| --- |
| title: Utility Efficiency & Rates |
| emoji: ⚡ |
| colorFrom: blue |
| colorTo: yellow |
| sdk: streamlit |
| sdk_version: "1.56.0" |
| app_file: streamlit_app.py |
| pinned: false |
| --- |
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| # Electricity Utility Fairness Residential Rate Analysis |
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| > **Research Question:** Are system-level inefficiencies — high energy losses and poor load factors — statistically correlated with higher retail rates for residential consumers? |
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| ## Overview |
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| This project investigates a fundamental fairness question in the U.S. electricity sector: do residential customers end up paying more when their utility operates inefficiently? Using the [CORGIS Electricity Dataset](https://corgis-edu.github.io/corgis/), this analysis builds a set of derived efficiency and equity metrics, then examines their statistical relationships through a suite of interactive visualizations. |
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| New York State serves as the primary case study. NY was selected through a data-driven ranking process (get_state_variance) that scores all 50 states across five analytical criteria: number of utilities, number of ownership types, residential price standard deviation, maximum system loss percentage, and industrial revenue dependency. New York ranks at or near the top on all five: it has over 100 utilities across 6 distinct ownership models, exhibits high residential price variance, and sits within one of the most actively scrutinized regulatory environments in the U.S. |
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| [](https://your-app-url.streamlit.app) |
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| --- |
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| ## Key Metrics |
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| | Metric | Description | |
| |---|---| |
| | `System Loss Percentage` | Energy lost in transmission/distribution as % of total supply | |
| | `Load Factor` | Ratio of actual energy delivered to theoretical maximum (demand efficiency) | |
| | `Residential Unit Price` | Residential rate in $/MWh | |
| | `Industrial Unit Price` | Industrial rate in $/MWh | |
| | `Price Spread` | Gap between residential and industrial rates (equity indicator) | |
| --- |
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| ## Visualizations |
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| - **State Selection Table** — Ranking of top 10 states by analytical richness; justifies NY case study |
| - **Price Spread Strip Plot** — Residential premium over industrial rates by ownership model |
| - **Correlation Heatmap** — Statistical significance matrix across all key metrics |
| - **Fairness Audit Scatter** — System loss (%) and load factor (dual y-axis) vs. residential price by ownership model, with OLS trendline |
| - **Rate Disparity Dumbbell** — Top 10 utilities by residential–industrial price gap |
| - **Energy Flow Sankey Diagram (State)** — Per-state (or utility) breakdown of energy sources and uses as percentages |
| - **Energy Flow Sankey Diagram (US)** — National aggregate energy flow |
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| --- |
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| ## Project Structure |
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| ``` |
| . |
| ├── utility_efficiency_fairness.ipynb |
| ├── streamlit_app.py |
| ├── requirements.txt |
| ├── README.md |
| ├── data/ |
| │ └── electricity.py |
| │ └── app.py |
| │ └── electricity.data |
| │ └── electricity.data |
| ├── src/ |
| │ └── util/ |
| ``` |
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| The three modules work as a clean pipeline: |
| - **`electricity`** — CORGIS data loader (unmodified third-party) |
| - **`data_util`** — all data preparation, filtering, and metric engineering |
| - **`plot_util`** — all chart construction and SVG expor |
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| ### `util.py` — Helper Functions |
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| | Function | Purpose | |
| |---|---| |
| | `prepare_data(df)` | Calculates key metrics and other features from raw columns | |
| | `get_state_data(state, df)` | Filters and subsets data for a given state | |
| | `get_state_variance(df)` | Ranks states by residential price variance (for state selection) | |
| | `get_customer_utilities(df, customer)` | Filters utilities by customer type served | |
| | `get_residential_load_factor(df)` | Subset with valid load factor for residential utilities | |
| | `get_residential_sys_loss(df)` | Subset with valid loss % for residential utilities | |
| | `get_utility_usage(utility)` | Converts raw MWh values to % of total supply for Sankey | |
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| --- |
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| ## Setup & Usage |
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| - The dataset is pre-bundled — no external downloads required for it. |
| - Run `jupytr notebook utility_efficiency_fairness.ipynb.` |
| - Run `pip install pandas plotly scipy kaleido streamlit` |
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| --- |
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| ## Data Source |
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| **[CORGIS Electricity Dataset](https://corgis-edu.github.io/corgis/python/electricity/)** — a cleaned and structured snapshot of U.S. Energy Information Administration (EIA) [Form 861](https://www.eia.gov/electricity/data/eia861/) data, covering utility-level electricity generation, sales, revenues, and customer counts across all U.S. states. |
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| ## Potential Extensions |
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| - Expand the analysis to all 50 states and compare regional patterns |
| - Incorporate time-series data to track changes in efficiency and pricing |
| - Apply regression modeling to control for utility size and customer density |
| - Explore the role of renewable energy mix on system loss rates |
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