Modeling Charging of Electric Vehicles in Smart Cities: Charles Square Use Case The main objective of this paper is to demonstrate a novel method for modeling smart cities and to introduce a use case study related to charging of electric vehicles. This use case is focusing on the research question: how many charging stations are needed to fulfill the dynamic demand for charging of electric vehicles. If electric vehicles become a mass product, it will be necessary to build many charging stations. On the other hand, the charging time will be significantly different from conventional cars and hardly as fast as conventional cars have (at least in the near future). In case of trucks, this will be even bigger difference. We can expect significant improvements in the speed of charging of batteries in the future. In this paper, we do not want to predict what will be the future, but rather demonstrate what will be the impact if we consider the number of charging stations, electric vehicles and charging time. This is a dynamic problem and the usage of a novel SMACEF framework using Multiagent systems is proposed as a suitable tool for this use case, as well as other smart city related problems.

Stochastic Model Predictive Control for Scheduling Charging of Electric Vehicle Fleets with Market Power With the penetration of electric vehicles in local markets, vehicleinduced electricity demand can cause power grid instability. Collaborative smart charging can help stabilize grid demand and mitigate those issues. This paper formulates charge scheduling when connected vehicles constitute a large portion of instantaneous demand. Allowing coordinated charging to sway electricity price, we formulate a multiobjective stochastic optimization problem to minimize cost while maximizing charge in each car. We model stochastic base electricity demand using a Gaussian Mixture Model (GMM) and solve the certaintyequivalent stochastic optimization problem. We then implement a stochastic model predictive control (SMPC) algorithm and compare performance between a naive policy, a certaintyequivalent optimized policy, and SMPC on a dataset derived from California ISOserviced demand.