Consumers and policymakers have raised concerns about the recent rise in electricity bills across the United States. While some have pointed to increased power use by data centers as a primary cause, a new study from Lawrence Berkeley National Laboratory offers a more nuanced analysis of factors influencing state-level retail electricity prices.
The research, published in The Electricity Journal, uses data from the U.S. Energy Information Administration along with information on state policies and other relevant factors. The study examines trends in inflation-adjusted electricity prices from 2019 to 2024, focusing on elements such as changes in electric consumption, utility-scale wind and solar production, renewable energy mandates, distributed solar power, and natural gas prices.
The findings show that national average electricity prices generally kept pace with inflation over the five-year period. However, this overall stability masked differences at the state level: real prices dropped in 31 states but rose in 17 others. Residential rates were somewhat higher after accounting for inflation, while commercial and industrial rates declined.
A notable result of the analysis is that increasing energy consumption within a state correlated with lower retail electricity rates. Specifically, each 10 percent rise in energy use was associated with a reduction of about 0.6 cents per kilowatt-hour. The authors link this trend to infrastructure costs being spread across more customers as usage grows.
The study did not find a simple relationship between renewable energy deployment and customer rates. States with strong renewable policies or significant growth in distributed resources like rooftop solar often saw rate increases; however, those with large increases in renewable generation did not necessarily experience higher customer bills. In some cases, rate hikes were attributed to recovery costs from natural disasters or efforts to mitigate future risks rather than changes tied directly to renewables.
States relying heavily on natural gas faced substantial fuel cost spikes during 2022 and 2023 due to external events such as the Russian invasion of Ukraine.
The authors acknowledge several limitations of their work: “Statistical power is limited, and findings reflect broad averages rather than utility specifics. Prices are also shaped by varied cost‑recovery rules and lags across states and utilities.” They add that using five-year snapshots may miss shorter- or longer-term effects among other constraints.
They note that newer methods such as difference-in-differences or regression discontinuity designs could better establish causal relationships between policy shifts or market shocks and price changes: “While careful analysis and judgment are critical, new tools have opened promising avenues for identifying causal effects in real-world data.”
Future research could apply advanced statistical techniques like polynomial distributed lag models or dynamic panel regressions to better understand both short- and long-term impacts on electricity pricing trends.
The authors conclude: “Overall, the paper is careful, clear, and candid about what the study design can and cannot do. The associations found between policies and prices are of great interest.” They emphasize that more sophisticated analyses focusing on particular regions are needed for clearer guidance to policymakers since their current work does not measure potential benefits alongside observed costs.
