SoftwareintheLoop Planning of Fast Charging Station based on GNNAccelerated Ordinal Optimization The allocation of fast charging stations is vital in promoting the regular travel of electric vehicle. Considering the impact of road network on charging service, we propose a softwareintheloop planning method to simulate the complex traffic condition. A SUMObased fast charging station planning model is established, while user experience and charging stationu0027s operation economy are both considered. To reduce the optimization time, we apply ordinal optimization theory, and establish a crude model to simplify the calculation. Moreover, based on GNNu0027s capability of learning from networks, we propose a GNNaccelerated ordinal optimization method for fast charging station planning, where GNN is deployed to perform endtoend fitting for crude evaluation results. Finally, a numeric example is employed to demonstrate the feasibility of proposed method and the effect of GNN.

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.