The economic value of photovoltaic (PV) systems depends on country-specific conditions. This study investigates the impact of grid fees, solar irradiance and local consumption on the profitability and penetration of PV systems and batteries in renewable energy communities. The linear optimization model calculates the optimal investments into PV and storages applied on a test community, which represents the European housing situation. The comparison of eight countries considers individual heat and cooling demands as well as sector coupling. Results show that renewable energy communities have the potential to reduce electricity costs due to community investments and load aggregation but do not necessarily lead to more distributed PV. Besides full-load hours, the energy component of electricity tariffs has the highest impact on PV distribution. Under current market conditions, battery energy storage systems are rarely profitable for increasing PV self-consumption but there is potential with power pricing. Renewable energy communities enable individuals to be a prosumer without the necessity of owning a PV system. This could lead to more (community) PV investments in the short term. Hence, it hinders investments in a saturated PV market.

In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.