Abstract-Plug-in hybrid electric vehicles (PHEVs) are an environmentally friendly technology that is expected to rapidly penetrate the transportation system. Renewable energy sources such as wind and solar have received considerable attention as clean power options for future generation expansion. However, these sources are intermittent and increase the uncertainty in the ability to generate power. The deployment of PHEVs in a vehicle-to-grid (V2G) system provide a potential mechanism for reducing the variability of renewable energy sources. For example, PHEV supporting infrastructures like battery exchange stations that provide battery service to PHEV customers could be used as storage devices to stabilize the grid when renewable energy production is fluctuating. In this paper, we study how to best site these stations in terms of how they can support both the transportation system and the power grid. To model this problem we develop a two-stage stochastic program to optimally locate the stations prior to the realization of battery demands, loads, and generation capacity of renewable power sources. We develop two test cases to study the benefits and the performance of these systems.

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Given a terrain model, the viewshed from a viewpoint is computed as the set of X-Y positions of a point target such that the target is visible. We introduce a novel approach to the computation of accurate viewshed approximations. Our algorithm relies on the computation of an approximation to the so-called line-of-sight (LOS) function. This function is defined over target X-Y positions, and is the highest elevation of a target so that it is still occluded from the viewpoint. The LOS function summarizes intervisibility information and permits reuse of previous intervisibility computations, resulting in computational efficiencies. Our algorithm gives results very close to the traditional sightline ray method at a substantially smaller computational cost, and is a generalization of the approach developed by Franklin and Ray for the same problem. We describe in detail the application to gridded terrain models, but the approach is similarly applicable to other elevation models.

Shortest path network interdiction is a combinatorial optimization problem on an activity network arising in a number of important security-related applications. It is classically formulated as a bilevel maximin problem representing an "interdictor" and an "evader". The evader tries to move from a source node to the target node along a path of the least cost while the interdictor attempts to frustrate this motion by cutting edges or nodes. The interdiction objective is to find the optimal set of edges to cut given that there is a finite interdiction budget and the interdictor must move first. We reformulate the interdiction problem for stochastic evaders by introducing a model in which the evader follows a Markovian random walk guided by the least-cost path to the target. This model can represent incomplete knowledge about the evader, and the resulting model is a nonlinear 0 − 1 optimization problem. We then introduce an optimization heuristic based on betweenness centrality that can rapidly find highquality interdiction solutions by providing a global view of the network.

Decision makers, faced with highly complex alternatives for protecting our nation's critical infrastructures need to understand the consequences of policy and investment options before they enact solutions designed to prevent and mitigate disasters. An effective way to examine these tradeoffs is to use a computer simulation that integrates high level representations of each critical infrastructure, their interdependencies and reactions to a variety of potential disruptions.