Our vision is to significantly reduce European energy costs in the coming years through intelligent expansion. Our state of the art planning tool gives you the means to model complex renewable energy behaviour accurately and hereby enables you to build more profitable and at the same time more sustainable systems
We work together as a multidisciplinary and international team to reach this goal.
We offer cloud based, complex energy system planning. The foundation of our energy system planning were laid in cooperation with RWTH Aachen University and Stanford University. Our services build on this foundation to make the latest scientific findings available
independent from device and timezone
adapted to work processes of energy planners
use extensive computing capacities, latest algorithms and automatic parallelization
focused on the critical information and answers
decision making through the comparison of multiple, detailed scenarios
integrate your own expertise
Emission reduction targets and falling prices for renewable generation and storage technologies are leading to structural changes in energy systems. This increases their dependence on weather conditions. The planning of future energy systems is supported by mathematical simulation and optimization models, which are, however, limited by available storage and the time required for solution. This leads to a balancing act between modelling of temporal complexity, spatial complexity and mapping of physical properties and ends in compromises.
To enable more detailed physical and spatial representation, it is common practice to reduce the temporal complexity of models by aggregation. For this aggregation, representative periods are usually modelled instead of the complete time series. We have shown that the aggregated representation of wind time series to representative days reduces the reliability of the energy system, leads to considerable distortions and underestimates the total costs. This phenomenon is particularly relevant for simulations and planning of energy systems with low emission limits. We find that the required capital costs are underestimated by more than 35% and the emissions using three weeks on representative days are more than twice as high as the planned emission limit.
Neither an increase in the number of representative periods, nor any other representation of the clusters, nor any different attribute weighting can solve the three problems mentioned. We recommend not to use time series aggregation to representative days to design energy systems with low emissions, especially if wind resources are important. Planning should consider cross-sectoral different technologies, high resolution weather, consumer and grid data in time and space.
The solutions offered by the market so far are no longer up to date because they use less adaptable and outdated algorithms, depend on expensive local computing power and require time-consuming data preparation.