consumes 19% less energy per event in high performance mode

%. This shows, that the impact of load shaping heavily depends on the power proportionality53 of the underlying hardware, and that it is not a reasonable measure per se.

Although solar forecasts are not very promising this time, it again permits to discharge to 30%, as the carbon intensity during the next day is expected to be especially low. Instead of drawing carbon-intensive grid energy at night, the demand is thereby shifted to the next morning where the batteries are charged to 60% using cleaner energy.

Although the carbon-aware experiment uses 2.4 % more energy than the baseline (which is because not all power modes have the same energy-efficiency), its associated carbon emissions through grid power consumption are 35.9 % lower. In the following, we will briefly analyze the two experiments and to demonstrate how our integration enables research and development of carbon-aware applications.

The control unit adaptively adjusts the battery's minimum state of charge and grid charge rate over time. In particular, in case of promising forecasts for solar power production or low carbon intensity, it is able to temporarily deplete the battery to 30%.

Note: The physical node's power usage is controlled via DVFS, while the virtual node uses rate limiting on the executed process.