Comparison of Charging Strategies of Electric Vehicles Using Local Power Production to Minimize Carbon Emissions The German WINNER research project aims at controlling and optimizing local power production, distribution, and consumption in residential areas. Beside residents, who consume electrical energy uncontrollable, there are electric vehicles of a car sharing provider as well. Their charging process can be monitored and controlled. Furthermore, we have a midsize photovoltaic (PV) power plant on the roof of the residential building. Within this local area, we measure energy consumptions and productions. This paper covers subsequent investigations on the possibility of using different charging strategies of electric vehicles from the view of the grid side. Thus, we have to measure energy consumption and production, calculate the surplus and store this excessive energy in the vehicle accumulator. We aim to be neutral against the local energy grid or at least not unhelpful, i. e., we have to avoid power withdrawal if possible. In this paper, we abstract from real use case with electric vehicles. We are using a simple battery model and make some assumptions on the energy consumption and PV production referring to our measurements. Afterwards, a recurrent approach is used to schedule charging and discharging processes along the day. We consider various charging aims over time, i. e., we vary the charging states and times, at which the aimed battery charging state should be reached. Finally, we use our system to answer the question, if our approach to lower the grid load was worth it. For low PV power days and cloudy days, we found usable strategies. Referring to PV peak days we had to do more intensive investigations.

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.