Enabling Battery-powered Train Operation: Power Forecast Model for Overhead Catenary Islands in Regional Passenger Railway

The amendment of the German Federal Climate Protection Act of 2022 defines targets to reduce greenhouse gas emissions by 65% by 2030 and 88% by 2040 compared to 1990 levels. In Germany, the transport sector accounts for 20% of CO2 equivalent emissions. While most of these emissions stem from road bound traffic, diesel vehicles are still common in rail-transport. For instance, a significant share of commuter transport services is currently achieved with diesel propelled multiple units. To reduce tailpipe emissions, these vehicles are ought to be replaced with electric multiple units. As the installation of overhead catenary along the entire track is often cost intensive and not economically viable, battery electric multiple units (BEMU) are going to be utilized in various railway networks, some of them already in operation. A major challenge of BEMU is the limited autonomy. Common market ready vehicles may cover autonomy ranges between 80 and 200 km, while circulation plans require often considerably larger ranges of not electrified kilometers over an operational day. The limited range of BEMU can be accounted for by installing additional recharging infrastructure at selected stations or track sections, typically designed as overhead catenary islands (OCI), as they are not connected to a wider rail catenary network. The vehicles can recharge the traction battery at these OCI the same way they do under conventional overhead catenary with a pantograph mounted at the vehicle roof. This way, the vehicles can also recharge under already electrified sections. Vehicles often drive under existing electrification when regional commuter lines meet long distance connections; i.e. at the centers of larger cities. OCI`s are usually installed on rural sides, with fewer connections and fewer passengers and, uncommonly in Europe, OCI are connected to the public electricity grid instead of the rail electricity grid. These rural electricity distribution grids are often not designed for the high-power loads occurring in the recharging of rail vehicles. Therefore, it is crucial to quantify occurring peaks and loads to equip weak grids to cope with these new challenges. As BEMU-vehicles are just entering market (for instance, as of 02/2024 only one railway network in Germany is operating BEMU), there is little to no experience in the operation of BEMU and OCI. To gain a better understanding of the implications of these novel technologies, we developed a model to i) model vehicle energy flows, battery state of charge and power at the converter to the catenary and ii) aggregate the overall power at OCI by considering comprehensive circulation plans. The model first links timetable data with vehicle circulation plans and adds infrastructure data such as station locations, max speed, track electrification and inclination. These inputs are run through a longitudinal simulation to calculate the traction power at wheel level. Vehicle energy flows are then modeled to calculate the power at the battery and the resulting state-of-charge of the on-board traction batteries. A geospatial sub-model is incorporated, to match the vehicle trajectories to the OCI. Vehicle recharges are aggregated with regard to the location of the corresponding OCI and the specific time of day. The aggregated curves serve as 24 hour demand profile forecasts of OCI. A sub model is set up to understand the implications of additionally installed stationary battery buffer storages to reduce peak loads on electricity grids. The model is applied on a German regional passenger railway network where BEMU operation is ought to start operation in the upcoming years. The demand profiles are analyzed in regard to peak power, average power and 15 minutes rolling average peaks. The implications on the BEMU operation and on the power grid are analyzed and discussed. As the model utilizes real world circulation plans, the OCI power forecast model generates a power demand profile as it would appear during uninterrupted operation including trips from and to vehicle depots and other additional transfer trips as they are common in many operational plans. The forecast model results can be useful for tendering authorities, railway operators, vehicle manufacturers and grid operators for designing recharging infrastructure and coordinating tradeoffs of dimensions on traction batteries and the density of recharging infrastructure in a railway network.