With the progressing deployment of battery electric vehicles (BEV) in public transportation the peak demands of energy for loading batteries will become an essential problem since the BEV have to recharge their batteries at some charging stations in order to overcome the range limitation. On the one hand, when generating required amounts of energy by photovoltaic systems, an effective tuning regarding the network utilization may open up degrees of freedom and, thus, cost saving and higher efficiency potentials. The scheduling of public transportation networks is already subject of extensive optimization approaches, which still mainly consider only cost savings for vehicle fleet operations and neglect BEV-induced requirements and stochastic aspects. Large amounts of both real-word data and data generated by stochastic simulation need to be incorporated in the new approaches for BEV-scheduling and for charging infrastructure design in order to predict and to control the future energy demand of public transport. The energy yield of photovoltaic systems does not only depend on the available solar radiation and given outdoor conditions. Such a system is tunable to some extent. One can adapt the materials spectral response as well as the installation conditions to the needed peak loading times. From theoretical simulations and from data acquired outdoors, we aim at identifying suitable module types and mounting conditions to optimize the yield to a required load profile.This project aims to optimize the PV installation to a given load condition and, furthermore, to integrate optimized control strategies for energy yield and for (emerging) energy demand arising from electric buses in public transportation.
1. Paolo Graniero, Atse Louwen, Rutger Schlatmann and Carolin Ulbrich. Comparison of Different Data Sources for Machine Learning Algorithms in Photovoltaic Output Power Estimation. Poster presentation at the online 37th EU PVSEC, 7-11 September 2020.