II3050v2_NAT_2050_high_synfuels

General disclaimer:

• The scenarios presented here are neither an exact prediction of the future nor do they reflect the opinion of the grid operators . Instead they are intended to investigate the impact of different possible developments towards a climate neutral energy system on the energy transport infrastructures. While the developments reflected in the scenarios have been broadly aligned with different stakeholders, alternative choices or variations on certain developments are possible which can be explored in scenario variants.

• The scenario assumptions for the industry sector are to a large extent taken over from the Carbon Transition Model (CTM) which models industrial processes in high detail and has been set-up in alignment with the largest industry parties in the Netherlands. The sectors being affected are chemicals, refineries, fertilizers, steel and waste. For these sectors, final energy demands are shown in ETM but the underlying inputs and assumptions cannot be changed by the user. For more background please consult the scenario documentation (reports).

• All weather dependent profiles follow the climate pattern of the historic climate year 2012 covering different combinations of supply & demand. Accordingly, for this study climate data (e.g. temperature, irradiation, wind profiles) from the Pan European Climate Database (PECD) has been processed and uploaded to the ETM. In addition, specific sectoral demand profiles have been derived from suitable (public) sources to reflect a realistic demand behaviour.

• The ETM is an energy system model covering all relevant energy carriers and end-user sectors for the aim of modelling how energy is being used under different scenarios and to estimate the impact on relevant energy & climate indicators like energy related emissions. However specific aspects like the electricity market, energy exchanges with surrounding countries, the characteristics and the operation of technologies are modelled in a simplified way for the sake of reducing complexity, keeping calculation times low and ensure as much transparency & accessibility to users as possible. Depending on the study and scope, the grid operators apply other more advanced tools e.g. to simulate the European electricity market in more detail and on the requested level of quality.

Scenario National Leadership with high industrial production of synthetic fuels (NAT_high_synfuels):

The Netherlands aim for an energetically efficient energy system within the Dutch capabilities and steers strongly on the future energy mix. The government is making choices about the technologies that will be used in the Netherlands. To this end, the government makes mandatory policy & regulations and participates financially in projects of national importance. The government promotes the development of new industries and stimulates the electrification of existing industry. In the built environment, centralised governance ensures the development of district heating networks, fed mainly by waste heat, geothermal and flexible electric sources such as power-to-heat. For energy supply, large-scale national projects are emerging, such as offshore wind used to maximum capacity and some flexible nuclear power plants. Green hydrogen plays an important role for balancing the electricity system, for supplying high-temperature heat in industry and as a feedstock. In addition to the base scenario, this variant assumes a high additional energy demand and demand for carbons for the large-scale industrial production of synthetic fuels.

This scenario was last updated on April 7, 2023, in an earlier version of the model. The most recent release of the model was on November 5, 2024.
How do updates to the model affect scenarios?
Slider changes
During updates we must sometimes make small changes to the saved settings for some sliders. This is done to ensure that scenario remains compatible with the new version of the model and, whenever possible, that the intentions of the scenario’s author are preserved.
Outcomes
Scenario outcomes may change due to improvements in the model, better quality data, or the addition of new sliders.