• ICON project MonIEFlex (2014-2015)
  • Company partners: REstore, Siemens, Crop’s
  • Research partners: IDLab, Distrinet, SMIT

Steering industrial manufacturer’s power consumption in function of fluctuating green energy supplies.

The threat of global warming, rapid depletion of natural resources such as oil, coal and gas, and recent issues with nuclear power plants have significantly raised political and societal interest in the use of green, renewable energy sources. Yet, green energy comes with an unpredictable output. It’s economically not viable to provide many classic backup facilities like gas-fired power plants that are only used a fraction of the time.  A much more sustainable solution is to adjust power consumption in function of the variable output of solar panels and wind turbines.

The goal of the MonIEFlex project was to unlock additional flexibility in energy consumption in (complex) industrial processes. Using IDLab’s extensive expertise in advanced machine learning techniques a tool was developed to estimate and forecast flexibility in energy consumption taking specific process constraints into account.



  • ICON project SWIFT (2013-2015)
  • Company partners: Eandis, 3E, GE
  • Research partners: IDLab, Distrinet, EELAB

Connecting wind turbines to the electricity grid in a more efficient way – to maximize the availability of green energy.

When new energy sources are integrated in electricity grids, grid operators make an assessment if the grid can handle the power injection at any time. If not, the grid is upgraded. In case of renewable energy sources with a high degree of volatility like wind turbines this means that grids are dimensioned to handle rare peaks, which is is expensive and slows down the process of connecting additional turbines to the grid.

IDLab’s expertise on demand response solutions and techno-economic analyses was combined with the expertise of the other partners to investigate different active network management approaches both from a technical and economical perspective to integrate new wind turbines more quickly and cost-efficiently: increase of local consumption (demand response), dynamic line rating of cables, use of storage technologies, fine grained curtailment of the turbines, and near real-time prediction of local wind production.


The various active network management approaches that we investigated all have their pros and cons, both technologically and economically. The ultimate solution to connecting wind turbines in a more efficient way will be composed of a mix of technologies.
– Matthias Strobbe (IDLab)

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