We study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.

10aRM11-0071 aAlizadeh, Mahnoosh1 aWai, Hoi-To1 aChowdhury, Mainak1 aGoldsmith, Andrea1 aScaglione, Anna1 aJavidi, Tara uhttps://certs.lbl.gov/publications/optimal-pricing-manage-electric01887nas a2200217 4500008003900000245007000039210006900109260003900178300001200217520122000229653001001449653000901459653001301468100002301481700001601504700002001520700002201540700001801562700001701580856007201597 2014 d00aOptimized path planning for electric vehicle routing and charging0 aOptimized path planning for electric vehicle routing and chargin aMonticello, IL, USAbIEEEc10/2014 a25 - 323 aWe consider the decision problem of an individual EV owner who needs to pick a travel path including its charging locations and associated charge amount under time-varying traffic conditions as well as dynamic location-based electricity pricing. We show that the problem is equivalent to finding the shortest path on an extended transportation graph. In particular, we extend the original transportation graph through the use of virtual links with negative energy requirements to represent charging options available to the user. Using these extended transportation graphs, we then study the collective effects of a large number of EV owners solving the same type of path planning problem under the following control strategies: 1) a social planner decides the optimal route and charge strategy of all EVs; 2) users reach an equilibrium under locationally-variant electricity prices that are constant over time; 3) the transportation and power systems are separately controlled through marginal pricing strategies, not taking into account their mutual effect on one another. We numerically show that this disjoint type of control can lead to instabilities in the grid as well as inefficient system operation.

10aCERTS10aPEVs10aRM11-0071 aAlizadeh, Mahnoosh1 aWai, Hoi-To1 aScaglione, Anna1 aGoldsmith, Andrea1 aFan, Yue, Yue1 aJavidi, Tara uhttps://certs.lbl.gov/publications/optimized-path-planning-electric