Evaluate the Customer Behavior in Competitive EV Charging and Parking Services

[摘要] In the last decade, the U.S. government has spurred efforts to boost the utilization oftransportation electrification technologies, because of their low-pollution emissions, energyindependence, and high fuel economy. An ever-increasing number of electric and plug-inelectric vehicles (EVs and PEVs) will radically change the traditional view of the transportation industry, social environment, and business world. Research on grid integration of EVs and PEVs typically addresses topics at the vehicle-grid boundary such as peak load impacts and optimal charging control. While researchers around the world are making significant advances in these areas, there is very little work investigating the customer behavior in competitive EV charging and parking services. On one hand, as a transportation tool and electricity carrier, EV can be charged at any charging facility and at any time, which brings spatial and temporal demand uncertainty to the service providers. On the other hand, the retail electricity price and parking fee may have an impact on customer behavior, eventually leading to a change in the expected profits of the service providers. In this thesis, the dynamic interactions between service providers and customers are studied and modeled using game theory. In the above mentioned competition, the players decide their own strategies (i.e., retail electricity price, parking fee, and rebate) while considering a variety of physical constraints such as transformer capacity. The customer segmentation is also taken into consideration. More specifically, the competitive market is studied using a non-cooperative Bertrand game. Case studies demonstrate the accuracy, and effectiveness of the proposed solution algorithms

[发布机构] University of Michigan

[效力级别] Game theory

[关键词] Electric vehicle;Game theory;Power system economics;Smart grid;Renewable energy;Electrical Engineering;Energy;Electrical Engineering, College of Engineering and Computer Science