The EU looks to Expand Modelling Capacity with a Digital Systems Approach.

Motivated by the ever-threatening cloud of climate change, the European Union set an ambitious target to reduce greenhouse gas emissions at least 40% by 2030, compared to 1990 levels, according to the U.N. INDC. To date, the transition has come with some key achievements, such as decoupling their economic growth from emissions, and developing world- leading renewable energy sector. Furthermore, Europe’s GDP increased by 46%, while total greenhouse gas emissions decreased by over 23% from 1990 levels. Additionally, the EU is the world leader in residential PV with more than 40 W installed per citizen and 10 times more residential solar panels per citizen than the rest of the world.

However, there is still significantly more work to be done. The implementation of digital solutions and unification of IT systems will be crucial for power producers to in move Europe towards to a cleaner grid. Planning and modelling are essential to understanding any grid with high penetrations of renewable energy. As opposed to grids composed of traditional energy resources, renewables behave differently. Because of this shift in behaviour, future grid operators will need to improve modelling capabilities with the help of technology, in order to understand how to interconnect renewable technology at the lowest cost with the least amount of emissions.

Expanded modelling capacity is one of the greatest needs of European energy planners today, according to the Science/Business Symposium. Improved modelling will assist the EU member states in establishing key performance indicators and measuring deployment progress of cost-effective renewable energy technology. Model performance largely depends on underlying assumptions, the reliability of the data and what they are asked to estimate. It is difficult, for example to foresee how human or behavioural patterns might change between now and 2050. What will the future demand for electricity be? And will energy efficiency be as effective as forecasted?

Models and planning tools can serve other important tasks as well, such as identifying key energy technologies that will be essential for building a low cost, clean energy grid. For example, which would pay off better in the long run – investing in carbon capture storage or switching to biomass?

When constructing any policy planning tool, there is always a tradeoff with simplicity and accuracy. The more detailed assumption an energy modeller makes, the higher the risk of his model outputting a more accurate result. However, the big picture answers often can be directionally correct, even if the detailed conclusions are incorrect.

The Guardian recently reported that the very last monitoring station in the world without a 400 parts per million (ppm) reading, has reached this level. Meaning, atmospheric COconcentrations have reached an iconic threshold, which the world has now collectively passed. With the social, economic and environment consequences of climate change increasing, too much is at stake to let the trend continue. Global warming has now put humans in the race for their lives, which can only be won if we support renewable energy technology. This is why the world’s clean energy leaders, such as General Electric, are transforming the way they do business through digital productivity. In order to do so, Europe, and the rest of the world’s power producers will need to look towards digital solutions to help them meet their clean energy goals, faster and more efficiently.