Delivery of Artelys Crystal City to the European Metropolis of Lille
In 2018 and 2019, Artelys supported the European Metropolis of Lille (MEL) in the development of an energy planning study, aiming to strengthen the knowledge of the current energy situation of the territory, and to build a shared vision and a roadmap common to all actors.
This study made a major contribution to the Climate Air Energy Territory Plan (PCAET) accepted by the metropolis in December 2019, which further confirms its commitment to fight against global warming.
The use of advanced modelling tools such as Artelys Crystal City enables the metropolis to assess the impacts of different energy policies (energy renovation of buildings, energy efficiency, etc.) and adapt the territory’s strategy in a dynamic way.
Artelys led the Assessment of Policy Options for Securing Inertia for the European Commission
The European Commission’s Directorate-General for Energy (DG ENER) selected Artelys (leader), Trinomics, and Tractebel ENGIE to study solutions for ensuring the future frequency stability of the European power system. The study report was published in August 2025 by...
Artelys Knitro 15.0: New Tools for Your Large-Scale Models
Artelys is pleased to announce the release of Knitro 15.0, which provides new algorithms and performance improvements to solve your large-scale optimisation problems, whether linear or non-linear, more quickly.
Artelys Introduces Future Sight: a Visualisation Tool Supporting the Energy Transition
As partner in the European Climate + Energy Modeling Forum (ECEMF) – a Horizon 2020 Europe project uniting research institutes and leading energy modellers in Europe – Artelys has contributed to modeling activities powered by Artelys Crystal Super Grid modelisation tool, and has led the development of a fully-fledged visualisation tool.
Supercharging Optimisation: How Artelys, FICO and NVIDIA cuOpt Join Efforts to Scale Up Energy System Optimisation
As energy system models continue to scale—reaching up to hundreds of millions of variables and constraints—traditional CPU-based optimisation solvers are hitting performance and memory bottlenecks. These increasingly complex models are essential for planning Europe’s energy transition, yet solving them within realistic timeframes has become a pressing challenge.
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