Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs that correlates with a value depreciation of the entire EV. As EV users' satisfaction depends on reliable and value-stable products, car manufacturers offer charging assistants for simplified and sustainable EV usage by considering individual customer needs and battery aging. Hitherto models to quantify battery aging have limited practicability due to a complex execution. Data-driven methods hold feasible alternatives for SOH estimation. However, the existing approaches barely use user-related data. By means of a linear and a neural network regression model, we first estimate the energy consumption for driving considering individual driving styles and environmental conditions. In following work, the consumption model trained on data from batteries without degradation can be used to estimate the energy consumption for EVs with aged batteries. A discrepancy between the estimation and the real consumption indicates a battery aging caused by increased internal losses. We then target to evaluate the influence of charging strategies on battery degradation

Open data base analysis of scaling and spatio-temporal properties of power grid frequencies

The electrical energy system has attracted much attention from an increasingly diverse research community. Many theoretical predictions have been made, from scaling laws of fluctuations to propagation velocities of disturbances. However, to validate any theory, empirical data from large-scale power systems are necessary but are rarely shared openly. Here, we analyse an open database of measurements of electric power grid frequencies across 17 locations in 12 synchronous areas on three continents. The power grid frequency is of particular interest, as it indicates the balance of supply and demand and carries information on deterministic, stochastic, and control influences. We perform a broad analysis of the recorded data, compare different synchronous areas and validate a previously conjectured scaling law. Furthermore, we show how fluctuations change from local independent oscillations to a homogeneous bulk behaviour. Overall, the presented open database and analyses constitute a step towards more shared, collaborative energy research

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented. This comparison is based on a simulated dataset and both R and Apache Spark are used. Furthermore, the obtained results show certain points in which the utilization of distributed computing based on Spark may be advantageous