For most people, a comfortable living and mobility are basic needs. With the rising individual demand for energy as well as the diminishing fossil energy resources, new optimized concepts for energy supply and usage are required. To address these challenges, renewable energy sources, decentralized storage, and electric mobility concepts are matters of rapidly growing importance.Future building energy systems have to successfully integrate user demands, local renewable energy, storage systems and charging infrastructure, a task requiring extensive scrutinizing.Typical questions to the engineer are to compare different system layouts with respect to sustainability, cost, and robustness, or to identify the right levers in an energy system to optimize components and control algorithms. This paper describes an approach to solve such questions using simulations with the non-causal language Modelica. Modeling paradigms and examples are shown. Special emphasize is given to the "Green Building" library and its components, bringing major building energy systems and electric vehicles to the same platform.

The development of efficient electric vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail. Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus the requested amount of energy or increased state of charge (SoC), where values are drawn from normal (Gaussian) distributions with mean and variance values derived from user studies of obviously limited sample size. In this work, we compare this basic approach with a more detailed customer model employing a multi-agent simulation (MAS) framework in order to investigate how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction in past charging sessions) impacts the essential key performance indicators of the charging system. Our findings show that small changes in the way customers are modeled can lead to quantitative and qualitative differences in the simulated performance of EV charging systems.

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Building physics and HVAC system simulation have become an important usage scenario of the Modelica modeling language and related simulation tools since the publication of first adequate libraries (Wetter, 2009). In 2010, the tool independent standard FMI was published in version 1.0. It enables the exchange of models between different simulation tools and even different modeling approaches. Although, automotive industry mainly initiated the FMI development, it can extensively benefit building simulation, too. This paper describes four completely different applications of FMI in the building simulation environment which even extend the basic idea of simple model exchange. This includes the description of developed models as well as additionally required software components implementing the FMI standard.

Renewable energy production and decentralized energy storage as well as optimized usage of existing energy resources are matters of rapidly growing importance. Even today in building architecture as well as modern mobility concepts these technologies are major cost drivers. Staying abreast to these changes, EA EnergieArchitektur GmbH together with IAD TU Dresden are developing a simulation tool to identify and optimize the potentials for building specific energy storage and production as well as optimized usage strategies on the consumer side. Furthermore the simulation tool allows analyzing the smart integration of new eMobility concepts. In this it works as a test bench for system wide energy management with priority on charging strategies for such vehicles from the decentralized power supply. Keywords: renewable energy; eMobility; modeling 1 Why a holistic energy simulation for car and buildingToday, there are various technologies available to provide local renewable energy, for example: microwind-turbines, photovoltaics, solar heat, heat pumps and combined heat and power units (CHP).These energy systems use direct natural energy resources like wind and sun or renewable fuels like wood, bio-gas or even vegetable oil. The availability and efficiency of these resources differ greatly depending on the specific location. Furthermore these energy systems are expensive in money and production resources. Therefore it is important to find an optimized configuration before installing an energy system in a specific building. A second thought on optimizing renewable energy includes the time of availability. There is no sunshine at night. Is it better to store the daylight energy using batteries, charge a heat storage or to install the photovoltaics facing westwards thus providing more energy when demand is high -in the evening? In the course of the increasing demand on electric mobility, the need for charging concepts has risen. Both power and energy have to be provided. Synchronization of the demand on energy and storage as well as on availability in a building is an important fact for future energy-management systems. Electric Mobility as mobile storage with constraints to availability (docked, undocked) and requirements from lifestyle (e.g. 100% charge in the morning) adds further complexity to the system. The energy system layout for a specific combination Building-eMobility as well as the research and development of optimized energy-management algorithms are two engineering tasks which demand for a