Inspiration

Understanding the electrical grid's fuel mix is critical for assessing carbon intensity, grid reliability, and progress on renewable energy. But raw generation data is hard to read. We wanted to make New York's energy story clear to anyone, not just grid analysts.

What it does

Our project is an exploratory data analysis and visualization suite for the New York Independent System Operator (NYISO) fuel mix from 2023 through 2025. It breaks monthly generation into three groups:

  • Fossil fuels: natural gas, dual fuel, and other fossil
  • Carbon-free: nuclear and hydro
  • Renewables: wind, solar, biomass, and other clean sources

It answers three questions:

  1. What share of the grid does each energy source supply over the full period?
  2. How does the mix shift month to month and season to season as heating and cooling demand changes?
  3. How are clean and carbon-free sources tracking year over year compared to fossil sources?

The visualizations include:

  • Overall pie chart: the aggregate fuel mix across all three years
  • Interactive monthly stacked bar chart: a dropdown toggles between total output (TWh) and percentage share across 36 months
  • Interactive yearly stacked bar chart: compares 2023, 2024, and 2025 with on-bar labels to show the transition trend

How we built it

We processed and cleaned NYISO's monthly generation data (nyiso_fuel_mix_monthly_2023_2025.csv, in MWh) in a notebook, then built static and interactive visualizations on top of it. We also made a presentation and website in Figma to share the findings.

Challenges we ran into

Thinking about what we needed to show the judges

Accomplishments that we're proud of

We are proud of displaying all of our findings in Figma, something the group had little experience in.We're also proud of turning dense grid data into charts that are interactive and easy to read.

What we learned

Nuclear gas is generated the most throughout NY from the past few years

What's next

We'd like to extend the analysis to more years, add emissions estimates, and connect the fuel mix to demand and weather data.

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