This high-tech platform uses artificial intelligence to analyze and predict where local and regional grid congestion risk is highest. It is not based on contracts for maximum capacity or (gu)estimations, but on actual data. CRISP creates a virtual representation of energy infra (a digital twin of electricity), to predict grid congestion. For this, the digital twin needs reliable data from several sources.
For each asset, energy supply / consumption profiles are modelled and aggregated to sub-station level and subsequently to higher voltage stations, creating a bottom-up view. These are used to create realistic energy use forecasts.
With these data sources CRISP creates high resolution insights. These already improve policy and investment decisions. But CRISP also allows for simulations: by submitting scenarios, the impact on a local part of the grid or on existing bottlenecks can be assessed. CRISP can simulate network evolution for energy decisions or even test the impact of specific assets such as adding a battery.
The combination of insights and simulations can answer questions such as: what is the best location for a solar farm? Where can large scale energy storage systems best be placed? How many charge stations can we build at this location without creating a bottleneck? Will additional investment be needed in the future? Any scenario, on local, regional or country level, can be simulated to substantiate investment decisions and reduce congestion risk.
Together with NEO, Itility developers created solution blocks that use earth observation data to create this accurate digital twin of electrical energy infrastructure. Software developers created API’s for several data sources, using low-code solution blocks to process data, and a user-friendly interface for the AI assistant.
CRISP can run on any cloud, but we advise the use of a sovereign or private cloud when using sensitive data.