Research

Project Overview:

This project focuses on the practical challenges of DER aggregations for wholesale market participation under FERC Order 2222. We propose optimal coordination approaches among the wholesale market operation, the distribution system operation, the DER aggregators, and the individual DERs. To ensure real-world applicability, we investigate optimal coordination under limited communications, which involves minimal changes to today's already-established wholesale market clearing rules and avoids confidential grid model exchanges between the transmission and distribution systems.

Selected Publications:

• M. Wu, Toward Future Electricity Markets with Massive DER Penetration and Optimal Transmission-Distribution Coordination, Invited talk at 2022 NREL 5th Workshop on Autonomous Energy Systems.

• M. Mousavi, and M. Wu, Transmission and Distribution Coordination for DER-rich Energy Markets: A Parametric Programming Approach, Preprint.

• M. Mousavi, and M. Wu, A DSO Framework for Market Participation of DER Aggregators in Unbalanced Distribution Networks, IEEE Transactions on Power Systems.

Project Overview:

This project proposes various machine learning models and approaches to forecast the day-ahead and real-time locational marginal prices (LMPs) across multiple locations of the wholesale market. We design data structures to store general heterogeneous spatio-temporal sequence data, and propose spatio-temporal decision transformer and spatio-temporal GAN to forecast general spatio-temporal sequence data. We design two-stage convolutional LSTM to resolve the data publication delays in real-world wholesale markets.

Selected Publications:

• Z. Zhang, and M. Wu, Real-Time Locational Marginal Price Forecast: A Decision Transformer-Based Approach, 2023 IEEE PES General Meeting.

• Z. Zhang, and M. Wu, Energy Price Prediction Considering Generation Bids Variation: A Two-Stage Convolutional Long Short-Term Memory Approach, 2022 IEEE PES General Meeting.

• Z. Zhang, and M. Wu, Predicting Real-Time Locational Marginal Prices: A GAN-Based Approach, IEEE Transactions on Power Systems.

Project Overview:

This project proposes first-principled and machine learning approaches for generating optimal bidding and operation strategies for utility-scale energy storage and general market participants. We propose bi-level optimization models and (inverse) reinforcement learning models to generate the optimal bidding policies of energy storage, and learn the bidding objectives and bidding intentions of real-world market participants from historical bidding and market clearing data.

Selected Publications:

• R. Khalilisenobari, and M. Wu, Optimal Participation of Price-maker Battery Energy Storage Systems in Energy and Ancillary Services Markets considering Degradation Cost, International Journal of Electrical Power & Energy Systems.

Project Overview:

This project proposes first-principle-based modeling and dynamic voltage stability assessment approaches for quantifying the impact of variable frequency drive (VFD) air conditioners and DERs on fault induced delayed voltage recovery (FIDVR).

Selected Publications:

• A. Cisco Sullberg, M. Wu, V. Vittal, B. Gong, and P. Augustin, Examination of Composite Load and Variable Frequency Drive Air Conditioning Modeling on FIDVR, IEEE Open Access Journal of Power and Energy.

Project Overview:

This project proposes various data-driven approaches to detect and identify different anomalies in the real-world PMU data. The detected anomalies include low-quality PMU data, false data injection attacks, and low-frequency system oscillations.

Selected Publications:

• M. Wu, and L. Xie, Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach, IEEE Transactions on Power Systems.

• T. Huang, M. Wu, and L. Xie, Prioritization of PMU Location and Signal Selection for Monitoring Critical Power System Oscillations, IEEE Transactions on Power Systems.

• M. Wu, and L. Xie, Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach, 50th Hawaii International Conference on System Sciences (HICSS).

Project Overview:

This project proposes first-principled approaches to initialize the full-order EMTP model of the DFIG-based wind turbine and to study the impact of wind farm spatial distribution on sub-synchronous oscillations.

Selected Publications:

• M. Wu, and L. Xie, Calculating Steady-State Operating Conditions for DFIG-Based Wind Turbines, IEEE Transactions on Sustainable Energy.

• M. Wu, L. Xie, L. Cheng, and R. Sun, A Study on The Impact of Wind Farm Spatial Distribution on Power System Sub-Synchronous Oscillations, IEEE Transactions on Power Systems.

Project Overview:

This project develops a fully automated power plant model verification (APPMV) tool which is currently running 24 by 7 as an online automated service at ISO New England. The APPMV tool automatically 1) retrieves eventful PMU data from PhasorPoint and SCADA data from PI database; 2) runs model validation for all online generators monitored by PMUs; 3) generates comparison figures between simulation and PMU data; 4) generates key performance indices (KPIs) to flag abnormal generator models; and 5) sends out validation results as emails.

Selected Publications:

• X. Luo, Success Story: Practical Use of Synchrophasor Technology in ISO-NE Operations, Talk at NASPI Work Group Meeting given by ISO New England.

• F. Q. Zhang, APPMV at ISO-NE, Talk at NERC Synchronized Measurement Subcommittee (SMS) Meeting given by ISO New England.

• M. Wu, Automated Power Plant Model Verification (APPMV) at ISO New England, Talk at NASPI Work Group Meeting.

• M. Wu, W. Huang, F. Q. Zhang, X. Luo, S. Maslenniko20v, and E. Litvinov, Power plant model verification at ISO New England, 2017 IEEE PES General Meeting.