Extended Areas of Research

The CityMoS platform does not only enable power system impact studies as conducted in this work. Instead, a broad range of possible topics in the wider area of electrified urban mobility can be investigated. Limits are only set by the platform’s joined federates. Research areas can be extended by, for instance, including arailway transportation system simulation or an agent heat emission model. Besides computer simulations, HLA allows federates also to be live as inlive, virtual, and constructive (LVC) systems. Including those systems as federates in the platform on the one hand allows real-time data to be integrated in the simulation

and on the other hand provide real-time information feedback to enable simulation-based decision support for the live system. In the following, three research questions are outlined which can be investigated with the current CityMoS platform including the improvements described in Section 5.2.1 where necessary. Areas of further research are thereby not limited to those questions. Instead, the questions provide an idea for topics suggesting themselves to be investigated as a logical next step to this work’s research.

• What are optimal spots for placing charging stations?

Transportation electrification requires the installation of CSs. Several constraints have to be considered to determine their optimal number, maximum power connection, and geographical placement. The basic problem thereby is trading coverage for costs. By decreasing the number of CSs, their total costs of installation are naturally also lower.

At the same time, costs in terms of time investments for detours or queuing arise for the drivers. From a power system perspective, the charging impact is spatially clustered which may require strong selective infrastructure enhancements. Repeatedly simulating the transportation system starting with CSs placed at every location each time consolidating neighboring CSs with complementary charging pattern results in identifying charging hotspots. At those hotspots, the charging need is sufficiently high to economically justify installing a CS while at the same time ensuring low time investment costs for the drivers.

• Do tempo-spatially different electricity prices influence the electrified traffic?

Besides the CSs’ location and their maximum power, electricity prices may be the one parameter allowing to control the power system impact. Prices can be both temporally and spatially different and may even be different depending on the maximum power drawn by a PEV. They do not necessarily have to correspond to real electricity market prices. Instead, they can be arbitrarily set to control the charging behavior of PEV drivers targeting different purposes, e.g., controlling the power demand to selectively regulate the power system impact or influencing itineraries of agents to some extent.

There are two research topics involved to comprehensively answer the posed ques-tion. First, agent charging behavior has to be realistically modeled including price-responsiveness to a certain degree. Agents may be differently motivated when, where, and with which strategy to charge. Maximizing their convenience while minimizing their costs is certainly a valid assumption though requiring some variation within the entire agent population. Second, prices have to be tempo-spatially modeled considering forecasts and uncertainties to achieve specific objectives, e.g., selectively relieving the power system infrastructure when necessary. Discriminating prices among different agents allows to better control the charging behavior of the entire agent population.

The strategy of disseminating information on electricity prices is certainly as important as determining prices itself. It is not only a question of whom to provide with which information but also a technical one of how to do this thereby not discriminating against single agents.

The topic for both the agent behavior as well as the price determination and dissemina-tion is getting more complex if in addidissemina-tion to charging also discharging is considered.

Distributed power sources and storage are promoting demand-responsive and

battery-to-grid (B2G) scheduling schemes. They certainly also have an impact on the electricity prices set for PEV charging and discharging.

• How to evaluate the robustness and resilience of a power system against faults as well as random or targeted attacks?

A power system is designed to faultlessly satisfy the demand of its consumers at all times. Faults in any of its components may result in not fulfilling this obligation. They may be the result of a technical malfunctioning or an attack against the power system and may occur either randomly or targeted. The latter aims at wreaking most damage by first targeting electrical installations having the highest influence on satisfying the power demand. While a power system is robust if it in principle continues functioning after a fault or cascading outages without fundamental changes to the system, the resilience refers to the power system’s capability to adapt to a fault by changing the system’s operation [257]. Using CityMoS Power, a robustness or resilience metric can be calculated by selectively removing buses or branches and subsequently conducting power flow simulations. Alternatively, it can roughly be approximated by the node degree or the betweenness [210]. The robustness and resilience of each electrical installation allows to evaluate its importance with regard to the proper functioning of the system. It may even allow identifying possible bottlenecks and targets worthwhile to be attacked. This, in turn, offers the possibility to enhance the system by selectively expanding it thereby including redundancy to some degree.

Content

A.1 CityMoS Power XSD . . . 154 A.2 Topological, Electrical, and Economic Metrics of the Cost-optimized

Singapore PNM . . . 160