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Component based event simulation for stochastic failures of power networks during extreme events.

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SV_PLM: Social Vulnerability and Power Loss Mitigation

Component based event simulation for stochastic failures of power networks during extreme events.

About

The SV_PLM (Social Vulnerability and Power Loss Mitigation) package is designed to allow users to model the social vulnerability impact associated with power transmission network losses in a given region. The included data files allow the package to be used for simulation of the transmission system of Puerto Rico. The Component Based Event Simulation (CBES) model given here has 5 key components, outlined below. In depth details of the methodology can be found in:

Boyle, Esther, Alireza Inanlouganji, Thomaz Carvalhaes, Petar Jevti\'{c}, Giulia Pedrielli, T. Agami Reddy, Social Vulnerability and Power Loss Mitigation: A Case Study of Puerto Rico (May 3, 2021). Available at SSRN: https://ssrn.com/abstract=3838896 

Python package dependencies

numpy pandas scipy math random networkx gurobipy - Liscence needed. Free academic liscence available from https://www.gurobi.com/

How to cite

Boyle, Esther, Alireza Inanlouganji, Thomaz Carvalhaes, Petar Jevti'{c}, Giulia Pedrielli, T. Agami Reddy, Social Vulnerability and Power Loss Mitigation: A Case Study of Puerto Rico (May 3, 2021). Available at SSRN: https://ssrn.com/abstract=3838896

Modeling Components

Weather Event Generation: Simulates the maximum windspeed exerted on each transmission line of network

Fragility Model: Determines the fragility of each transmission line of the network based on the observed maximum windspeed by geographical region. Based on fragility of transmission towers reported in:

Panteli M, Pickering C, Wilkinson S, Dawson R, Mancarella P. Power System Resilience to Extreme Weather: Fragility Modelling, Probabilistic Impact Assessment, and Adaptation Measures. IEEE Trans Power Syst 2017 32:1-1. doi:10.1109/TPWRS.2016.2641463. 

and further available from:

Bennett, Jeffrey A.; DeCarolis, Joseph F.; Clarens, Andres F., 2020, "Model and data for "Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico"", https://doi.org/10.18130/V3/QB0NPX, University of Virginia Dataverse, V1 

Stochastic Network Breakage: Simulates failures of transmission lines

Power Flow Model: Simulates power flow through a given network

Social Vulnerability Model: Estimates impact of power loss on social vulnerable communities and produces critiality metrics for identifying critical network components

Included Data

  1. adjacency.csv: Adjacency matrix of the power network topology of Puerto Rico, gathered from the following sources:

    “Puerto Rico Integrated Resource Plan (IRP) 2018-2019: Draft for the Review of the Puerto Rico Energy Bureau,” Siemens,Tech.Rep.RPT-015-19,2019.[Online]. Available: aeepr.com

    “Fortieth Annual Report on the Electric Property of the Puerto Rico Electric Power Authority,” URS Corporation, San Juan, Tech. Rep., June 2013. [Online]. Available: https://aeepr.com/en-us/quiC3A9nes-somos/portal-inversionistas/financial-information

  2. NetworkLinks.csv: Information about the links in the network topology of Puerto Rico, gathered from the following sources:

    “Puerto Rico Integrated Resource Plan (IRP) 2018-2019: Draft for the Review of the Puerto Rico Energy Bureau,” Siemens,Tech.Rep.RPT-015-19,2019.[Online]. Available: aeepr.com

    “Fortieth Annual Report on the Electric Property of the Puerto Rico Electric Power Authority,” URS Corporation, San Juan, Tech. Rep., June 2013. [Online]. Available: https://aeepr.com/en-us/quiC3A9nes-somos/portal-inversionistas/financial-information

  3. NetworkNodes.csv: Information about the nodes in the network topology of Puerto Rico, gathered from the following sources:

    “Puerto Rico Integrated Resource Plan (IRP) 2018-2019: Draft for the Review of the Puerto Rico Energy Bureau,” Siemens,Tech.Rep.RPT-015-19,2019.[Online]. Available: aeepr.com

    “Fortieth Annual Report on the Electric Property of the Puerto Rico Electric Power Authority,” URS Corporation, San Juan, Tech. Rep., June 2013. [Online]. Available: https://aeepr.com/en-us/quiC3A9nes-somos/portal-inversionistas/financial-information

  4. LineIntersections.csv: Gives which municipalities each link of the network passes through in the network topology of Puerto Rico, gathered from the following sources:

    “Puerto Rico Integrated Resource Plan (IRP) 2018-2019: Draft for the Review of the Puerto Rico Energy Bureau,” Siemens,Tech.Rep.RPT-015-19,2019.[Online]. Available: aeepr.com

    “Fortieth Annual Report on the Electric Property of the Puerto Rico Electric Power Authority,” URS Corporation, San Juan, Tech. Rep., June 2013. [Online]. Available: https://aeepr.com/en-us/quiC3A9nes-somos/portal-inversionistas/financial-information

  5. HistoricalMaxWindspeedscsv: Maximum sustained wind speeds in each municipality of Puerto Rico for 30 historical storm events, gathered from the following sources:

    National Ocianic and Atmospheric Administration, “Global Surface Summary-of-the-Day -GSOD,” National Centers for Environmental Information. Accessed: 2021. Available:https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day[64]

International Best Track Archive for Climate Stewardship (IBTrACS) Technical Documentation 2019 NOAA/NCEI. https://www.ncdc.noaa.gov/ibtracs/pdf/IBTrACSversion4TechnicalDetails.pdf

  1. CDC_SoVI_2017.csv: Contains SoVI for the municipalities of Puerto Rico gathered from

    “CDC Social Vulnerability Index,” Agency for Toxic Substances and Disease Registry. Accessed: 2021. [Online]. Available: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html

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