Emission-intensity-informed load regulation for grid decarbonization

Cell Reports Sustainability (2025), first author

Carbon-aware grid
How much do the emissions attributed to electricity consumption fall when load is re-timed according to hourly average grid emission intensity across U.S. regions, and which strategies suit which types of grid?

First author

  • 12 U.S. grid regions
  • Hourly EIA data, 2023
  • Mixed-integer load regulation
  • Month, quarter, year windows
  • Three case studies

Research question

How much do the emissions attributed to electricity consumption fall when load is re-timed according to hourly average grid emission intensity across U.S. regions, and which strategies suit which types of grid?

Key insight

Shifting load toward cleaner hours cuts attributed emissions only modestly

Because total use in each window cannot fall, emissions drop only through the gap in intensity between hours, and the allowed range limits how much load can move. In the reported scenarios, reductions were on the order of one to two percent of regional totals or less, largest with longer windows and wider ranges in renewable-rich, diverse grids.

How much can re-timing load cut emissions?

Each hour of the grid has an average emission intensity. The model moves part of the load from high-intensity hours to low-intensity hours in the same window, within an allowed range, and keeps total use unchanged. Attributed emissions fall by the load moved times the intensity gap between those hours. Because the range is limited, the reduction is modest; it grows with a wider range and a longer window.

Illustrative model with stylized numbers: it shows the mechanism described in the paper, not the study's data or results.

My contribution

First author. I did most of the work: the methodology, data processing, optimization modeling, analysis and writing.

How I approached it

  1. ChallengeGrid regions differ in renewable share, generation mix and intensity swings, so one strategy cannot fit all.

    What I didI helped group the regions by renewable penetration, generation diversity and intensity fluctuation, and tested one strategy dimension on each: window length, adjustment range and adjustment penalty.

    Why it matteredThis showed which strategies suit which types of grid, the question the study set out to answer.

  2. ChallengeRe-timing load inconveniences users, which an emissions-only objective ignores.

    What I didI helped add a penalty on each adjusted hour, counted with a binary variable, and varied its weight.

    Why it matteredIt revealed that regions with low intensity fluctuation are more sensitive to the cost of regulating load.

Design choices

Re-time load without reducing total use
Alternatives considered: also allowing demand reductionEmissions then fall only because load moves to cleaner hours, so the study isolates the value of timing.

Method

Mixed-integer load-regulation model solved for each optimization cycle (month, quarter or year): hourly load moves toward lower-intensity hours within an allowed adjustment range, without lowering the cycle's total consumption, and each adjusted hour carries a penalty. Applied to U.S. grid regions with EIA hourly data (2023), varying the cycle, the adjustment range and the penalty, and grouping regions by renewable penetration, generation diversity and intensity fluctuation.

Key findings

  1. Longer optimization windows and wider adjustment ranges increase the reduction in attributed emissions, most in renewable-rich and diverse grids.
  2. In the reported scenarios, reductions in attributed emissions are modest, on the order of one to two percent of regional totals or less.
  3. Regions with low intensity fluctuation are more sensitive to the cost of regulating load.

Boundary conditions

Reductions are computed under average-emission-intensity accounting with perfect foresight; they are not estimates of the consequential (marginal) system response.

Publications

  1. Cell Rep. Sustain.
    Real-time greenhouse gas emission intensity informed demand-side load regulation for power grid decarbonization
    H. Li, G. Shen, S. Senemmar, A. Mehmani, J. Zhang
    Cell Reports Sustainability, vol. 2, article 100367, 2025.
    DOI PDF Code News Cited by 10

Citation counts: Google Scholar, October 30, 2026.

  • Demand-side management under real-time greenhouse gas emission factor for electricity — 2024 ACEEE Summer Study on Energy Efficiency in Buildings (second author).