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Renewables and Grid Analysis Group


The Renewables and Grid Analysis Group supports the Sustainable Energy Technologies Department by performing research that addresses the key challenges facing modernization of the electric power grid and the integration of renewable energy resources.


  • 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

  • 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


Sample Research Projects

Probabilistic Grid Reliability Metrics

Traditional contingency analyses calculate the potential for experiencing system problems deterministically.These results can serve as metrics for reliability in transmission planning and can be used to develop mitigation plans and remedial actions for improving reliability. However, traditional deterministic contingency analyses (DCA) are not very accurate since they do not differentiate the likelihood of occurrences among different types of contingencies. The implied assumption of equally likely contingencies in DCA can lead to over conservativism in decision-making for planning activities. It is also difficult to treat variable generation from renewables using DCA. We are addressing this by developing probabilistic reliability metrics. Probabilistic models and the parameterization of the models for grid contingencies are being developed to provide input data for frequencies and duration times of different outage modes in the contingency analysis. Historical renewable generation data is being collected to analyze the statistics of these outage modes, e.g., how often a reduction in wind generation at a certain level occurs and how long this reduction lasts. Such models for renewable generation can be readily fed into an enhanced Python-driven Probabilistic Contingency Analysis tool, which was developed and is available at BNL to calculate the probabilistic reliability metrics.

Dynamic Stability Assessment

The increased penetration of distributed energy resources (DERs) in the power grid can lead to new and unprecedented challenges in maintaining reliable operation. Power electronic devices are usually used to interface with DERs, such as FACTS, energy storage systems, and HVDC links with the grid. Although they enable ultra-fast grid control and load changes, the high penetration of power electronic components will reduce the grid inertia significantly, making the grid highly sensitive to disturbances and threatening the power system stability. To address this concern, BNL is establishing an innovative and tractable method for assessing the stability of both transmission and distribution grids under uncertainties from heterogeneous sources such as DERs. A formal theory with mathematical rigor is being developed for computing the bounds of all possible (infinitely many) trajectories and estimating the stability margin for the entire system including the integrated transmission and distribution network. The goal is to develop a new open-source Formal Analysis tool based on reachable set computations that can be used for real-time dynamic analysis and stability margin calculations. It will be applicable for not only forecasting and monitoring grid performance but also formally verifying various resiliency enhancement strategies such as new schemes for system integrity protection and automation to adapt to this evolution of electric networks.

Stochastic Sizing and Operation of Grid Energy Storage

Energy storage systems (ESSs) offer a possible means of maintaining a healthy level of grid inertial response where renewable generation penetration levels are high. ESSs possess response speeds that are superior to conventional generators and can be beneficial to operations by providing much needed energy, ramping as well as reserve services, especially under high penetration of intermittent renewables.  The major challenges that need to be addressed with ESS deployment are (1) what are the required capacities of ESSs, i.e., the sizing of ESSs, to achieve the desired inertial response to different types of disturbances; and (2) how to efficiently operate the grid with ESSs to ensure efficiency, security and reliability upon their deployment. BNL is developing revolutionary methodologies to answer these questions. As part of our energy storage research, we are developing probabilistic sizing techniques for ESSs based on grid-inertia responses under high penetration of renewables, along with stochastic operation optimization techniques for ESSs that account for the intermittence of renewable generation. The grid inertial response-based ESS sizing approach will ensure the frequency stability of the grid, while the stochastic operation optimization approach will ensure efficient and reliable operation of ESSs together with other units on the grid.

Cybersecurity for Grid Forecasting Data

Energy delivery systems are increasingly dependent on sophisticated forecasting data for efficient 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 is developing a method to detect and mitigate cyber-attacks on this forecasting data. The goal is to develop a 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

  • Y. Li, P. Zhang, M. Althoff, and M. Yue, “Distributed Formal Analysis for Power Networks with Deep Integration of Distributed Energy Resources”, accepted by IEEE Transactions on Power Systems, to appear.
  • M. Cui, J. Wang, and M. Yue, “Machine Learning Based Anomaly Detection for Load Forecasting under Cyberattacks,” accepted by IEEE Transactions on Smart Grid, to appear.
  • M. Yue, T. Hong, and J. Wang, “Data Analytics Based Anomaly Detection in Time Series Data for Online Cybersecure Load Forecasting,” accepted by IEEE Transactions on Smart Grid, to appear.
  • J. Zhan, M. Yue, and L. Fan, “Reliability-Based Stochastic Transmission Expansion Planning Considering Uncertainties of Dynamic Thermal Rating and Wind Power,” IEEE PES General Meeting, August 2019
  • 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 Clear 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 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.
  • 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.
  • 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
  • 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.
  • M. Yue and X. Wang, “'An Enhanced Probabilistic Contingency Analysis for Development of Risk-Informed Transmission Planning,” IEEE PES General Meeting, July 2016.
  • 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.
  • 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.
  • M. Yue and X. Y. Wang, “A Revised Incremental Conductance MPPT Algorithm for Solar PV Generation Systems,” Published online and available at
  • X. Y. Wang, M. Yue, and E. Muljadi, “Modeling and Control System Design for an Integrated Solar Generation and Energy Storage System with a Ride-through Capability,” Proceedings of IEEE ECCE 2012.
  • M. Yue and X. Wang, “Assessment of Cloud Transient Impacts of Grid-integrated Solar and Battery Energy Systems,” IEEE 39th Photovoltaic Specialists Conference, June 2013.
  • M. Yue and X. Wang, “Dynamic Modeling of Grid Integrated Solar and Battery Energy System for Cloud Transient Impact Study,” IEEE EnergyTech Conference, May 2013.
  • X. Y. Wang, M. Yue, and E. Muljadi, “Modeling and Control System Design for an Integrated Solar Generation and Energy Storage System with a Ride-through Capability,” Proceedings of IEEE ECCE 2012.
  • M. Yue, M. Villaran, R. Lofaro, and X. Wang, "Detailed Modeling and Integration Study of Grid-Connected Solar PV Generation with Energy Storage and Power-factor Control Capability," Solar Power International Conference, October 2011.
photo of Grid Trans Towers