Fix tiny typos

README.md

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 ### Abstract
 
-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
+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 flexibility: 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±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.
 
 ### How to reproduce results from the paper?

manuscript/sections/introduction.tex

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 % The Problem**
 \textbf{The problem --}
-Companies aiming for 24/7 clean electricity have a unique challenge regarding load-shifting. Because they rely on their own portfolio of carbon-free energy generators instead of buying electricity from a local grid, grid signals such as average carbon emissions or locational electricity prices no longer play a significant role for informed load-shifting. This effect was illustrated with the help of energy system modelling in the previous work of the authours \cite{riepin-zenodo-systemlevel247,riepinMeansCostsSystemlevel2023}. In this context, the following question arise: \textit{In pursuit of achieving 24/7 Carbon-Free Energy objectives, what signals should datacenter operators focus on to facilitate informed and effective load shifting across space and time?}
+Companies aiming for 24/7 clean electricity have a unique challenge regarding load-shifting. Because they rely on their own portfolio of carbon-free energy generators instead of buying electricity from a local grid, grid signals such as average carbon emissions or locational electricity prices no longer play a significant role for informed load-shifting. This effect was illustrated with the help of energy system modelling in the previous work of the authors \cite{riepin-zenodo-systemlevel247,riepinMeansCostsSystemlevel2023}. In this context, the following question arise: \textit{In pursuit of achieving 24/7 Carbon-Free Energy objectives, what signals should datacenter operators focus on to facilitate informed and effective load shifting across space and time?}
 
 % 6. **Relevance of this paper**
 \textbf{Contribution --} This is the first paper to examine the role of space-time load shifting in the context of 24/7 carbon-free energy matching.