Green Energy Lab Open Data Platform (GEL ODP)

Abstract

The transition from a centralised energy system on the basis of fossil fuels to a decentralised, renewables- based system requires the large-scale implementation of technological innovations and measurement systems. Such intelligent metering devices (smart meters) enable customers to analyse their energy consumption and to participate pro-actively in the electricity market. This can be done, for instance, through flexible electricity prices and adapted electricity procurement. For such an implementation, however, a data transfer and a subsequent visualisation of the data is necessary. In this manner, the measurement data can be made available to the stakeholders involved: end customers, energy suppliers, grid operators, but also research companies. A better knowledge of the consumption and generation of the customers enables distribution grid operators to better understand the condition of the grid and free grid capacities can subsequently be made available. This enables an improved integration of consumers with high peak loads such as heat pumps or electric vehicles. In the GEL OpenDataPlatform (GEL ODP) project, a publicly accessible web platform for the energy sector was developed to provide easy access to and an overview of relevant data and interrelationships in the energy system. The online platform enables customers to view their energy consumption and generation of a photovoltaic system online in a clear and concise manner. The greatest importance was attached to compliance with the Basic Data Protection Regulation. Although all information is geographically assigned to an area, it can no longer be traced back individually by combining several households. The smallest possible resolution always comprises at least five households, which means that data protection is implemented in the best possible way in combination with local weather data that is as accurate as possible. This weather information is used to calculate the expected heat demand with the assistance of an intelligent predictive algorithm. From that, a temporal flexibility with regard to the electricity demand can be derived, which covers both the heat demand and the electricity demand. It can also be used in the future, e.g. for more cost-effective electricity procurement. This promotes the active participation of customers in the electricity market. In addition, algorithms developed particularly for this research project make it possible to disaggregate the measured total electricity demand and assign it to specific appliance classes. The resulting classification of total consumption by individual consumers enables customers to identify high-consumption devices. On the one hand, this creates a better understanding of electricity costs, and on the other hand, reasonable measures to increase energy efficiency can be identified