General Lab Information

Energy Systems Division

Renewables and Grid Analysis

Renewables and Grid Analysis 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

  • 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

Resources

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.

New York Center for Grid Innovation

Brookhaven Lab is pursuing the development of a new facility that will enable the research needed to help New York State meet its energy goals—the New York Center for Grid Innovation. With research and technology, New York State is reimagining how electricity is produced, managed, shared, and consumed by all market participants (including customers), exploiting major capabilities in data management to deliver the electricity system of the next 100 years.

Details of the New York Center for Grid Innovation.

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.

Selected Publications

[1] 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,” accepted by IEEE PES General Meeting 2020.

[2] M. Yue, “Method and Tool Development for Assessing Renewable Impacts on Probabilistic Contingency Analysis,” EPRI Risk-based Planning Industry Workshop, November 2019.

[3] 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, November 2019.

[4] 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, Vol. 10, No. 6, November 2019.

[5] 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..

[6] Y. Li, P. Zhang, and M. Yue, “Networked Microgrid Stability through Distributed Formal Analysis,” Applied Energy, Vol. 228, October 2018, pp279-288.

[7] M. Yue, “Evaluation of A Data Analytic Based Anomaly Detection Method for Load Forecasting Data,” Proceedings of IEEE PES General Meeting, July 2018

[8] 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.

[9] M. Yue, “An Investigation of Modeling and Parameterization of Intermittency Induced Renewable Outages,” EPRI Risk-Based Planning Workshop for Transmission System Planning, March 1 – 2, 2018.

[10] M. Yue, “An Enhanced Probabilistic Contingency Analysis for Transmission Planning under High Penetration of Renewables: Data and Tool,” Panel Session of Risk Based Planning, IEEE PES General Meeting, July 2017.

[11] M. Yue, “An Integrated Anomaly Detection Method for Load Forecasting Data under Cyberattacks,” IEEE PES General Meeting, July 2017.

[12] M. Yue and X. Wang, “An Enhanced Probabilistic Contingency Analysis for Development of Risk-Informed Transmission Planning,” IEEE PES General Meeting, July 2016.

[13] 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.