US electricity rates rose 15% to 53% since 2020 depending on where you live - 20 major utilities compared [OC] Visualization
![US electricity rates rose 15% to 53% since 2020 depending on where you live - 20 major utilities compared [OC] Visualization](/api/images/reddit-maps/1shyv9q_1775865602054.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "US electricity rates rose 15% to 53% since 2020 depending on where you live - 20 major utilities compared [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on I pulled EIA residential electricity price data (Table 5.6.A of the EIA Electric Power Monthly) across 20 of the largest US utilities to see how much rates have actually changed since 2020. The variation is wild.
**What's in the chart:** % rate increase 2020-2024, current rate in cents/kWh, current average monthly bill, and how many extra dollars per month customers are paying versus 2020.
**What's driving the patterns:** **New England (+48-53%) is the most extreme case** and it's almost entirely a natural gas pipeline story. New England generates most of its electricity from gas but has barely any pipeline capacity to import it during peak winter demand. When gas prices spiked in 2021-2022, the region had nowhere to turn - Eversource and National Grid customers absorbed the full hit. PG&E customers are paying $67/mo more than in 2020 mostly for a different reason: billions in wildfire liability and underground line-burial costs, recovered from ratepayers over 20-30 years. **California (+38-50%) is a wildfire infrastructure story.** PG&E and SCE are in the middle of decades-long programs to harden the grid against fire ignition. These capital costs are approved by the CPUC and recovered from ratepayers over 20-30 years - so this is baked in for a long time regardless of what gas prices do. **The South and Texas (+15-22%) held up best.** Lower renewable transition pressure, less wildfire exposure, and generally less aggressive state clean energy mandates. The tradeoff is fewer rebate programs and less grid modernization investment. **The rate-per-kWh gap is the underappreciated number:** PG&E customers pay 33¢/kWh while Austin Energy customers pay 11¢/kWh - a 3x multiplier on the economics of everything. A heat pump, solar array, or EV that pays back in 6 years in Northern California would take 18 years in Austin with the same equipment.
**Notes on methodology:**
* EIA Table 5.6.A reports state-level residential averages, not utility-level. For utilities serving multiple states (Duke Energy, Dominion, National Grid), the figures are a weighted approximation across their service territory, not a precise utility-level figure. Utility-specific data would require pulling individual rate case filings from each state PUC, which are not centrally aggregated. * "All-in" means supply charge + delivery/distribution charge combined, which is what actually appears on a residential bill. * Bill increase figures are derived: if 2024 avg bill = $X and the increase was Y%, the 2020 baseline = $X / (1 + Y/100).
**Source:** EIA Electric Power Monthly, Table 5.6.A - Average Retail Price of Electricity by State (eia.gov/electricity/monthly). Chart built with React.
I put together [EcoAudit](https://ecoaudit.app/) to run energy analysis at the household level - it factors in your specific utility, local rebates, and home characteristics to show actual payback periods rather than national averages that don't apply to most people's situation., which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.
The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.
Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...