The Power Switcher simplifies Axpo’s complex optimization model into an accessible, user-friendly tool that delivers instant results. This tool leverages key assumptions from the larger model, such as seasonal hydroelectric production and electricity imports from neighboring countries like Italy.

The Power Switcher is based on a simplified energy stacking methodology that relies on monthly energy balances rather than hourly capacity balances. This approach is specifically tailored to Switzerland's energy system, where hydropower and pumped storage play a key role in managing short-term fluctuations. This means that monthly energy balances are the basis for the model instead of power balances. Unlike many other European countries, in Switzerland it is not a potential capacity shortage in individual hours that is critical for the security of supply. This is because a high number of hydraulic storage and pumped storage plants and new storage options make it possible to compensate for hourly or daily fluctuations in electricity production. The problem, rather, is that in the event of a prolonged lull in other electricity production, large storage reservoirs can run dry. In this case, the energy scarcity can result in a possible interruption of the power supply.

Key Assumptions: Generation and Demand Profiles

• Electricity Generation: The model calculates the maximum monthly production for each technology based on installed capacities and utilization rates. These monthly profiles are derived from historical data, reflecting the average availability of each energy source.
• Electricity Demand: Monthly demand is distributed according to typical annual profiles. Scenarios define the annual total demand, while historical data guide how this demand is allocated across the months.

We chart how much electricity can be generated by which technologies on an annual and monthly basis. In terms of methodology, this means that for each of the scenarios we record the electricity generation capacity for each technology. Based on the maximum amount of electricity that can be produced monthly by each technology, we derive the maximum amount of electricity that can be produced in each month of the year. The stored monthly profiles for each technology can be found in the input data for capacities under their utilisation. Typical profiles are derived from historical data based on average availability.

For demand, the figures showing the percentage of the demand typically occurring in each month are also stored. The annual quantities are taken from the respective scenarios.

Results are the energy mix, security of supply, resilience and costs. The Power Switcher allows users to analyze whether Switzerland can secure its electricity supply under different scenarios. It highlights the trade-offs involved, such as the challenges of reducing reliance on imports while avoiding nuclear energy or landscape-impacting generation. Achieving a secure supply will require compromises. With the Power Switcher, users can explore whether Switzerland will have sufficient electricity or face shortages based on various factors, including the country’s energy policy goals (e.g., Mantelerlass), slower PV expansion, or unfavorable weather conditions.

The energy balances show Switzerland's supply security, which is sensitive to prolonged periods of low renewable energy production. Such phases could drain reservoirs and lead to energy shortages. These effects can be tested in Expert Mode using the red stress sliders.

Stress Tests: Five Adjustable Parameters
Users can explore the robustness of scenarios under challenging conditions by adjusting the following stress factors:

1. Weather Dependency of Renewable Energy (Wind, PV, Hydro)
Production levels of renewable energy are varied based on historical weather data:

• Very low: Worst conditions observed between 1982 and 2016. For each month, the worst weather year is selected, maintaining monthly correlations (e.g., between countries and technologies like wind and PV).
  Example: January 2008 represents the worst January; all weather-dependent generation in Switzerland and neighboring countries is based on this data. February 2010 corresponds to the worst February, and so on.
• Low: Fifth-worst weather year.
• Normal: Average weather year.
• High and Very high: Fifth-best and best weather year, respectively.

2. Temperature Variations
Adjustments are made to temperature-dependent electricity demand:

• Cold and Very cold: Average temperatures 2.5°C and 5°C below normal increase demand (e.g., for heat pumps).
• Warm and Very warm: Average temperatures 2.5°C and 5°C above normal decrease demand.

3. Gas Availability in Europe
Simulates reduced availability of natural gas for Switzerland and neighboring countries, adjustable to 80%, 60%, 40%, or 20% of the historical average.

4. Availability of French Nuclear Power Plants
Reflects potential reductions in nuclear generation capacity in France:

• Availability can be reduced to 80%, 60%, 40%, or 20% of historical levels.
  For comparison: In the summer of 2022, availability temporarily dropped to around 50%.

5. EU Import Restrictions
Limits Switzerland’s ability to import electricity due to potential EU regulations, such as the 70% rule, which reserves part of grid capacity for internal EU exchanges. The slider specifies the percentage of hours these restrictions apply, ranging from 20% to 80% of all hours.

Using these adjustments, users can test various scenarios to identify vulnerabilities in Switzerland’s energy supply and evaluate possible responses.

The Power Switcher has some important limitations: It does not perform market coupling or simulate electricity price formation. It does not optimize costs, such as deciding between imports or domestic gas plant usage. The focus is on security of supply and levelized cost of electricity.