Parameter Estimation for Distribution Grid Reliability Assessment

Strengthening distribution grids reliability and resilience against technical and natural hazards is a costly endeavor including equipment upgrades and distributed energy resources. Therefore, using accurate data when assessing grid reliability is key to identify effective solutions. As literature parameters can be inaccurate for specific locations, tuning and validating reliability models against real-world data is key for accurate assessments. In this paper, distribution grid reliability is modelled by considering three failure mechanisms in a Monte Carlo simulation: bus and line failures within the distribution grid, blackouts of the surrounding grid, and dependent failures due to extreme events. Ten parameters governing the frequency and duration distributions of the three failure mechanisms are tuned using metaheuristic optimization. A subsequent global sensitivity analysis quantifies the importance of the estimated parameters.

[1]  Carmine Galasso,et al.  Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database , 2018 .

[2]  Marco Cuturi,et al.  On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests , 2015, Entropy.

[3]  Javier Contreras,et al.  Reliability assessment for distribution optimization models: A non-simulation-based linear programming approach , 2017, 2017 IEEE Power & Energy Society General Meeting.

[4]  Mahmud Fotuhi-Firuzabad,et al.  Role of Outage Management Strategy in Reliability Performance of Multi-Microgrid Distribution Systems , 2018, IEEE Transactions on Power Systems.

[5]  Melissa Dumas,et al.  Extreme Weather and Climate Vulnerabilities of the Electric Grid: A Summary of Environmental Sensitivity Quantification Methods , 2019 .

[6]  Janusz Bialek,et al.  Benchmarking and Validation of Cascading Failure Analysis Tools , 2016, IEEE Transactions on Power Systems.

[7]  Wei Yuan,et al.  Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters , 2016, IEEE Transactions on Smart Grid.

[8]  Anmar Arif,et al.  Distribution Network Outage Data Analysis and Repair Time Prediction Using Deep Learning , 2018, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[9]  Harvey M. Wagner,et al.  Global Sensitivity Analysis , 1995, Oper. Res..

[10]  Anna Scaglione,et al.  Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems , 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[11]  A. Al-Shaalan.  Reliability Evaluation of Power Systems , 2019, Reliability and Maintenance - An Overview of Cases.

[12]  Enrico Zio,et al.  A particle swarm‐optimized support vector machine for reliability prediction , 2012, Qual. Reliab. Eng. Int..

[13]  Enrico Zio,et al.  The Monte Carlo Simulation Method for System Reliability and Risk Analysis , 2012 .

[14]  R.E. Brown,et al.  Failure rate modeling using equipment inspection data , 2004, IEEE Transactions on Power Systems.

[15]  Giovanni Sansavini,et al.  Meta-Heuristic Approach for Validation and Calibration of Cascading Failure Analysis , 2018, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[16]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[17]  Xiaobo Zhou,et al.  Global Sensitivity Analysis , 2017, Encyclopedia of GIS.

[18]  Stefania Conti,et al.  Monte Carlo Simulation by Using a Systematic Approach to Assess Distribution System Reliability Considering Intentional Islanding , 2015, IEEE Transactions on Power Delivery.

[19]  J. G. Bartzis,et al.  A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer , 2017, Boundary-Layer Meteorology.

[20]  Stefano Marelli,et al.  UQLab: a framework for uncertainty quantification in MATLAB , 2014 .