Electricity Mix + CO2

1. Introduction

Why these electricity data are relevant

Electricity from the public grid does not have a constant CO₂ intensity. Emission intensity varies significantly over the course of the day, as the generation mix is continuously changing: shares from conventional (fossil) power plants with high CO₂ emissions contrast with fluctuating shares from renewable energy sources with significantly lower emission intensity.

Sustainability is therefore inherently time-dependent: it is not only how much electricity is consumed, but when it is consumed that matters.

In the context of dynamic electricity tariffs and increasing CO₂ pricing, this temporal dimension also gains economic relevance. The objective of an optimized energy concept is thus demand-side load management that deliberately shifts electricity consumption to periods with low CO₂ intensity while avoiding periods with high emission intensity. In practice, this requires appropriate load-shifting strategies as well as the use of energy storage technologies.

Analogous to climate data in the context of Climate Design Days (CDD), this section provides processed, hourly resolved analyses of the electricity mix and its CO₂ intensity. These form the basis for a robust evaluation and optimization of energy concepts in line with Building Design Days + Energy.

Data sources and processing

The underlying data are based on net electricity generation in Germany, provided by Fraunhofer Institute for Solar Energy Systems ISE via its platform energy-charts.info.

These raw data are quality-checked, filtered, and transformed into a structured format to ensure consistent usability for the subsequent analyses and visualizations.

2. Analyses

After login, you gain access to several coordinated layers of analysis, offering different perspectives on the electricity mix, CO₂ intensity, and prices.

2.1 CO₂ Emissions of the Electricity Mix

The focus is on high-resolution temporal analysis of CO₂ intensity [kg/kWh]:

  • Tabular overview
    Compact presentation of CO₂ intensity, including identified extreme values for summer and winter periods.
  • Carpet plot (annual overview)
    Visualization of all 8,760 hours of a year, structured by calendar days (x-axis) and time of day (y-axis). The colour scale directly highlights periods of low and high CO₂ intensity.
  • Annual profile (hourly values)
    Representation of the original data as a continuously fluctuating curve. Additionally, averaged monthly daily values are plotted to reveal typical patterns within the variability.
  • Monthly daily profiles
    Comparison of CO₂ daily patterns across all months, making seasonal shifts in emission minima and maxima clearly visible.
  • Cumulative curve (duration curve)
    Hourly CO₂ values sorted in ascending order and displayed as a cumulative curve. This allows direct assessment of the frequency of specific emission levels.
  • Correlation with outdoor temperature
    Scatter plot with outdoor air temperature (x-axis) and CO₂ intensity (y-axis). Point density indicates frequency and reveals potential systematic relationships between weather conditions and emission intensity.

2.2 Electricity Price (Day-Ahead)

In addition to environmental evaluation, the economic perspective is integrated:

  • Day-ahead electricity prices [€/MWh€]
    Hourly market prices displayed as a carpet plot (original data).

This enables parallel analysis of cost and CO₂ optimization potentials and provides the basis for combined strategies (e.g., cost- and emission-optimized system operation).

2.3 Energy Sources in the Electricity Mix

To contextualize CO₂ intensity, the underlying generation mix is analyzed in detail:

  • Key figures by energy source
    For all relevant categories (e.g., lignite, hard coal, natural gas, nuclear, as well as renewables such as solar, wind, biomass, and hydropower), the following are provided:
    • Annual average electricity production [MWh]
  • Annual carpet plot (generation shares)
    Hourly representation of the shares of conventional and renewable energy sources throughout the year.
    The visualization follows the established 8,760-hour carpet scheme and shows both annual trends and typical daily patterns.

This analysis enables an immediate understanding of which generation types dominate at which times—and thus the root cause of the respective CO₂ intensity.

2.4 Historical Data Analysis (2015–2025)

Long-term time series are available for robust evaluation and trend analysis:

  • Time series analysis of CO₂ intensity
    Representation of developments from 2015 to 2025 as annual curves, including optional trend lines.
  • Zoomable detailed analysis
    Freely selectable time periods with visualization of daily averages, extreme values, and their correlations.
  • Design periods (winter / summer)
    Identification of characteristic load cases:
    • 50 coldest days (winter)
    • 50 hottest days (summer)
    each with corresponding CO₂ intensity based on temperature-correlated evaluation.
  • International comparison data
    Comparable analyses are available for several European countries, including Belgium, Denmark, France, Italy, the Netherlands, Norway, and Austria

Outcome for the User

You are provided with a consistent analytical framework that:

  • makes the temporal variability of CO₂ intensity transparent,
  • reveals the relationships between generation mix, temperature, and price,
  • and delivers concrete starting points for CO₂-optimized energy management.

This section thus forms a central data foundation for the development, evaluation, and optimization of sustainable energy concepts in the context of Building Design Days + Energy.