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Energy Systems Division
Renewables and Grid Modernization
Renewables and Grid Modernization research addresses the key challenges facing modernization of the electric power grid and the integration of renewable energy resources.
Capabilities
- Power system modeling, simulation, stability analysis and control synthesis
- Forecasting and damage prediction models for grid hardening and restoration
- Application of advanced sensors and monitoring data for improved grid operation
- Electric grid reliability and security assessment
- Probabilistic techniques for enhanced grid operation and planning
- Digital system reliability assessment
- Cybersecurity solutions for grid forecasting and operation
- Optimal grid planning and operation
Research Areas of Interest
- Addressing the challenges of grid modernization in transmission and distribution networks, particularly in northeast
- Developing and evaluating new technologies and control strategies for improved grid efficiency, reliability and resilience
- Addressing the impacts of renewables integration and DER deployment on grid performance and power quality
- Understanding the role of energy storage and how it might facilitate renewable integration into the grid
- Developing new methods for damage forecasting and vulnerability assessments during severe weather events
- Developing innovative technologies for cybersecurity detection and mitigation in energy delivery systems
Research Efforts
- Use of energy storage systems to facilitate grid integration of DER and improve grid frequency response
- Development of probabilistic techniques to optimize sizing, siting, and operation of energy storage systems
- Evaluation of grid impacts from high penetrations of renewable generation
- Using Radar data in real-time response for forecasting grid outages and expediting restoration of electric utility systems
- Development of probabilistic techniques for transmission system planning
- Evaluation of benefits of smart grid technologies deployed in distribution systems
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Meng Yue
Group Leader, Research Staff Electrical Engineer
Energy Systems Division
Interdisciplinary Science Department
(631) 344-7140, yuemeng@bnl.gov
Facilities
Long Island Solar Farm
Northeast Solar Energy Research Center
Center for Grid Innovation
Real-Time Grid Simulator
Sample Research Projects
Advanced Grid Modeling
Support for the DOE Office of Electricity's Advanced Grid Modeling Program
The electric power industry has undergone extensive changes over the past several decades and become substantially more complex, dynamic, and uncertain, as new market rules, regulatory policies, and technologies have been adopted. As the electric delivery system continues to evolve, the availability of more detailed data about system conditions from devices such as phasor measurement units (PMUs) used for wide area visibility and advanced meter infrastructure (AMI) used for dynamic pricing and demand response will help improve the system's reliability and flexibility.
Working with the large volume and variety of data to make it more relevant and actionable to grid operators and utilities, however, poses significant challenges. Continuing to shift operational data analytics from a traditionally off-line environment to further expand real-time situational awareness of grid conditions and measurement-based, faster control will require significant advancements in algorithms and computational approaches.
Details of Brookhaven Lab's Advanced Grid Modeling projects.
Utility Projects
Support for electric utilities
Electric utilities are working to modernize their electrical distribution and transmission systems to make them more reliable and resilient. Brookhaven Lab is using its grid research capabilities to support the needs of local electric utilities in addressing the challenges of grid modernization. The following examples are projects Brookhaven Lab is performing in collaboration with Consolidated Edison of New York.
Grid Cybersecurity
Support for the DOE Office of Cybersecurity Energy Security and Emergency Response (CESER) Cybersecurity for Energy Delivery Systems (CEDS) program
Energy delivery systems are increasingly dependent on sophisticated forecasting data for efficient and secure operations. Weather data, load profiles and forecasting information about renewable generation are vital to scheduling functionalities for both transmission and distribution operations. However, the forecasting data could be vulnerable to manipulation and corruption. A shortfall in operating reserves due to compromised critical short-term forecasting data could seriously impact grid operations. Also at risk is the economic viability of utility operations if errant data triggers uneconomic dispatch. BNL identified a need for an integrated tool and database for cyberattack assessment and mitigation of compromised short-term forecasting data and database for cyberattack assessment and mitigation of compromised short-term forecasting data and is developing a method to detect and mitigate cyber-attacks on this forecasting data. The goal is to develop an online tool that will be made available to both transmission and distribution utilities. The integrated software tool solution will ensure reliable operation of the grid without failure or detrimental impact to operations – the flow of energy will not be impeded.
Details of Brookhaven Lab's work on Grid Cybersecurity.
Publications
2023
- T. Zhao, N. Raghunathan, A. Yogarathnam, M Yue, and P. B. Luh, “A Scalable Planning Framework of Energy Storage Systems under Frequency Dynamics Constraints,” the International Journal of Electrical Power and Energy Systems, Vol. 145, February 2023.
- A. Yogarathnam, L. Karunaratne, N. R. Chaudhuri, and M. Yue, “Modeling Adequacy of Droop-Controlled Grid-Forming Converters for Transient Studies: Singular Perturbation Analysis,” 2023 IEEE PES Innovative Smart Grid Technologies Conference (ISGT2023).
- J. Li, Y. Zhao, and M. Yue, “Integrating Learning and Physics based Computation for Fast Online Transient Analysis,” the 2023 IEEE PES Innovative Smart Grid Technologies Conference (ISGT2023).
2022
- Y. Lin, J. Wang, and M. Yue, “Equity-based grid resilience: how do we get there?” the Electricity Journal, Vol. 35, No. 5, June 2022.
- T. Zhao, M. Yue, and J. Wang, “Structure-informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics,” IEEE Transactions on Power Systems, Vol. 37, No. 6, November 2022.
- J. Jiao, Z. Tang, J. Yan, P. Zhang, and M. Yue, “Cyberattack-Resilient Load Forecasting with Adaptive Robust Regression,” International Journal of Forecasting, https://doi.org/10.1016/j.ijforecast.2021.06.009.
- T. Zhao, B. Beckman, M. Yue, and R. Foster, “ESE: A Tool for Enhanced STIX Elevation,” Resilience Week, September 2022.
- A. Meliopoulos, M. Papic, S. H. Tindemans, S. Ekisheva, M. Yue, and D. M. Logan, “Composite Power System Reliability with Renewables and Customer Flexibility,” accepted by IEEE PMAS 2022.
- L. Karunaratne, A. Yogarathnam, N. R. Chaudhuri, and M. Yue, “Grid of Near Future: Impact on (N-1) Contingency Analysis in Presence of Droop-Controlled Grid-Forming Converter-Interfaced Generations,” one of the best conference papers in IEEE PES General Meeting 2022.
- T. Zhao, Y. Zhang, and M. Yue, “Scalable Deep Reinforcement Learning-based Volt-VAR optimization in Distribution Systems: a Mean-field Approach,” IEEE PES General Meeting 2022.
- M. Yue, A. Yogarathnam, M. Jensen, T. Fairless, and A. Zhou, “Problem Reformulation for Improved Regression Analysis in Granular Damage Forecasting,” IEEE PES T&D Conference and Exposition, 2022.
2021
- Y. Zhang, M. Yue, and J. Wang, “Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning,” IEEE PES General Meeting 2021.
- G. Larangeira, Y. Yogarathnam, M. Yue, and F. Dong, “Exact Quantification of System Reliability Indexes in Probabilistic Contingency Analysis,” IEEE PES General Meeting 2021.
- J. Li, M. Yue, Y. Zhao, and G. Lin, “Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics,” IEEE SmartGridComm, 2020.
2020
- S. Zhang, A. Yogarathinam, J. Zhan, M. Yue, and G. Lin, “A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid,” IEEE PES General Meeting 2020.
2019
- M. Cui, J. Wang, and M. Yue, “Machine Learning Based Anomaly Detection for Load Forecasting under Cyberattacks,” IEEE Transactions on Smart Grid, Vol. 10, No. 5, January 2019.
- M. Yue, T. Hong, and J. Wang, “Data Analytics Based Anomaly Detection in Time Series Data for Online Cybersecure Load Forecasting,” IEEE Transactions on Smart Grid, Vo. 1, No. 1, January 2019.
- Y. Li, P. Zhang, M. Althoff, and M. Yue, “Distributed Formal Analysis for Power Networks with Deep Integration of Distributed Energy Resources”, IEEE Transactions on Power Systems, Vol. 34, No. 6, Nov. 2019. X. Lu, J. Wang, Z. Li, and M. Yue, “Dynamic Stability Assessment for Integrated Transmission-Distribution System Considering Distributed Energy Resources,” 2019 International Conference on Smart Energy Systems and Technologies (SEST).
2018
- Y. Li, P. Zhang, and M. Yue, “Networked Microgrid Stability through Distributed Formal Analysis,” Applied Energy, Vol. 228, Oct. 2018, pp279-288.
- J. Luo, T. Hong, and M. Yue, “Real-time Anomaly Detection for Very Short-Term Load Forecasting,” Journal of Modern Power Systems and Clean Energy, Vol 6, No 2, March 2018.
- M. Yue, T. Toto, M. P. Jensen, S. E. Giangrande, and R. Lofaro, “A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data,” IEEE Transactions on Smart Grid, Vol. 9, No. 6, November 2018.
- M. Yue, S. W. Kang, C. Jin, and J. Matevosjana, “An Investigation of Potential Intermittency Induced Outage Modes for Wind Generation,” Proceedings of PMAPS 2018.
- M. Yue, “Evaluation of A Data Analytic Based Anomaly Detection Method for Load Forecasting Data,” IEEE PES General Meeting, July 2018
2017
- M. Yue, T. Toto, M. P., Jensen, and S. Giangrande, “Outage Prediction Using Radar Measurement for Facilitating Distribution System Restoration,” IEEE PES General Meeting, July 2017
- M. Yue, “An Integrated Anomaly Detection Method for Load Forecasting Data under Cyberattacks,” IEEE PES General Meeting, July 2017.
2016
- M. Yue and X. Wang, “'An Enhanced Probabilistic Contingency Analysis for Development of Risk-Informed Transmission Planning,” IEEE PES General Meeting, July 2016.
2015
- M. Yue and X. Wang, “Grid Inertial Response-Based Probabilistic Determination of Energy Storage System Capacity under High Solar Penetration,” IEEE Transactions on Sustainable Energy, Vol. 6, No. 3, July 2015.
- M. Yue and X. Wang, “Assessing Cloud Transient Impacts of Solar and Battery Energy Systems on Grid Inertial Responses,” Journal of Electric Power Components and Systems, 43(2):200-211, January 2015.
- M. Yue and X. Wang, “Outage Data Collection and Parameter Estimation for an Improved Probabilistic Contingency Analysis,” IEEE PES General Meeting, July 2015.
- M. Yue and X. Wang, “Grid Inertial Response-Based Probabilistic Determination of Energy Storage System Capacity under High Solar Penetration,” IEEE PES General Meeting, July 2015.
- X. Wang and M. Yue, “Capacity Specification for Hybrid Energy Storage System to Accommodate Fast PV Fluctuations,” IEEE PES General Meeting, July 2015.
2014
- X. Wang, M. Yue, and E. Muljadi, “Probabilistic Approach for Power Capacity Specification of Wind Energy Storage Systems,” IEEE Transactions on Industry Applications, Vol. 50, No. 2, 2014.
- X. Wang, M. Yue, and E. Muljadi, “PV Generation Enhancement with a Virtual Inertia Emulator to Provide Inertial Response to the Grid,” The Energy Conversion Congress and Exposition (ECCE), 2014.
2023
- T. Zhao, N. Raghunathan, A. Yogarathnam, M Yue, and P. B. Luh, “A Scalable Planning Framework of Energy Storage Systems under Frequency Dynamics Constraints,” the International Journal of Electrical Power and Energy Systems, Vol. 145, February 2023.
- A. Yogarathnam, L. Karunaratne, N. R. Chaudhuri, and M. Yue, “Modeling Adequacy of Droop-Controlled Grid-Forming Converters for Transient Studies: Singular Perturbation Analysis,” 2023 IEEE PES Innovative Smart Grid Technologies Conference (ISGT2023).
- J. Li, Y. Zhao, and M. Yue, “Integrating Learning and Physics based Computation for Fast Online Transient Analysis,” the 2023 IEEE PES Innovative Smart Grid Technologies Conference (ISGT2023).
2022
- Y. Lin, J. Wang, and M. Yue, “Equity-based grid resilience: how do we get there?” the Electricity Journal, Vol. 35, No. 5, June 2022.
- T. Zhao, M. Yue, and J. Wang, “Structure-informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics,” IEEE Transactions on Power Systems, Vol. 37, No. 6, November 2022.
- J. Jiao, Z. Tang, J. Yan, P. Zhang, and M. Yue, “Cyberattack-Resilient Load Forecasting with Adaptive Robust Regression,” International Journal of Forecasting, https://doi.org/10.1016/j.ijforecast.2021.06.009.
- T. Zhao, B. Beckman, M. Yue, and R. Foster, “ESE: A Tool for Enhanced STIX Elevation,” Resilience Week, September 2022.
- A. Meliopoulos, M. Papic, S. H. Tindemans, S. Ekisheva, M. Yue, and D. M. Logan, “Composite Power System Reliability with Renewables and Customer Flexibility,” accepted by IEEE PMAS 2022.
- L. Karunaratne, A. Yogarathnam, N. R. Chaudhuri, and M. Yue, “Grid of Near Future: Impact on (N-1) Contingency Analysis in Presence of Droop-Controlled Grid-Forming Converter-Interfaced Generations,” one of the best conference papers in IEEE PES General Meeting 2022.
- T. Zhao, Y. Zhang, and M. Yue, “Scalable Deep Reinforcement Learning-based Volt-VAR optimization in Distribution Systems: a Mean-field Approach,” IEEE PES General Meeting 2022.
- M. Yue, A. Yogarathnam, M. Jensen, T. Fairless, and A. Zhou, “Problem Reformulation for Improved Regression Analysis in Granular Damage Forecasting,” IEEE PES T&D Conference and Exposition, 2022.
2021
- Y. Zhang, M. Yue, and J. Wang, “Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning,” IEEE PES General Meeting 2021.
- G. Larangeira, Y. Yogarathnam, M. Yue, and F. Dong, “Exact Quantification of System Reliability Indexes in Probabilistic Contingency Analysis,” IEEE PES General Meeting 2021.
- J. Li, M. Yue, Y. Zhao, and G. Lin, “Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics,” IEEE SmartGridComm, 2020.
2020
- S. Zhang, A. Yogarathinam, J. Zhan, M. Yue, and G. Lin, “A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid,” IEEE PES General Meeting 2020.
2019
- M. Cui, J. Wang, and M. Yue, “Machine Learning Based Anomaly Detection for Load Forecasting under Cyberattacks,” IEEE Transactions on Smart Grid, Vol. 10, No. 5, January 2019.
- M. Yue, T. Hong, and J. Wang, “Data Analytics Based Anomaly Detection in Time Series Data for Online Cybersecure Load Forecasting,” IEEE Transactions on Smart Grid, Vo. 1, No. 1, January 2019.
- Y. Li, P. Zhang, M. Althoff, and M. Yue, “Distributed Formal Analysis for Power Networks with Deep Integration of Distributed Energy Resources”, IEEE Transactions on Power Systems, Vol. 34, No. 6, Nov. 2019. X. Lu, J. Wang, Z. Li, and M. Yue, “Dynamic Stability Assessment for Integrated Transmission-Distribution System Considering Distributed Energy Resources,” 2019 International Conference on Smart Energy Systems and Technologies (SEST).
2018
- Y. Li, P. Zhang, and M. Yue, “Networked Microgrid Stability through Distributed Formal Analysis,” Applied Energy, Vol. 228, Oct. 2018, pp279-288.
- J. Luo, T. Hong, and M. Yue, “Real-time Anomaly Detection for Very Short-Term Load Forecasting,” Journal of Modern Power Systems and Clean Energy, Vol 6, No 2, March 2018.
- M. Yue, T. Toto, M. P. Jensen, S. E. Giangrande, and R. Lofaro, “A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data,” IEEE Transactions on Smart Grid, Vol. 9, No. 6, November 2018.
- M. Yue, S. W. Kang, C. Jin, and J. Matevosjana, “An Investigation of Potential Intermittency Induced Outage Modes for Wind Generation,” Proceedings of PMAPS 2018.
- M. Yue, “Evaluation of A Data Analytic Based Anomaly Detection Method for Load Forecasting Data,” IEEE PES General Meeting, July 2018
2017
- M. Yue, T. Toto, M. P., Jensen, and S. Giangrande, “Outage Prediction Using Radar Measurement for Facilitating Distribution System Restoration,” IEEE PES General Meeting, July 2017
- M. Yue, “An Integrated Anomaly Detection Method for Load Forecasting Data under Cyberattacks,” IEEE PES General Meeting, July 2017.
2016
- M. Yue and X. Wang, “'An Enhanced Probabilistic Contingency Analysis for Development of Risk-Informed Transmission Planning,” IEEE PES General Meeting, July 2016.
2015
- M. Yue and X. Wang, “Grid Inertial Response-Based Probabilistic Determination of Energy Storage System Capacity under High Solar Penetration,” IEEE Transactions on Sustainable Energy, Vol. 6, No. 3, July 2015.
- M. Yue and X. Wang, “Assessing Cloud Transient Impacts of Solar and Battery Energy Systems on Grid Inertial Responses,” Journal of Electric Power Components and Systems, 43(2):200-211, January 2015.
- M. Yue and X. Wang, “Outage Data Collection and Parameter Estimation for an Improved Probabilistic Contingency Analysis,” IEEE PES General Meeting, July 2015.
- M. Yue and X. Wang, “Grid Inertial Response-Based Probabilistic Determination of Energy Storage System Capacity under High Solar Penetration,” IEEE PES General Meeting, July 2015.
- X. Wang and M. Yue, “Capacity Specification for Hybrid Energy Storage System to Accommodate Fast PV Fluctuations,” IEEE PES General Meeting, July 2015.
2014
- X. Wang, M. Yue, and E. Muljadi, “Probabilistic Approach for Power Capacity Specification of Wind Energy Storage Systems,” IEEE Transactions on Industry Applications, Vol. 50, No. 2, 2014.
- X. Wang, M. Yue, and E. Muljadi, “PV Generation Enhancement with a Virtual Inertia Emulator to Provide Inertial Response to the Grid,” The Energy Conversion Congress and Exposition (ECCE), 2014.
