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Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions --- equivalent to a drop of 23.3% in our electric grid emissions.
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This content will become publicly available on March 1, 2026
Toward Carbon-Aware Data Transfers
The growing adoption of cloud, edge, and distributed computing, as well as the rise in the use of AI/ML workloads, have created a significant need to measure, monitor, and reduce the carbon emissions associated with these resource-intensive tasks. One significant but often overlooked source of emissions is data transfers over wide-area networks (WANs), primarily due to the challenges in accurately measuring the carbon footprint of end-to-end network paths. We introduce a novel mechanism to measure network carbon footprints and propose strategies for optimizing the scheduling of network-intensive tasks. We show that users can achieve significant carbon savings by shifting data transfer tasks across time and geographic regions based on local carbon intensity.
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- Award ID(s):
- 2313061
- PAR ID:
- 10658024
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Internet Computing
- Volume:
- 29
- Issue:
- 2
- ISSN:
- 1089-7801
- Page Range / eLocation ID:
- 19 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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