NOVAgent

The increasing integration of a large number of decentralised, renewable energy producers combined with decreasing network budgets has posed new challenges for distribution network operators in recent years and continues to do so today. In addition, air and climate protection is focusing on the need to reduce emissions through the transformation of the transport sector by means of sector coupling of road transport and electrical energy supply, and will lead to further increases in the demands on electrical distribution networks in the coming years. Electromobility is also changing the supply task of distribution network operators, which is characterised by increasing volatility, and is significantly increasing planning uncertainties. The divergent and wide range of possible market run-up scenarios and spatial penetration rates of electric vehicles play a major role in this context.

The complexity of distribution network planning increases considerably as a result: while conventional planning is based on only a few extreme cases, the supply task will in future have to be increasingly differentiated in terms of location and customer-specificity. In addition, the call for temporal and technical flexibility must already be taken into account in the planning process in order to realise a network expansion in line with demand. Finally, the divergent bandwidth of the different generation, storage and consumption scenarios must be handled with network planning agility and addressed with a risk-based prioritisation of measures.

For this purpose, NOVAgent is to determine the location-specific investment behaviour in electric vehicles and storage solutions on the prediction level based on divergent framework conditions using data mining methods based on sociodemographic & socio-geographic data. The movement and consumption profiles of the users and their multi-dimensional interaction define the future generation situations and supply tasks in a time- and location-specific manner in the form of hotspot analyses and are obtained by agent-based simulations weighted by probability. On the action level, the results enable the connection of spatial and network planning, which allows the determination of optimal charging infrastructures and the derivation of prioritised cost-optimal recommendations for action in distribution network planning by means of innovative optimisation.

Project duration: 05/2019 - 04/2022