3 Toolchain //

The scenario building workflow is structured as a multi-stage modelling process, supported by a set of complementary tools that exchange data throughout the modelling chain (Scenario Toolchain).

The objective of the toolchain is to ensure a consistent and traceable translation of annual scenario inputs into hourly demand profiles, market simulation outputs and aggregated supply indicators, while allowing different modelling components to be developed in a coherent framework.

Figure 2 illustrates the data flow in the Scenario Toolchain used in the TYNDP 2026 scenario building cycle. Starting with TSOs data collection in ETM and other tools, a first result is the annual Final Energy Demand, provided with a detailed sectoral breakdown. After hourly profiling and market modelling electricity and hydrogen in PLEXOS®, model outputs are presented in the form of country-specific dashboards (yearly and hourly) also allowing a sub-nodal view. The Supply Tool finally complements the model output with non-modelled energy carriers in PLEXOS® and derives the Total Energy Consumption and CO2 emissions.

Additional tools and input data from PECD and PEMMDB are explained in the respective chapters below.

The Scenario Toolchain focuses on electricity, hydrogen, synthetic fuels, heat and transport demand. Fossil fuel demand collected through the data collection is neither transferred to nor explicitly modelled in PLEXOS®, as the corresponding fuel markets are not represented within the scope of the modelling framework. It is bypassed directly to the Supply Tool, where it is combined with the primary energy demand resulting from the PLEXOS® simulations to compute Total Energy Consumption.

This approach allows sectoral demand assumptions, with electricity, hydrogen, heat and synthetic fuels demand profiles, and market modelling to be developed in a consistent and iterative manner. In practice, this means that some input values initially defined in ETM are refined downstream through explicit modelling in PLEXOS®. One example is the dispatch of boilers and heat pumps in hybrid heat pump systems which is a result of the hourly PLEXOS® modelling and might lead to slightly different results compared to the original TSO data collection.

A detailed sectoral breakdown is available at ETM output level only, while the PLEXOS® model for Scenario Building 2026 represents demand with a more aggregated sectoral structure. Further details on the market model representation are provided in Chapter 8.

Following the collection of sectoral energy volumes in ETM, the scenario building process proceeds with the integration of these outputs into the other modelling tools. For TYNDP 2026, dedicated interfaces were developed to enable a structured exchange of data between the ETM and other tools within the modelling chain.

These interfaces connect the ETM to the three main downstream components of the Scenario Toolchain:

This integrated workflow ensures that sectoral demand assumptions are consistently translated into carrier-specific demand profiles for electricity, hydrogen, heat (for hybrid heat pumps) and synthetic fuels, which are subsequently used as inputs for market simulations.

The starting point of the scenario building process is the collection of sectoral annual energy demand data in the ETM.

Within the ETM environment, electricity and gas TSOs provide projections of final energy demand across a detailed sectoral breakdown and across multiple energy carriers (including electricity, hydrogen, methane, heat, biofuels, coal, oil, ammonia and others).

All data provided by TSOs correspond to NT. The NT+ ­scenario is derived only after the application of the gap-filling ­methodology, as described in Chapter 9 of this report.

The ETM data collection covers the four scenario target years: 2030, 2035, 2040 and 2050.

All projections are anchored to the ETM reference year 2019, which currently serves as the baseline energy system dataset within the ETM. The baseline values are mostly derived from Eurostat energy statistics, ensuring a harmonised and transparent starting point for all countries included in the Scenario Building process.

The results of this data collection are aggregated and can be explored through an interactive Visualisation Platform, allowing stakeholders and scenario developers to explore the evolution of energy demand across sectors, countries, and energy vectors.

Annual electricity demand values collected in ETM and complemented with additional data (additional electricity demand for CCS, grid losses, etc.) are converted into hourly electricity demand profiles using the Demand Forecasting Tool (DFT).

The DFT generates electricity demand time series using sectoral annual electricity demand, historical load patterns, normalised load patterns and weather-dependent relationships derived from the selected Weather Years. As such, the resulting profiles represent final electricity demand only and do not include components that are modelled endogenously in the power system optimisation. In particular, electricity consumption from electrolysers, as well as electricity used for storage technologies, is not included in the DFT-generated demand profiles, as these are determined as part of the market simulation.

The DFT-derived demand profiles therefore capture native electricity demand, including base load, electricity demand from heat pumps, and electric vehicle charging profiles based on predefined assumptions.

These time series serve as inputs to the power system modelling performed in PLEXOS®.

Within the power system model, selected demand components are represented explicitly and optimised endogenously, notably passenger electric vehicles and hybrid heat pumps. For these technologies, electricity consumption is not taken directly from the DFT profiles but instead results from the optimisation process, allowing for price-responsive behaviour and technology-specific operational constraints enabled by the sector-coupled modelling of electricity, heat, and gas.

Consequently, while the DFT provides the underlying structure of final electricity demand (including base load, heat pumps, and electric vehicles), part of this demand – specifically for passenger EVs and hybrid heat pumps – is re-optimised within the model, whereas other demand components such as electrolysers and storage-related consumption are fully determined as model outputs.

Hydrogen demand time series are constructed using a ­dedicated tool which translates annual hydrogen demand volumes from ETM and an additional data collection (see Chapter 4) into hourly profiles. Using the sectoral breakdown of demand provided by ETM, the tool constructs sector-specific demand time series. The sectoral profiles are subsequently aggregated to form hourly hydrogen demand per node. The resulting time series are used as inputs to energy system modelling in PLEXOS®. Certain hydrogen uses, such as for power generation, hydrogen boilers as part of hybrid heat pumps, and the production of synthetic fuels are modelled endogenously.

Hybrid heat pump thermal energy demand profiles are generated using a similar tool. The tool translates annual thermal demand into hourly profiles based on temperature-dependent relationships. The time series are used as inputs to the model, which determines endogenously whether the demand is satisfied using a boiler or a heat pump (see Chapter 5.3 for explanation).

Synthetic fuel demand profiles are derived by distributing the aggregated EU27 annual demand uniformly across all hours of the year. The resulting flat demand profiles for synthetic natural gas (SNG) and e-liquids are used as inputs to energy system modelling in PLEXOS®.

For further details please see Section 5.4 on synthetic fuels.

The installed capacities considered in the analysis, together with all other data required to model electricity generation, transmission, and electrolyser demand, are collected through the Pan-European Market Modelling Database (PEMMDB) application. PEMMDB enables electricity TSOs to report this information on a unit-by-unit basis, thereby centralising the parameters required to construct the market model. The reported data primarily include the allocation of units to the bidding zones represented in the model, commissioning and decommissioning dates, installed ­generation capacities, and relevant operational constraints.

Data submission within PEMMDB remains under the responsibility of the reporting TSOs and follows the ENTSO-E data governance process, including dedicated validation rounds and consistency checks to ensure alignment with the applicable TYNDP scenario assumptions. Following validation and data freeze, unit level data are aggregated through dedicated scripts to safeguard sensitive information and comply with data confidentiality requirements. The resulting aggregated and scenario consistent dataset is subsequently used as input to the modelling framework.

All energy carrier-specific demand profiles generated along the scenario building toolchain are provided as inputs to PLEXOS®, which performs hourly market simulations for the integrated European electricity and hydrogen ­systems. The market simulations determine, among others, generation dispatch, energy flows, and system operation across the modelling horizon by minimising system cost over the ­considered target year and under given boundary conditions.

This step provides insights into cost-sensitive elements such as electricity and hydrogen generation, fuel consumption, energy flows and hydrogen imports, and supports the ­assessment of the implications of the demand projections for the integrated energy system.

The Supply Tool aggregates model outputs together with exogenous data collections to construct a consistent energy balance across all energy carriers, illustrating how they are used and where they come from – whether from domestic production, imports or conversion from one carrier into another (for example, heat supplied from hydrogen that is produced by domestic electrolysers powered by electricity). In doing so, it balances demand and supply for all energy carriers and ensures a consistent representation of energy flows across sectors within the scenario framework.

The resulting supply side information for the EU27 also serves as the basis for calculating the EU wide carbon budget. Further details on the EU-wide carbon budget methodology are provided in Section 5.6.

The TYNDP 2026 Scenario modelling workflow establishes a structured interaction between sectoral demand modelling, demand profiling, and integrated electricity and hydrogen market simulation. By connecting the ETM, various hourly demand forecasting tools for different carriers, PLEXOS®, and the Supply Tool through dedicated interfaces, the methodology ensures a coherent translation of sectoral demand assumptions into multi-energy system outcomes.

This integrated approach represents an important methodological improvement compared to previous scenario cycles, strengthening the consistency between demand projections and market modelling, while maintaining a system-wide energy perspective.