59] S. Wang, J. Duan, D. Shi, C. Xu, H. Li, R. Diao and Z. Wang, “A Data-driven Multi-agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning,” IEEE Transactions on Power Systems, 2020.
[J58] X. Wang, Y. Wang, D. Shi, J. Wang and Z. Wang, “Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach,” IEEE Transactions on Smart Grid, 2020.
[J57] X. Wang, Y. Wang, J. Wang and D. Shi, “Residential Customer Baseline Load Estimation Using Stacked Autoencoder With Pseudo-Load Selection,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 1, pp. 61-70, Jan. 2020.
[J56] Y. Lin, Y. Wang, J. Wang, S. Wang and D. Shi, “Global Sensitivity Analysis in Load Modeling via Low-rank Tensor,” IEEE Transactions on Smart Grid, 2020.
[J55] J. Duan, D. Shi, R. Diao, H. Li, Z. Wang, B. Zhang, D. Bian and Z. Yi, “Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 814-817, Jan. 2020.
[J54] Z. Ma, Z. Wang, Y. Wang, R. Diao and D. Shi, “Mathematical representation of the WECC composite load model,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 5, pp. 1015-1023, Sept. 2020.
[J53] J. Xie, Z. Ma, K. Dehghanpour, Z. Wang, Y. Wang, R. Diao and D. Shi, “Imitation and Transfer Q-learning-Based Parameter Identification for Composite Load Modeling”, IEEE Transactions on Smart Grid, 2020.
[J52] Y. Fu, L. Chen, Z. Yu, Y. Wang and D. Shi, “Data-Driven Low Frequency Oscillation Mode Identification and Preventive Control Strategy Based on Gradient Descent,” Electric Power System Research, 2020.
[J51] X. Deng, D. Bian, W. Wang, Z. Jiang, W. Yao, W. Qiu, N. Tong, D. Shi and Y. Liu, “A Novel Deep Learning Model to Detect Various Synchrophasor Data Anomalies,” IET Generation, Transmission & Distribution, 2020.
[J50] M. Khan, H. Sun, Y. Xiang and D. Shi, “Electric Vehicles Participation in Load Frequency Control based on mixed H2/H∞,” International Journal of Electrical Power & Energy Systems, 2020.
[J49] C. Fu, C. Wang, L. Y. Wang, and D. Shi, “An Alternative Method for Mitigating Impacts of Communication Delay on Load Frequency Control,” International Journal of Electrical Power & Energy Systems, 2020.
[J48] L. Zhang, J. Qin, D. Shi, and Z. Wang, “Improved Equivalent Circuit Model of MMC and Influence Analysis of Simulation Time Step,” IET Power Electronics, 2020.
[J47] J. Duan, D. Shi, R. Diao, Z. Wang, D. Bian, Z. Yi, “Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations,” IEEE Trans. Power Systems, 2019.
[J46] X. Wang, Y. Wang, J. Wang, and D. Shi, “Residential Customer Baseline Load Estimation Using Stacked Autoencoder with Pseudo-load Selection,” IEEE Journal on Selected Areas in Communications (J-SAC) Issue on Communications and Data Analytics in Smart Grid, 2019.
[J45] X. Deng, D. Bian, D. Shi, W. Yao, L. Wu, and Y. Liu, “Impact of Low Data Quality on Disturbance Triangulation Application Using High-density PMU Measurements,” IEEE Access, 2019.
[J44] C. Liu, F. Hu, D. Shi, X. Zhang, K. Sun, and Z. Wang, “Measurement-based Voltage Stability Assessment Considering Generator VAR Limits,” IEEE Trans. Smart Grid, 2019.
[J43] M. Cui, J. Wang, Y. Wang, R. Diao, and D. Shi, “Robust Time-Varying Synthesis Load Modeling in Distribution Networks Considering Anomalies,” IEEE Trans. Power Systems, 2019.
[J42] Z. Yi, X. Zhao, D. Shi, J. Duan, Y. Xiang, and Z. Wang, “Accurate Power Sharing and Synthetic Inertia Control for DC Building Microgrids with Guaranteed Performance,” IEEE Access, 2019.
[J41] H. Li, Q. Chen, C. Fu, Z. Yu, D. Shi, and Z. Wang, “Bayesian Estimation on Load Model Coefficients of ZIP and Induction Motor Model,” Energies, vol. 12, no. 3, pp. 547-562, 2019.
[J40] D. Bian, M. Kuzlu, M. Pipattanasomporn, S. Rahman, and D. Shi, “Performance Evaluation of Communication Technologies and Network Structure for Smart Grid Applications,” IET Communications, 2019.
[J39] J. Duan, Z. Yi, D. Shi, Z. Wang, “Reinforcement-Learning-Based Optimal Control for Hybrid Energy Storage System”, IEEE Transactions on Industrial Informatics, 2019.
[J38] K. Lai, Y. Wang, D. Shi, M. S. Illindala, Y. Jin, and Z. Wang, “Sizing Battery Storage for Islanded Microgrid Systems to Enhance the Robustness against Attacks on Energy Sources,” Journal of Modern Power Systems and Clean Energy, 2019. [Best Paper]
[J37] X. Wang, D. Shi, J. Wang, and Z. Wang, “Online Identification and Data Recover for PMU Data Manipulation Attack,” IEEE Trans. Smart Grid, 2019.
[J36] D. Bian, Z. Yu, D. Shi, R. Diao, Z. Wang, “A Real-time Robust Low-Frequency Oscillation Detection and Analysis (LFODA) System with Innovative Ensemble Filtering,” CSEE Journal of Power and Energy Systems, 2019.
[J35] H. Li, R. Diao, X. Zhang, X. Lin, X. Lu, D. Shi, Z. Wang, and L. Wang, “An Integrated Online Dynamic Security Assessment System for Improved Situational Awareness and Economic Operation,” IEEE Access, 2019.
[J34] Z. Liang, D. Bian, X. Zhang, D. Shi, R. Diao, and Z. Wang, “Optimal Energy Management for Commercial Buildings Considering Comprehensive Comfort Levels in a Retail Electricity Market,” Applied Energy, 2018.
[J33] L. Yu, D. Shi, G. Xu, X. Guo, Z. Jiang, C. Jing, “Consensus Control of Distributed Energy Resources in a Multi-bus Microgrid for Reactive Power Sharing and Voltage Control,” Energies, 2018.
[J32] D. Bian, D. Shi, M. Pipattanasomporn, M. Kuzlu and S. Rahman, “Mitigating the Impact of Renewable Variability with Demand-Side Resources Considering Communication and Cyber Security Limitations,” IEEE Access, 2018.
[J31] L Yu, D. Shi, X. Guo, Z. Jiang, G. Xu, G. Jian, J. Lei, C. Jing, “An Efficient Substation Placement and Sizing Strategy Based on GIS Using Semi-supervised Learning,” CSEE Journal of Power and Energy Systems, 2018.
[J30] Y. Yan, D. Shi, D. Bian, B. Huang, Z. Yi, Z. Wang, “Small-signal Stability Analysis and Performance Evaluation of Microgrids under Distributed Control,” IEEE Trans. Smart Grid, 2018.
[J29] X. Zhang, D. Shi, Z. Wang, X. Wang, K. Tomsovic, Y. Jin, “Optimal Allocation of Series FACTS Devices Under High Penetration of Wind Power Within a Market Environment,” IEEE Transactions on Power Systems, 2018.
[J28] R. Yao, K. Sun, D. Shi, and X. Zhang, “Voltage Stability Analysis of Power Systems with Induction Motors Based on Holomorphic Embedding,” IEEE Transactions on Power Systems, 2018.
[J27] K. Lai, Y. Wang, D. Shi, M. Illindala, X. Zhang, Z. Wang, “A Resilient Power System Operation Strategy Considering Transmission Line Attacks,” IEEE Access, 2018.
[J26] L. Mang, D. Shi, Z. Yu, W. Zhu, Z. Wang, Y. Xiang, “An Alternating Direction Method of Multipliers Based Approach for Phasor Measurement Unit Data Recovery,” IEEE Trans. Smart Grid, 2018.
[J25] H. Banna, Z. Yu, D. Shi, Z. Wang, D. Su, C. Xu, S. Solanki, J. Solanki, “Online Coherence Identification Using Dynamic Time Warping for Controlled Islanding,” Journal of Modern Power Systems and Clean Energy, 2018.
[J24] T. Zhang, X. Chen, Z. Yu, X. Zhu, and D. Shi, “A Monte-Carlo Simulation Approach to Evaluate Service Capacities of EV Charging and Battery Swapping Stations,” IEEE Trans. Industrial Informatics, 2018.
[J23] Z. Yu, D. Shi, Z. Wang, Q. Zhang, J. Huang, and S. Pan, “Distributed Estimation of Oscillations in Power Systems: an Extended Kalman Filtering Approach,” CSEE Journal of Power and Energy Systems, 2018.
[J22] H. Yuan, F. Li, H. Cui, D. Shi, and Z. Wang, “A Measurement-based VSI for Voltage Dependent Loads Using Angle Difference between Tangent Lines of Load and PV Curves,” Electric Power Systems Research, 2018.
[J21] Y. Zhu, C. Liu, K. Sun, D. Shi, and Z. Wang, “Optimization of Battery Energy Storage to Improve Power System Oscillation Damping,” IEEE Transactions on Sustainable Energy, 2018.
[J20] Z. Jiang, D. Shi, X. Guo, G. Xu, L. Yu, and C. Jing, “Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping,” Energies, 2018.
[J19] C. Liu, B. Wang, X. Xu, K. Sun, D. Shi, and C. L. Bak, “A Multi-Dimensional Holomorphic Embedding Method for Analytical Power Flow Solutions,” IEEE Access, 2017.
[J18] L. Zhang, J. Yu, J. Qin, D. Shi, and Z. Wang, “Modeling, Control, and Protection of Multi-terminal Modular Multilevel Converters-based HVDC Systems,” CSEE Journal of Power and Energy Systems, Invited Paper, 2017.
[J17] X. Zhang, C. Xu, D. Shi, Z. Wang, Q. Zhang, G. Liu, K. Tomsovic, A. Dimitrovski, “The Allocation of Variable Series Reactor Considering AC Constraints and Contingencies,” CSEE Journal of Power and Energy Systems, 2017.
[J16] F. Hu, K. Sun, D. Shi, and Z. Wang, “Measurement-based Voltage Stability Assessment for Load Areas Addressing n-1 Contingencies,” IET Generation, Transmission & Distribution, 2017.
[J15] X. Wang, D. Shi, Z. Wang, C. Xu, Q. Zhang, X. Zhang, and Z. Yu, “Online Calibration of Phasor Measurement Unit Using Density-Based Spatial Clustering,” IEEE Transactions on Power Delivery, 2017.
[J14] B. Kan, W. Zhu, G. Liu, X. Chen, and D. Shi. “Topology Modeling and Analysis in Power Grid Network based on Graph Database,” International Journal of Computational Intelligence Systems, 2017.
[J13] Y. Li, D. Shi, X. Guo, et al., “An Efficient GIS-based Substation Placement and Sizing Strategy Using Semi-Supervised Learning,” CSEE Journal of Power and Energy Systems, 2017.
[J12] D. Bian, M. Kuzlu, M. Pipattanasomporn, S. Rahman, D. Shi, “Performance Evaluation of Communication Network Supporting Various Smart Grid Applications,” IET Communications, 2018.
[J11] D. Shi, X. Chen, Z. Wang, J. Chai, X. Zhang, and X. Wang, “A Distributed Cooperative Control Framework for Synchronized Reconnection of a Multi-Bus Microgrid,” IEEE Trans. Smart Grid, 2017.
[J10] F. Hu, K. Sun, D. Shi, and Z. Wang, “Measurement-based Voltage Stability Assessment for Load Areas Addressing n-1 Contingencies,” IET Generation, Transmission & Distribution, 2017.
[J9] X. Zhang, C. Xu, D. Shi, Z. Wang, Q. Zhang, G. Liu, K. Tomsovic, A. Dimitrovski, “The Allocation of Variable Series Reactor Considering AC Constraints and Contingencies,” CSEE Journal of Power and Energy Systems, 2017.
[J8] Z. Yu, D. Shi, Z. Wang, Q. Zhang, J. Huang, and S. Pan, “Distributed Estimation of Oscillations in Power Systems: an Extended Kalman Filtering Approach,” CSEE Journal of Power and Energy Systems, 2017.
[J7] X. Wang, D. Shi, Z. Wang, et al., “Online Calibration of Phasor Measurement Unit Using Density-based Spatial Clustering,” IEEE Trans. Power Delivery, 2017.
[J6] G. Liu, D. Shi, W. Zhu, X. Chen, and J. Chen, “Clouds and Fog Computation Based Optimal Control and Software-defined Application Techniques,” Power Information and Communication Technology, vol. 14, no. 3, 2016.
[J5] H. Zhou, X. Zhao, D. Shi, H. Zhao, and C. Jing, “Transmission Line Sequence Impedances Identification Using PMU Measurements,” Journal of Energy and Power Engineering, no. 9, pp. 214-221, 2015.
[J4] D. Shawhan, J. Taber, D. Shi, R. Zimmerman, et al., “Does a Detailed Model of the Electricity Grid Matter? Estimating the Impacts of the Regional Greenhouse Gas Initiative,” Resource and Energy Economics, vol. 36, no. 1, pp. 191-207, Jan. 2014.
[J3] D. Shi, and D. J. Tylavsky, “A Novel Bus-Aggregation-Based Structure-Preserving Network Equivalent Method,” IEEE Trans. Power Syst., Oct. 2014.
[J2] D. Shi, D. J. Tylavsky and N. Logic, “An Adaptive Method for Detection and Correction of Errors in PMU Measurements,” IEEE Trans. on Smart Grid, vol. 3, no. 4, Dec. 2012.
[J1] D. Shi, D. J. Tylavsky, K. M. Koellner, N. Logic and D. E. Wheeler, “Transmission Line Parameter Identification using PMU Measurements,” Euro. Trans. Electr. Power, vol. 21, no. 4, pp. 1574-1588, Nov. 2010.
[Conference Papers]
[C89] J. Ansu, F. Wang, and D. Shi, “Analysis of Unit Commitment Considering Demand Response and Synthetic Inertia in Electricity Markets,” The 56th North American Power Symposium (NAPS), 2024.
[C88] D. Shi, Q. Zhang, M. Hong, F. Wang, S. Maslennikov, X. Luo, and Y. Chen, “Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights,” 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT - Europe), 2024.
[C87] L. Zhou, Y. Zhou, Z. Yi, D. Shi, and Z. Huang, “Optimizing Grid Services: A Deep Deterministic Policy Gradient Approach for Demand-Side Resource Aggregation,” 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT - Europe), 2024.
[C86] Y. Zhang, J. Zhao, D. Shi, and S. Chung, “Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-source Multi-rate Data,” IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2024.
[C85] M. Arif, F. Wang, D. Shi, L. Sun, and Z. Wang, “Analysis of the Impacts of Reserve Requirements on Marginal Emission Rate,” IEEE PES Innovative Smart Grid Technologies (ISGT) North America, 2024.
[C84] H. Davoudi, F. Wang, A. Xavier, F. Qiu, and D. Shi, “Market Pricing and Settlements Analysis Considering Capacity Sharing and Reserve Substitutions of Operating Reserve Products,” North American Power Symposium, 2023.
[C83] X. Wang, D. Shi, G. Xu, and F. Wang, “Edge-Computing Based Dynamic Anomaly Detection for Transmission Lines,” 2023 IEEE PES Innovative Smart Grid Technologies Conference, Jan. 2023
[C82] H. Davoudi, F. Wang, A. Xavier, F. Qiu, and D. Shi, “Market Pricing and Settlements Analysis Considering Capacity Sharing and Reserve Substitutions of Operating Reserve Products,” North American Power Symposium (NAPS), 2023.
[C81] Y. Zhou, L. Zhou, D. Shi, X. Zhao, “Coordinated Frequency Control through Safe Reinforcement Learning,” IEEE PES General Meeting, 2022.
[C80] H. Li, Y. Xiang, T. Jin, S. Wang, Z. Yi, and D. Shi, “A DRL-Based Approach for System Frequency Response Model Calibration,” 52nd North American Power Symposium (NAPS), 2021.
[C79] Z. Liang, D. Bian, C. Xu, X. Zhang, D. Shi, and Z. Wang, “Deep Reinforcement Learning based Energy Management Strategy for Commercial Building Considering Comprehensive Comfort Levels,” 52nd North American Power Symposium (NAPS), 2021.
[C78] R. Diao, D. Shi, B. Zhang, S. Wang, H. Li, C. Xu, et al., “On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control,” NeurIPS 2020.
[C77] X. Zhou, S. Wang, R. Diao, D. Bian, J. Duan, D. Shi, et al., “Rethink AI-based Power Grid Control: Diving Into Algorithm Design,” NeurIPS 2020.
[C76] Z. Wang, X. Lu, R. Diao, H. Li, C. Xu, J. Duan, N. Zhang, and D. Shi, “Deep-Reinforcement-Learning-Based Autonomous Control and Decision Making for Power Systems,” Electric Power Engineering Technology, vol. 39, no. 6, Nov. 2020. (In Chinese)
[C75] X. Wang, Y. Wang, D. Shi, J. Wang, S.Wang, R. Diao and Z. Wang, “Evaluating Load Models and Their Impacts on Power Transfer Limits,” IEEE ICCA, Oct. 2020.
[C74] Z. Liang, D. Bian, C. Xu, X. Zhang, D. Shi, and Z. Wang, “Deep Reinforcement Learning based Energy Management Strategy for Commercial Buildings Considering Comprehensive Comfort Levels,” North American Power Symposium, Tempe, AZ, USA, 2020.
[C73] T. Xia, Z. Yu, K. Sun, D. Shi and Z. Wang, “Extended Prony Analysis on Power System Oscillation Under a Near-Resonance Condition,” IEEE PES General Meeting, Montreal, Canada, 2020. [Best Paper]
[C72] Z. Yu, D. Shi, J. Li, Y. Wang, X. Zhao, Z. Wang and J. Li, “Using Transfer Learning to Distinguish between Natural and Forced Oscillations,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C71] Z. Xu, Y. Zan, C. Xu, J. Li, D. Shi, Z. Wang, B. Zhang and J. Duan, “Accelerated DRL Agent for Autonomous Voltage Control Using Asynchronous Advantage Actor-critic,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C70] Y. Lin, X. Zhang, S. Yin, J. Wang and D. Shi, “Real-Time Economic Dispatch for Integrated Energy Microgrid Considering Ancillary Services,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C69] G. Tian, Y. Gu, X. Lu, D. Shi, Q. Zhou, Z. Wang and J. Li, “Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C68] B. Zhang, X. Lu, R. Diao, H. Li, T. Lan, D. Shi and Z. Wang, “Real-time Autonomous Line Flow Control Using Proximal Policy Optimization,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C67] S. Wang, R. Diao, T. Lan, Z. Wang, D. Shi, H. Li and X. Lu, “A DRL-Aided Multi-Layer Stability Model Calibration Platform Considering Multiple Events,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C66] T. Lan, J. Duan, B. Zhang, D. Shi, Z. Wang, R. Diao and X. Zhang, “AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs,” IEEE PES General Meeting, Montreal, Canada, 2020.
[C65] MD Kamruzzaman, X. Zhang, M. Abdelmalak, M. Benidris and D. Shi, “A Method to Evaluate the Maximum Hosting Capacity of Power Systems to Electric Vehicles,” IEEE PMAPS, 2020.
[C64] H. Li, Y. Xiang, T. Jin, S. Wang, Z. Yi and D. Shi, “A DRL-Based Approach for System Frequency Response Model Calibration,” North American Power Symposium, Tempe, AZ, USA, 2020.
[C63] J. Duan, H. Li, X. Zhang, R. Diao, B. Zhang, D. Shi, X. Lu, Z. Wang, and S. Wang, “A Deep Reinforcement Learning Based Approach for Optimal Active Power Dispatch,” IEEE Sustainable Power and Energy Conference, Beijing, 2019.
[C62] X. Zhang, D. Bian, D. Shi, Z. Wang, and G. Liu., “Community Microgrid Planning Considering Building Thermal Dynamics”, IEEE Sustainable Power and Energy Conference, 2019. [Best Paper]
[C61] S. Wang, X. Zhao, F. Xue, W. Li, H. Peng, D. Shi, S. Wang, and Z. Wang, “Electromechanical Transient Modeling of Modular Multilevel Converter based HVDC Network,” IEEE Sustainable Power and Energy Conference, Beijing, 2019. [Best Paper]
[C60] X. Gao, G. Li, X. Zhang, D. Shi, X. Wang, and Z. Bie, “An Iteration Method for Optimal Energy Flow of Combined Heating and Electricity System,” IEEE International Conference on Energy Internet, 2019.
[C59] Q. Qiu, G. Li, X. Zhang, D. Shi, T. Gao, and Z. Bie, “Analysis of Heating and Electrical Loads Based on Auto-encoder for Integrated Park,” IEEE International Conference on Advanced Power System Automation and Protection, Xi’an, China, 2019.
[C58] X. Deng, D. Bian, D. Shi, W. Yao, Z. Jiang, and Y. Liu, “Line Outage Detection and Localization via Synchrophasor Measurement,” IEEE PES Innovative Smart Grid Technologies (ISGT) Asia, Chengdu, China, 2019.
[C57] Q. Chang, Y. Wang, X. Lu, D. Shi, H. Li, J. Duan, and Z. Wang, “Probabilistic Load Forecasting via Point Forecast Feature Integration,” IEEE PES Innovative Smart Grid Technologies (ISGT) Asia, Chengdu, China, 2019.
[C56] Y. Peng, Y. Wang, X. Lu, H. Li, D. Shi, Z. Wang, and J. Li, “Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis,” IEEE PES Innovative Smart Grid Technologies (ISGT) Asia, Chengdu, China, 2019.
[C55] K. Li, L. Chen, Z. Yu, Y. Wang, D. Shi, “Improvement of Electromechanical Mode Identification from Ambient Data with Stochastic Subspace Method,” IEEE PES APPEEC, 2019. [Best Paper]
[C54] Z. Nie, X. Zhang, X. Zhao, Y. Xu, D. Shi, J. Duan, Z. Wang , “Adaptive Online Learning with Momentum for Contingency-based Voltage Stability Assessment,” IEEE PES General Meeting, 2019.
[C53] R. Diao, Z. Wang, D. Shi, Q. Chang, J. Duan, X. Zhang, “Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning,” IEEE PES General Meeting, 2019. [Best Paper]
[C52] Y. Wang, X. Lu, Y. Xu, D. Shi, Z. Yi, J. Duan, Z. Wang, “Submodular Load Clustering with Robust Principal Component Analysis,” IEEE PES General Meeting, 2019.
[C51] X. Wang, Y. Wang, D. Shi, J. Wang, “A Deep Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving,” IEEE PES General Meeting, 2019.
[C50] Y. Xiang, X. Lu, Z. Yu, D. Shi, H. Li, Z. Wang, “IoT and Edge Computing Based Direct Load Control for Fast Adaptive Frequency Regulation”, 2019 IEEE PES General Meeting, 2019.
[C49] Chang Fu, Zhe Yu, D. Shi, Haifeng Li, Caisheng Wang, and Zhiwei Wang, “Bayesian Estimation Based Parameter Estimation for Composite Load,” IEEE PES General Meeting, 2019.
[C48] Z. Liang, D. Bian, D. Su, R. Diao, D. Shi, Z. Wang, W. Su, “Adaptive Robust Energy Management Strategy for Campus-Based Commercial Buildings Considering Comprehensive Comfort Levels,” 2019 IEEE PES General Meeting, 2019.
[C47] X. Deng, D. Shi, D. Bian, W. Yao, L. Wu, Y. Liu, “Impact of Low Data Quality on Disturbance Triangulation Application using High-Density PMU Measurements,” IEEE PES ISGT Asia, 2019.
[C46] Y. Peng, Y. Wang, X. Lu, H. Li, D. Shi, Z. Wang, J. Li, “Short-term Load Forecasting at Different Aggregation Levels,” IEEE PES ISGT Asia, 2019.
[C45] J. Duan, Z. Yi, D. Shi, H. Xu, and Z. Wang, “A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties,” 2019 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 2019.
[C44] Y. Cui, Z. Yi, J. Duan, D. Shi, and Z. Wang, “A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation,” 2019 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 2019.
[C43] Z. Yu, D. Shi, H. Li, Y. Wang, Z. Yi, Z. Wang, “An Extended Kalman Filter Enhanced Hilbert-Huang Transform in Oscillation Detection,” ISGT Europe 2018, Sarajevo, Bosnia and Herzegovina, Oct. 2018.
[C42] A. Babqi, Z. Yi, D. Shi, and X. Zhao, “Model Predictive Control of H5 Inverter for Transformerless PV Systems with Maximum Power Point Tracking and Leakage Current Reduction”, 44th Annual Conference of the IEEE Industrial Electronics Society (IECON 2018), 2018.
[C41] Y. Meng, Z. Yu, D. Shi, et al., “Forced Oscillation Source Location via Multivariate Time Series Classification,” IEEE PES Transmission and Distribution (T&D) Conference and Exposition, 2018.
[C40] Y. Wang, Z. Yi, D. Shi, Z. Yu, D. Bian, and Z. Wang, “Optimal Distributed Energy Resources Sizing for Commercial Building Hybrid Microgrids,” 2018 IEEE PES General Meeting, 2018.
[C39] X. Wang, J. Wang, D. Shi, M. Khodayar, “A Factorial Hidden Markov Model for Energy Disaggregation Based on Human Behavior Analysis,” 2018 IEEE PES General Meeting, 2018.
[C38] Z. Yi, A. Babqi, Y. Wang, D. Shi, A. H. Etemadi, Z. Wang, and Bibin Huang, “Finite-Control-Set Model Predictive Control for Hybrid Microgrids,” 2018 IEEE PES General Meeting, 2018.
[C37] Y. Xiang, X. Zhang, D. Shi, Y. Jin, Z. Wang and L. Wang, “A Robust Power Grid Defense Model Considering Load Demand and Wind Generation Uncertainties,” 2018 IEEE PES General Meeting, 2018.
[C36] X. Zhang, D. Shi, X. Lu, Z. Yi, Q. Zhang, and Z. Wang, “Sensitivity Based Thevenin Index for Voltage Stability Assessment Considering N-1 Contingency,” 2018 IEEE PES General Meeting, 2018.
[C35] S. Rao, D. Tylavsky, W. Yi, V. Vittal, D. Shi, and Z. Wang, “Fast Weak-bus and Bifurcation Point Determination Using Holomorphic Embedding Method,” 2018 IEEE PES General Meeting, 2018.
[C34] M. Liao, D. Shi, Z. Yu, W. Zhu, Z. Wang, Y. Xiang, “Recover the lost Phasor Measurement Unit Data using Alternating Direction Multipliers Method,” 2018 IEEE PES Transmission & Distribution Conference and Exposition, Denver, CO, 2018.
[C33] Y. Meng, Z. Yu, D. Shi, D. Bian, and Z. Wang, “Forced Oscillation Source Location via Multivariate Time Series Classification,” 2018 IEEE PES Transmission & Distribution Conference and Exposition, Denver, CO, 2018.
[C32] X. Zhang, D. Shi, Z. Wang, Z. Yu, X. Wang, D. Bian, and K. Tomsovic, “Bilevel Optimization Based Transmission Expansion Planning Considering Phase Shifting Transformer,” 2017 North American Power Symposium, Morgantown, WV, 2017.
[C31] G. Liu, K. Liu, D. Shi, W. Zhu, Z. Wang, and X. Chen, “Graph Computation and Its Applications in Smart Grid,” 2017 International Congress on Big Data, Honolulu, HI, 2017.
[C30] X. Lu, D. Shi, B. Zhu, et al., “PMU Assisted Power System Parameter Calibration at Jiangsu Electric Power Company,” 2017 IEEE PES General Meeting, Chicago, 2017. [Best Paper]
[C29] L. Yu, D. Shi, X. Guo, et al., “GIS-based Optimal Siting and Sizing of Substation Using Semi-Supervised Learning,” IEEE Green Energy and Smart Systems Conference, Long Beach, CA, 2017.
[C28] L. Zhao, D. Shi, C. Wang, et al., “A Distributed Control Framework for Microgrid with Renewables and Energy Storage Systems,” IEEE Green Energy and Smart Systems Conference, Long Beach, CA, 2017.
[C27] C. Wang, L. Zhao, D. Shi, C. Hong, et al., “Modeling and Control of Energy Storage System in a Microgrid using Electromagnetic Simulation Program (ESP),” IEEE Green Energy and Systems Conference, 2016.
[C26] X. Zhang, D. Shi, Z. Wang, et al., “Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming,” 2017 IEEE PES General Meeting, 2017.
[C25] D. Shi, J. Luo, W. Zhu, X. Chen, and G. Xu, “Distributed Control for Microgrid with Renewables and Energy Storage Systems,” 2016 International Conference on Global Energy Interconnection, Beijing, 2016.
[C24] G. Liu, K. Liu, D. Shi, W. Zhu, X. Chen, X. Chang, and Y. Xu, “Graph Computation and its Applications in Global Energy Interconnection,” 2016 International Conference on Global Energy Interconnection, Beijing, 2016.
[C23] F. Gao, G. Liu, C. Saunders, D. Shi and W. Zhu, “Energy Disaggregation Based on Mixed Integer Programming and Collocation Method,” IEEE PES General Meeting, 2016.
[C22] B. Ansari, D. Shi, R. Sharma, and M. G. Simoes, “Economic Analysis, Optimal Sizing and Management of Energy Storage for PV Grid Integration,” IEEE PES T&D Conference & Exposition, 2016.
[C21] B. Asghari, R. Patil, D. Shi, and R. Sharma, “A Mixed-mode Energy Management System for Grid Scale Energy Storage Units,” IEEE International Conference on Smart Grid Communications, Miami, FL, 2015.
[C20] Y. Zhu, R. Azim, H. A. Saleem, K. Sun, D. Shi, and R. Sharma, “Microgrid Security Assessment and Islanding Control by Support Vector Machine,” IEEE PES General Meeting, Denver, 2015.
[C19] R. Azim, Y. Zhu, H. A. Saleem, K. Sun, F. Li, D. Shi and R. Sharma, “A Decision Tree Based Approach for Microgrid Islanding Detection,” IEEE Innovative Smart Grid Technologies, Washington, D.C., 2015.
[C18] H. Zhou, X. Zhao, D. Shi, H. Zhao, C. Jing, “Calculating Sequence Impedances of Transmission Line Using PMU Measurements,” IEEE PES General Meeting, Denver, 2015.
[C17] R. Azim, K. Sun, F. Li, Y. Zhu, H. Saleem, D. Shi, and R. Sharma, “A Comparative Analysis of Intelligent Classifiers for Passive Islanding Detection in Microgrids,” Powertech Eindhoven 2015, June 2015.
[C16] D. Shawhan, J. Taber, R. Zimmerman, J. Yan, C. Marquet, W. Schulze, R. Schuler, R. Thomas, D. Tylavsky, D. Shi, N. Li, W. Jewell, T. Hardy, and Z. Hu, “A Detailed Power System Planning Model: Estimating the Long-Run Impact of Carbon-Reducing Policies,” 48th Hawaii International Conference on System Sciences (HICSS), 2015.
[C15] J. Wang, Y. Su, J. Li, C. Jing, D. Shi, and J. Liu, “Online Probabilistic Security Risk Assessment Implementation at China Southern Power Grid Towards Smart Control Center,” IEEE GreenTech, New Orleans, 2015.
[C14] D. Shi, Y. Luo and R. Sharma, “Active Synchronization Control for Microgrid Reconnection after Islanding,” IEEE PES Innovative Smart Grid Technologies (ISGT) Europe, 2014.
[C13] D. He, D. Shi and R. Sharma, “Consensus-based Distributed Cooperative Control for Microgrid Voltage Regulation and Reactive Power Sharing,” IEEE PES Innovative Smart Grid Technologies Europe, 2014.
[C12] J. Zhao, D. Shi and R. Sharma, “Microgrid Reactive Power Management During and Subsequent to Islanding Process,” IEEE PES Transmission and Distribution (T&D) Conf. and Expo., Chicago, Apr. 14-17, 2014.
[C11] R. Patil, A. Keeli, D. Shi and R. Sharma, “Zone Based Control of Building A/C Systems through Temperature Monitoring and Control,” ASME 2014 Power Conference, Baltimore, Maryland, 2014.
[C10] J. Wang, Y. Su, J. Li, C. Jing, and D. Shi, “Real Time Probabilistic Security Risk Assessment at an EMS,” IEEE PES Transmission and Distribution (T&D) Conference and Exposition, Apr. 14-17, 2014.
[C9] D. Shi, R. Sharma, and Y. Ye, “Adaptive Control of Distributed Generation for Microgrid Islanding,” IEEE Innovative Smart Grid Technologies (ISGT) Europe, Lyngby, Denmark, 2013.
[C8] D. Shi, and R. Sharma, “Adaptive Control of Energy Storage for Voltage Regulation in Distribution System,” IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, Canada, 2013.
[C7] D. Shi, D. Tylavsky, and N. Logic, “An Adaptive Method for Detection and Correction of Errors in PMU Measurements,” IEEE PES General Meeting, Vancouver, Canada, 2013.
[C6] Y. Ye, R. Sharma, and D. Shi, “Adaptive Control of Hybrid Ultracapacitor-battery Storage System for PV Output Smoothing,” Proceedings of the ASME 2013 Power Conference, Boston, Massachusetts, 2013.
[C5] D. Shi, D. L. Shawhan, N. Li, D. J. Tylavsky, J. Taber, and R. D. Zimmerman, “Optimal Generation Investment Planning: Pt. 1: Network Equivalents,” 44th North American Power Symposium, 2012.
[C4] N. Li, D. Shi, D. L. Shawhan, D. J. Tylavsky, J. Taber, and R. D. Zimmerman, “Optimal Generation Investment Planning: Pt. 2: Application to the ERCOT System,” 44th North American Power Symposium, 2012.
[C3] Y. Qi, D. Shi, and D. J. Tylavsky, “Impact of Assumptions on DC Power Flow Model Accuracy,” 44th North American Symposium, 2012.
[C2] D. Shi and D. J. Tylavsky, “An Improved Bus Aggregation Technique for Generating Network Equivalents,” IEEE PES General Meeting, San Diego, USA, 2012.
[C1] D. Shi, D. J. Tylavsky, N. Logic, and K. M. Koellner, “Identification of Short Transmission Line Impedance Parameters using Synchrophasor Measurements,” 40th North American Power Symposium, Nov. 2008.