Toward Efficient Cascading Outage Simulation and Probability Analysis in Power Systems

This paper addresses how to improve the computational efficiency and estimation reliability in cascading outage analysis. Under mild assumptions, we first formulate a cascading outage as a Markov model with specific state space and transition probability by leveraging the Markov property of cascading outages. Such a model provides a rigorous formulation that allows analytic investigation on cascading outages in the framework of standard mathematical statistics. Then, we derive a sequential importance sampling (SIS)-based strategy for cascading outage simulations and blackout risk analysis with solid theoretical foundation. Numerical experiments manifest that the proposed SIS strategy can significantly enhance the efficiency of simulations and reduce the estimation variance of blackout probability/risk compared with the traditional Monte Carlo simulation strategy.

[1]  Chanan Singh,et al.  Expected power loss calculation including protection failures using importance sampling and SOM , 2004, IEEE Power Engineering Society General Meeting, 2004..

[2]  Benjamin A. Carreras,et al.  How Many Occurrences of Rare Blackout Events Are Needed to Estimate Event Probability? , 2013, IEEE Transactions on Power Systems.

[3]  James A. Bucklew,et al.  Introduction to Rare Event Simulation , 2010 .

[4]  Anna Scaglione,et al.  A Markov-Transition Model for Cascading Failures in Power Grids , 2012, 2012 45th Hawaii International Conference on System Sciences.

[5]  Ia An N Do.  A LOADING-DEPENDENT MODEL OF PROBABILISTIC CASCADING FAILURE , 2005 .

[6]  James S. Thorp,et al.  Anatomy of power system disturbances : importance sampling , 1998 .

[7]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[8]  Z. Dong,et al.  Multi-Stage Flexible Expansion Co-Planning Under Uncertainties in a Combined Electricity and Gas Market , 2015, IEEE Transactions on Power Systems.

[9]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[10]  Lennart Söder,et al.  Improved importance sampling for reliability evaluation of composite power systems , 2017 .

[11]  Ian Dobson,et al.  Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model , 2005 .

[12]  Quan Chen,et al.  Composite Power System Vulnerability Evaluation to Cascading Failures Using Importance Sampling and Antithetic Variates , 2013, IEEE Transactions on Power Systems.

[13]  Pierre-Etienne Labeau,et al.  Improving Monte Carlo simulation efficiency of level-I blackout probabilistic risk assessment , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[14]  Chun-Hung Chen,et al.  Rare-event splitting simulation for analysis of power system blackouts , 2011, 2011 IEEE Power and Energy Society General Meeting.

[15]  Ian Dobson,et al.  Validating OPA with WECC Data , 2013, 2013 46th Hawaii International Conference on System Sciences.

[16]  James A. Bucklew,et al.  Splitting Method for Speedy Simulation of Cascading Blackouts , 2013, IEEE Transactions on Power Systems.

[17]  Chun-Hung Chen,et al.  Efficient Splitting Simulation for Blackout Analysis , 2015, IEEE Transactions on Power Systems.

[18]  L. Jeff Hong,et al.  Monte Carlo estimation of value-at-risk, conditional value-at-risk and their sensitivities , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[19]  I. Dobson,et al.  Risk Assessment of Cascading Outages: Methodologies and Challenges , 2012, IEEE Transactions on Power Systems.

[20]  Yue Wang,et al.  Adaptive sequential importance sampling technique for short-term composite power system adequacy evaluation , 2014 .

[21]  L. Soder,et al.  Importance Sampling of Injected Powers for Electric Power System Security Analysis , 2012, IEEE Transactions on Power Systems.

[22]  Ian Dobson,et al.  Using Transmission Line Outage Data to Estimate Cascading Failure Propagation in an Electric Power System , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[23]  David Cornforth.  Long tails from the distribution of 23 years of electrical disturbance data , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[24]  Shengwei Mei,et al.  Towards Estimating the Statistics of Simulated Cascades of Outages With Branching Processes , 2013, IEEE Transactions on Power Systems.

[25]  P. Hines,et al.  Large blackouts in North America: Historical trends and policy implications , 2009 .

[26]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[27]  Nasir Ghani,et al.  Stochastic Analysis of Cascading-Failure Dynamics in Power Grids , 2014, IEEE Transactions on Power Systems.

[28]  Ian Dobson,et al.  An initial model fo complex dynamics in electric power system blackouts , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[29]  Yusheng Xue,et al.  An Efficient Probabilistic Assessment Method for Electricity Market Risk Management , 2012, IEEE Transactions on Power Systems.

[30]  Gang Wang,et al.  A Study of Self-Organized Criticality of Power System Under Cascading Failures Based on AC-OPF With Voltage Stability Margin , 2008, IEEE Transactions on Power Systems.

[31]  Chuangxin Guo,et al.  A Cross-Entropy-Based Three-Stage Sequential Importance Sampling for Composite Power System Short-Term Reliability Evaluation , 2013, IEEE Transactions on Power Systems.

[32]  Janghoon Kim,et al.  Testing Branching Process Estimators of Cascading Failure with Data from a Simulation of Transmission Line Outages , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[33]  James S. Thorp,et al.  Expose hidden failures to prevent cascading outages [in power systems] , 1996 .

[34]  Ian Dobson,et al.  Evidence for self-organized criticality in a time series of electric power system blackouts , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.