General Lab Information

Energy Systems Division

Renewables and Grid Modernization

Advanced Grid Modeling

Develop Risk-Informed Decision-Making Capability using Electric Power Industry Standard Planning Tools as Platform 


Completed on September 30, 2021.


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 conservatism in decision-making for planning activities. It is also difficult to treat variable generation from renewables using DCA. Therefore, the objective of this project was to demonstrate the usefulness of probabilistic electric grid reliability metrics and the complementary enhancement of the deterministic counterpart considering various uncertainties, including those from renewable generation in the emerging probabilistic contingency analysis (PCA) for a large utility scale system.


BNL addressed this by developing probabilistic reliability metrics. Probabilistic models and the parameterization of the models for grid contingencies were developed to provide input data for frequencies and duration times of different outage modes in the contingency analysis. Historical renewable generation data was 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.


  • A PCA tool has been successfully developed and case studies have been completed;
  • The final report was reviewed by Idaho Power Company and ERCOT and has been submitted; and
  • The final version of the tool has been delivered.
  • A follow-on study was completed to further refine the PCA tool by developing an enhanced quantification Scheme for PCA
  • Applied a formal statistical test to determine the poolability of grid component outage data from different sources and develop distributions to characterize component outage statistics
  • Developed a grid component outage data repository based on outage information collected from various sources
  • Proposed and developed probabilistic models and parameterization for intermittency induced outages (IIOs) and common mode IIOs to take into account fast ramping events of renewable generation in the probabilistic contingency analyses
  • Developed improved quantification schemes, i.e., a minimum cutset upper bound (MCUB) and binary decision diagram (BDD), for probabilistic reliability indexes
  • Implemented using a set of Python modules driving the PCA capability in PSS/E, an enhanced PCA (ePCA) tool, available by request


G. Larangeira, A. Yogarathnam, M. Yue, and F. Dong, “Exact Quantification of System Reliability Indexes in Probabilistic Contingency Analysis,” accepted by IEEE PES GM, 2021.

M. Yue, G. Larangeira, F. Dong, and R. Lofaro, “An Investigation and Implementation of Quantification Methods for Probabilistic Contingency Analysis,” Technical report submitted to AGM, September 2020.

M. Yue and J. Zhan, “Enhancing PSS/E Probabilistic Contingency Analysis for Development of Risk-Informed Decision-Making Capability,” Technical report submitted to AGM, July 2019.

M. Yue, “Method and Tool Development for Assessing Renewable Impacts on Probabilistic Contingency Analysis,” EPRI Risk-Based Planning Industry Workshop, November 13, 2019.

M. Yue, “An Investigation of Modeling and Parameterization of Intermittency Induced Renewable Outages,” EPRI Risk-Based Planning Industry Workshop, March 1, 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, “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.

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, “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.