Agent-based modeling and simulation of large scale electric mobility in power systems

Due to a paradigm shift towards more sustainable socio-ecological energy systems, more and more distributed energy resources are integrated into power systems. This, together with the the liberalization of electric power systems, creates numerous challenges. Build and expanded during the last century, the power system was initially not designed to cope with large amounts of distributed energy resources, sometimes far away from load centers and with fluctuating power infeed. An increased level of transnational electricity trading, one facette of the liberalization, leads to an increasing number of congestion of cross-border electricity lines. Recently, in order to transform the system into one that can stand up better to these challenges, the concept of Smart Grid gained momentum. While its exact definition is still to be formulated, among other goals, the introduction and integration of large scale electric mobility is one central issue. The benefits of individual electric transportation feature an increased overall energy efficiency, especially if the vehicles are charged by renewable energy sources, and the possibility utilize the vehicles as a potential storage in power systems. However, the introduction of individual electric mobility on a large scale also poses numerous challenges to the power system infrastructure initially not designed to supply loads of unknown temporal and, even worse, spatial behavior. The vision of using the vehicles as a storage, which includes feeding power from the vehicles to the power system, commonly referred to as vehicle-to-grid (V2G) services, aggravates the complexity of the challenge. This thesis develops an agent based analysis tool in order to investigate the impacts of large scale electric vehicle adoption on power systems. The tool, beyond simple analysis measures, provides an agent based, intelligent charging control approach. It allows to operate the power