Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 389-396, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-389-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 389-396, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-389-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

SIMULATING INFRASTRUCTURE OUTAGES: AN OPEN-SOURCE GEOSPATIAL APPROACH

O. Pala1 and P. Schrum2 O. Pala and P. Schrum
  • 1Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
  • 2Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA

Keywords: Critical Infrastructure, Utilities, Spatial Decision Support Systems, Cascading Effects, Outage, Undirected Graph, Graph Theory, GIS

Abstract. Understanding the impact of service outages caused by natural or man-made disasters in utility services is a key part of decision-making in response and recovery efforts. Large-scale outages in the last 15 years, from the 2003 northeast blackout to Hurricane Maria devastating Puerto Rico in 2017, highlighted the importance of tight couplings within and across various utilities. The brittleness of these tight couplings results in long delays in restoring large-scale outages. Such cross-infrastructure effects can make analysis for decision makers and responders far more complex. To facilitate recovery, decision makers need to use specialized Decision Support Systems (DSS) that allow simulation of various alternative enablement options along with their impact on society.
In this article, we describe our geo-simulation engine and datasets used for outage modelling. First, we detail our efforts in correcting and completing Electric Power (EP) network for the western US. Next, we explain the architecture and initial implementation of the platform-independent, open-source geospatial simulation engine that we are in the process of developing. Using this engine, we can consider the amount of commodity at the transmission source (power plants) and sinks (substations) and set thresholds at sinks to trigger and simulate outages. For instance, a threshold can be set to trigger an outage at substation level if the available commodity amount drops below 80 % of the demand. Future additions include cross-infrastructure and enablement consequence analysis to provide a complete and transparent DSS to study outages on multiple interrelating infrastructures through scenario-based evaluation criteria.