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.

Distributed Coordination of EV Charging with Renewable Energy in a Microgrid of Buildings With the rapid development of electric vehicles (EVs), the consequent charging demand represents a significant new load on the power grids. The huge number of highrise buildings in big cities and modern technological advances have created conditions to mount onsite wind power generators on the buildings. Since modern buildings are usually equipped with large parking lots for EVs, it shows vital practical significance to utilize the on site wind power generation to charge EVs parked in the buildings. In this paper, we first use a case study in Beijing to show that the onsite wind power generation of highrise buildings can potentially support all the EVs in the city. Considering that the charging demand of EVs usually does not align with the uncertain wind power, the coordination of EV charging with the locally generated wind power in a microgrid of buildings is investigated and three main contributions are made. First, we investigate the problem and formulate it as a Markov decision process (MDP), which incorporates the random driving requirements of EVs among the buildings. Second, we develop a distributed simulationbased policy improvement (DSBPI) method, which can improve from heuristic and experiencebased policies. Third, the performance of the distributed policy improvement method is proved. We compare DSBPI with a central version method on two case studies. The DSBPI method demonstrates good performance and scalability.