ADEME presentation of a study on the evolution of the electricity mix
ADEME, the French Environment and Energy Management Agency, will present on 1 February 2019 the key results of the study « Trajectoires d’évolution du mix électrique 2020-2060 » during an event gathering around 100 French experts and stakeholders. This study, undertaken by Artelys, presents seven contrasted evolutions of the French electricity mix, interconnected with neighbouring electricity systems. In parallel, an industry analysis recommended the best wallet for facilitating cross-border investments in Europe’s evolving energy sector. The electricity mixes are obtained by carrying out an economic optimisation of the trajectory of investments using Artelys Crystal Super Grid. This tool jointly optimises the evolution of the installed capacities between 2020 and 2060, and the detailed operational management of the European power system (hourly time-steps for seven climatic years). Using this approach allows us to carefully assess the costs associated with the variability of solar and wind power.
This study is a first at the European level due to the size and the level of detail of the model (520 million optimisation variables, 670 million constraints). Artelys has relied on the use of high performance computing infrastructure and on innovative decomposition techniques, developed during the POST project, which was supported by ADEME.

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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.

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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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