Smart Grid for Electric Point Heating Systems (InfraGrid)
Electric point heating systems in the Deutsche Bahn AG network consume as much energy as a large city in winter. Calculated over the whole year, the average electrical power requirement of a single point heating system corresponds to the consumption of a two-family house (approx. 10,000 KWh). Since it is only operated at low temperatures, consumption in winter is correspondingly higher.
Electric point heating systems in the Deutsche Bahn AG network consume as much energy as a large city in winter. Calculated over the whole year, the average electrical power requirement of a single point heating system corresponds to the consumption of a two-family house (approx. 10,000 KWh). Since it is only operated at low temperatures, consumption is correspondingly higher in winter. The state of the art is the local weather-dependent operation of the individual systems. Here, the points are heated depending on environmental parameters measured on site. The determination of the environmental parameters is locally limited by the number and type of sensors used. By integrating weather forecasts in conjunction with the consideration of a plant network instead of a single plant, a more efficient management of the energy supply and an associated reduction in costs are possible. In addition, weather forecasts offer the possibility of increasing operational availability.
The work can essentially be divided into three areas:
- The preparation of load forecasts through weather forecasts and communication with the switch heater manufacturers.
- Communication of load forecasts and energy peaks to or from energy suppliers
- Internal creation and processing of all information
The GRIB (GRIdded Binary) format standardised by the World Metereological Organization offers the greatest potential with regard to a universal weather service. First, the processing of the raw data from the German Weather Service was implemented in the GRIB format including interpolation to any coordinate and could be used as a basis for the weather service to be developed. The exact weather data, parameters and their temporal resolution were determined in consultation with the point heating manufacturers. The developed prototype was then further developed and adapted. Based on this, a weather service was implemented and automated. The weather data is downloaded, processed and stored in a database. From this, they can be accessed via a web service that has an interface defined and documented as part of the project. This was also implemented and enables the project participants to retrieve forecast data as well as historical weather data in combination with historical plant data. The historical plant data is available as diagnostic data from the DB Netz AG diagnostic platform. The processing of the weather data was converted to the new standard format for weather forecasts of the WMO, GRIB2, in the course of the project.
The data from the diagnostic platform was also used as a starting point for an analysis of the historical behaviour of the plants in order to develop a model for the load forecast. The most important finding here is that the individual plants behave very individually and therefore the historical behaviour of the plant itself must be known for a forecast. The behaviour of an individual plant, in turn, is reproducible if different periods of time are considered for a plant.
Within the framework of a reference implementation, the basis for the new work package “Increasing operational availability” was created. In close consultation with the switch heating manufacturers, scenarios were first identified that lead to switch icing. These were subsequently transferred into a decision tree.
The analysis of weather forecasts now makes it possible to predict the occurrence of these scenarios and to determine switch-on times for preventive heating in an automated manner, so that the previously usual manual operation can be dispensed with.
A visualisation was developed to display the various work packages in a central location. This enables users to view the weather data provided by the weather service throughout Germany as well as in relation to the operating locations of DB Netz AG. For operating sites where historical data from the diagnostic system is available, the user can view the historical behaviour of the individual systems under specific climatic conditions. In addition, the calculated times at which it makes sense to preheat the systems based on the scenario analysis are displayed.
From the point of view of optimising energy consumption, a new operating mode for point heating systems was developed. The operating mode “OFF_CONDITIONAL” switches the system off if no precipitation is expected despite low temperatures. The operating mode was adopted in the new version of the technical document (TU) of DB Netz AG and will thus be a prerequisite for all new installations in the future. An exact precipitation forecast is necessary for the new operating mode. In order to optimise the existing forecast, the integration of radar data was implemented. These are obtained from an external partner and provide a precise, Germany-wide precipitation forecast for a time horizon of 2 hours with a resolution of 5 minutes.
As a further result, research was carried out to determine which price components affect the electricity consumption of point heating systems in various electricity price models. Based on this, a simple tool was developed that displays and calculates the composition of the electricity price on the basis of the load profiles entered for a point heating system.