  Companies with datacenters are procuring significant amounts of renewable
energy to reduce their carbon footprint. There is increasing interest in
achieving 24/7 Carbon-Free Energy (CFE) matching in electricity usage, aiming
to eliminate all carbon footprints associated with electricity consumption on
an hourly basis. However, the variability of renewable energy resources poses
significant challenges for achieving this goal. We explore the impact of
shifting computing jobs and associated power loads both in time and between
datacenter locations. We develop an optimization model to simulate a network of
geographically distributed datacenters managed by a company leveraging
spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three
signals relevant for informed use of load flexiblity: varying average quality
of renewable energy resources, low correlation between wind power generation
over long distances due to different weather conditions, and lags in solar
radiation peak due to Earth's rotation. We illustrate that the location of
datacenters and the time of year affect which signal drives an effective
load-shaping strategy. The energy procurement and load-shifting decisions based
on informed use of these signals facilitate the resource-efficiency and
cost-effectiveness of clean computing -- the costs of 24/7 CFE are reduced by
1.29$\pm$0.07 EUR/MWh for every additional percentage of flexible load. We
provide practical guidelines on how companies with datacenters can leverage
spatio-temporal load flexibility for truly clean computing. Our results and the
open-source optimization model can also be useful for a broader variety of
companies with flexible loads and an interest in eliminating their carbon
footprint.
