Abstract
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.
Highlights
AND MOTIVATIONDecision makers face many challenges in prioritizing resources for protection and response during and after the occurrence of a disaster
The environment during a terrorist attack can be considered as opposing sides wanting to control the states of the critical infrastructure systems in order to achieve their goals (Haimes 2006)
In such a changing decision-making environment, reducing the decision-making effort can be as important as increasing the decision quality (Todd et al, 1992)
Summary
AND MOTIVATIONDecision makers face many challenges in prioritizing resources for protection and response during and after the occurrence of a disaster. In the pre-planning stage, homeland security and emergency management planners discover and isolate weaknesses within a power grid that are likely to be impacted by a disaster This process is often hindered by limited information about the utilities. There has been significant interest in analysing interdependencies from various perspectives, mainly resilience, optimization and modelling In order both to guard against and respond to critical infrastructure failures, multi-dimensional infrastructure modelling and simulation has been proposed as a way to support analysis and decision-making (Rinaldi et al, 2001; Tolone, 2004; Dudenhoeffer, 2006; Santella et al, 2009; Wilson et al, 2009, Eusgeld et al, 2011). Their reliability is insufficient in situations in which complex interdependencies exist (Coffrin et al, 2012)
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