A daily test of carbon-aware electricity pricing is being conducted across Switzerland and various regions of the US. This initiative examines how electricity prices could decrease when the grid operates with lower carbon emissions and increase when emissions are higher. The project measures the potential CO₂ savings compared to standard pricing models.
The study focuses on retail electricity tariffs that adjust according to the grid's carbon intensity, distinguishing it from traditional carbon pricing and carbon-aware computing. It aims to determine how much CO₂ emissions could be reduced if households paid less for electricity during cleaner grid periods.
Data regarding generation mix, load, and wholesale prices are sourced from grid operators such as ENTSO-E, EIA, NESO/Elexon, and CAISO. The carbon intensity is calculated using life-cycle emission factors from the IPCC AR5, with Switzerland's data incorporating consumption-based imports from neighboring countries.
The study analyzes consumer behavior in response to varying electricity prices, using a constant elasticity of substitution model. While total daily consumption remains constant, the timing of electricity use is adjusted to maximize savings during cleaner hours. The hourly tariff is directly linked to carbon intensity, allowing for load shifting towards cleaner periods. However, the design of the tariff may lead to instances where the carbon peak pricing does not always outweigh time-of-use pricing, particularly in solar-heavy grids.
The carbon intensity figures used are averages for each hour, although marginal intensity data is not consistently available across all regions. The effectiveness of the pricing mechanism is quantified in terms of percentage effectiveness and total carbon savings in tonnes per day. The demand model undergoes revisions based on ongoing reviews, with each version clearly documented.
All calculations are based on open data, and the complete hourly records are published alongside the daily results, allowing for independent verification of the figures presented. This project is a continuation of the Carbon-Aware Pricing Simulation master's thesis, utilizing generation-mix data from ENTSO-E and EIA.