# A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction

arXiv: https://arxiv.org/abs/2601.17990 · HTML: https://mlsanity.com/paper/2601.17990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting](https://mlsanity.com/paper/2105.09120/) (2021)
- [Spatio-temporal load shifting for truly clean computing](https://mlsanity.com/paper/2405.00036/) (2024)
- [ElectricityEmissions.jl: A Framework for the Comparison of Carbon Intensity Signals](https://mlsanity.com/paper/2411.06560/) (2024)
- [Load Shifting in the Smart Grid: To Participate or Not?](https://mlsanity.com/paper/1509.07963/) (2015)
- [Dynamic Virtual Power Plant Design for Fast Frequency Reserves: Coordinating Hydro and Wind](https://mlsanity.com/paper/2107.03087/) (2021)
- [Dynamic Virtual Power Plant: A New Concept for Grid Integration of Renewable Energy Sources](https://mlsanity.com/paper/2108.00153/) (2021)
- [Control Design of Dynamic Virtual Power Plants: An Adaptive Divide-and-Conquer Approach](https://mlsanity.com/paper/2108.00925/) (2021)
- [Grid-Forming and Spatially Distributed Control Design of Dynamic Virtual Power Plants](https://mlsanity.com/paper/2202.02057/) (2022)

## Abstract (arXiv)

Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid-level average carbon intensity, locational marginal price, or marginal emissions, is challenging due to the lack of detailed counterfactual data required for accurate attribution. This study uses a series of calibrated granular ERCOT day-ahead direct current optimal power flow (DC-OPF) simulations for counterfactual analysis of a broad set of load shaping strategies on grid CO2 emissions and cost of electricity. In terms of annual grid level CO2 emissions reductions, LMP-based shaping outperforms other common strategies, but can be significantly improved upon. Examining the performance of practicable strategies under different grid conditions motivates a more effective load shaping approach: one that "cherry-picks" a daily strategy based on observable grid signals and historical data. The cherry-picking approach to power load shaping is applicable to any large flexible consumer on the electricity grid, such as data centers, distributed energy resources and Virtual Power Plants (VPPs).