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Background: We use Agent Based Models (ABMs) to study and contrast the projected adoption of integrated photovoltaic and battery systems in both Ontario, Canada and Bavaria, Germany. Methods: We carry out surveys in both jurisdictions to elicit Agent Based Model (ABM) model parameters and to learn the decision function that determines whether an agent purchases a system or not. We use our fitted ABMs to assess the impact of different policy variants on Solar Photovoltaic (PV) system adoption in both jurisdictions. Results: We find that different adoption behaviours exist in both jurisdictions, and that, in jurisdiction, of the polices that we considered, different policy incentives bring about the most significant increase in adoption. For example, reducing PV prices best increases adoption in Ontario but increasing the price of electricity would have the most significant impact in Germany. Conclusion: ABMs allow policy makers and PV/battery manufacturers to estimate the jurisdiction-specific impact of a range of policy prescriptions.

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The electrification of the mobility sector comes with multiple challenges such as the lack of information on when, where, how long and how fast charging processes of electric vehicles will take place. In order to keep up with increasing power demand of charging processes, besides better predictions also the active control of charging processes will be necessary to minimize infrastructure costs. This work deals with a realtime mechanism for supporting the Power Quality (PQ) in electric distribution grids in terms of congestion and voltage management. In the paper, we propose a distributed smart charging approach that considers real-time conditions of the distribution grid provided by an event-driven architecture that collects data from different points in the grid. Our approach adopts the traffic light model, which allows smooth changing of the charging power to avoid drastic changes of the grid state. In order to be ready for real-world application, the algorithm is validated by a series of experiments on two setups: Pure software (co-)simulation and Power Hardware In the Loop (PHIL) where physical charging stations and electric cars are controlled in a laboratory setup.

Electric vehicles (EVs) are gaining widespread adoption, which requires expanding the charging infrastructure. This infrastructure is part of a complex ecosystem that consists of multiple entities interacting with each other and exchanging (often personal) user data. Such a heterogeneous system with multiple participants exchanging personal data poses severe privacy risks to users. State-of-the-art literature insufficiently covers privacy aspects of charging ecosystem use cases. In this paper, a profound analysis of this ecosystem with respect to privacy is provided: First, the EV charging ecosystem and its entities are defined. Second, high-level use cases for EV charging identified in literature are analyzed and used for defining data flows within the charging ecosystem. Third, the identified use cases are compared in terms of privacy guarantees and adherence to standards. Fourth, representative implementations of these use cases are evaluated, i.e., all actors and (unintended) data flows are described and potential privacy threats are identified and visualized. It is found that privacy is not sufficiently covered by standards and implementations of EV charging use cases from literature. Furthermore, recommendations and future directions for protecting user privacy in the EV charging ecosystem are derived. In summary, stricter adherence to standards and privacy by design are suggested.