About the project
Project description
Objective
The increasing integration of a large number of decentralized, renewable energy generators in conjunction with decreasing grid budgets has presented distribution grid operators with new challenges in recent years. In addition, air and climate protection is bringing the need to reduce emissions by transforming the transport sector through the sector coupling of road traffic and electrical energy supply into focus and will further increase the demands on electrical distribution grids in the coming years. Electromobility is also changing the supply task of distribution grid operators, which is characterized by increasing volatility, and is significantly increasing planning uncertainties. The divergent and wide range of possible market ramp-up scenarios and spatial penetration levels of electric vehicles play a key role in this.
This significantly increases the complexity of distribution grid planning: while conventional planning is based on only a few extreme cases, the supply task will increasingly have to be considered on a locally differentiated and customer-specific basis in future. In addition, the use of temporal and technical flexibilities must already be taken into account in the planning process in order to ensure that the grid is expanded in line with demand. Finally, the divergent range of different generation, storage and consumption scenarios must be handled in an agile manner in terms of grid planning and addressed in a risk-based prioritization of measures.
To this end, NOVAgent is to determine the location-specific investment behavior in electric vehicles and storage solutions at the forecast level based on divergent framework conditions using data mining methods based on socio-demographic and socio-geographic data. The movement and consumption profiles of the users and their multidimensional 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 in a probability-weighted manner using agent-based simulations. The results enable the connection of spatial and grid planning at the action level, whereby optimal charging infrastructures can be determined and prioritized cost-optimal recommendations for action for distribution grid planning can be derived using multi-criteria optimization.
Results
The results serve as a preliminary stage for further commercial use in the form of studies or software applications for grid operators by the participating SMEs.
Project partners (consortium)
TU Dortmund ie3, ef-ruhr GmbH, enerVance GmbH
Project partners (associated)
Westnetz GmbH, Stadtwerke Bochum, Stadtwerke Witten