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Basisszenario Steinfurt 2030 (Steinfurt, 2030)

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Das Basisszenario stellt eine realistische Entwicklung in der Region Steinfurt dar. Im Zieljahr 2030 sind die Technologiekosten wie erwartet gesunken und die Ausbaupotenziale für erneuerbare Energien orientieren sich an den aktuellen Rahmenbedingungen. 30 % der nachgefragten Energiemenge des regionalen Verkehrssektors und 6 % des regionalen Wärmemarktes werden durch Wasserstoff gedeckt.

The base scenario represents a realistic development in the Steinfurt region. In the target year 2030, the technology costs have decreased as expected and the expansion potentials for renewable energies are oriented to the current framework conditions. 30 % of the demanded energy volume of the regional transport sector and 6 % of that of the regional heating market is served by hydrogen.

 

 

System key figures: KPIs

Total balances

Hydrogen

Electricity

Balances over the course of the year

Hydrogen

Electricity

Heat

Water

Full-load hours

Composition of regional hydrogen production costs

total: [[ renderValueWithUnit(other_scalars.hydrogen_supply_costs, {minimumFractionDigits: 2, maximumFractionDigits: 2}) ]]

Profability of the overall system
net present value (total system) [[ renderValue(other_scalars.npv_total_system, {maximumFractionDigits: 2, maximumSignificantDigits: 2}) ]] [[ renderUnit(other_scalars.npv_total_system) ]]
return on equity [[ renderValue(other_scalars.return_on_equity, {maximumFractionDigits: 1, maximumSignificantDigits: 3}) ]] [[ renderUnit(other_scalars.return_on_equity) ]]
payback period [[ renderValue(other_scalars.infrastructure_amortization_period, {maximumFractionDigits: 1, maximumSignificantDigits: 2}) ]] [[ renderUnit(other_scalars.infrastructure_amortization_period) ]]

Composition of revenues

total: [[ renderValueWithUnit(other_scalars.total_revenues, {maximumSignificantDigits: 2, maximumFractionDigits: 2}) ]]

Direct regional added value

total: [[ renderValueWithUnit(kpis.regional_added_value, {maximumFractionDigits: 2, maximumSignificantDigits: 2}) ]]

CO₂ emissions

total: [[ renderValueWithUnit(other_scalars.total_emissions, {maximumFractionDigits: 2, maximumSignificantDigits: 2}) ]]

Avoided external costs

total: [[ renderValueWithUnit(kpis.forgone_external_costs, {maximumFractionDigits: 2, maximumSignificantDigits: 2}) ]]

Unforeseen events

The infrastructures will be in operation for 15 years and longer. Since initial assumptions about hydrogen demand, system performance, willingness to pay, or electricity costs may change over time, what-if scenarios help to understand the likely impact of changes in the operating environment for hydrogen infrastructure.

[[ what_if_parameter.hydrogen_demand_transport_factor ]]%
[[ what_if_parameter.hydrogen_demand_heat_factor ]]%
[[ what_if_parameter.regional_hydrogen_production_factor ]]%

Warning: If the system availability is decreased, less hydrogen will be produced and the missing output has to be replaced by imported hydrogen. If the initial scenario has a tight limit on imports, this may not be possible. In this case, you can allow exceeding the import limit below.

[[ what_if_parameter.average_electricity_price_factor ]]%
[[ what_if_parameter.willingness_to_pay_transport_factor ]]%
[[ what_if_parameter.willingness_to_pay_heat_factor ]]%

Note: System optimization has been activated. Due to the heavy calculations involved, the KPI will no longer automatically update; rather, the update has to be triggered by clicking the button below.

System optimization is running...

System optimization succeeded.

System optimization failed. We are currently experiencing technical difficulties. Please try again in a few minutes.

System optimization failed. Under the given parameter changes, no operation strategy can always satisfy both the resource constraints and the hydrogen demand.

Note: The effects on the system KPI are visualized above. This simulation is simplified and does not yet include the optimization of the operating strategy of the existing plants, so that the results are generally somewhat less favourable than an adjusted operating strategy would be. Optimization of the operating strategy may be activated using the checkbox above. However, this may take a while due to the amount of calculations involved.

Profit Optimization

While most factors are beyond the control of regional actors, there are some adjustments to improve system performance. How-to scenarios quantify the impact of specific policies or activities on operational profit, facilitating decision-making and increasing hydrogen economy adoption.

The desired profit can be achieved by adjusting the parameter value to [[ renderValue(how_to_result) ]] [[ renderUnit(how_to_result) ]] .

Calculation started.

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