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.

In recent years, the transmission network expansion planning (TNEP) problem has become increasingly complex. As this problem is a nonlinear and nonconvex optimization problem, researchers have traditionally focused on approximate models of power flows to solve the TNEP problem. Until recently, these approximations have produced results that are straightforward to adapt to the more complex problem. However, the power grid is evolving towards a state where the adaptations are no longer as easy (e.g., large amounts of limited control, renewable generation), necessitating new approaches. In this paper, we propose a discrepancy-bounded local search (DBLS) that encapsulates the complexity of power flow modeling in a black box that may be queried for information about the quality of a proposed expansion. This allows the development of an optimization algorithm that is decoupled from the details of the underlying power model. Case studies are presented to demonstrate cost differences in plans developed under different power flow models.

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