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The Optimal Reset-Hour of a Once-Daily Petrol Price Increase Limit

Math & EconomicsEnergy

Key takeaway

A government is considering limiting gas price increases to once per day. Researchers found the best time for the daily price reset is crucial to keeping prices down for drivers throughout the day.

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Quick Explainer

The study examined an optimal timing strategy for a proposed policy that would limit petrol stations in Germany to a single price increase per day. By analyzing consumer demand patterns and simulating price paths under different reset-hour scenarios, the researchers identified an 11:00 reset-hour as the optimal choice. This approach prevented petrol stations from engaging in intraday price discrimination, leading to a flat equilibrium price path throughout the day. The flat price path benefits price-sensitive evening consumers but impacts price-insensitive morning commuters and firms. This conceptual approach of inducing a flat price path was shown to be optimal for the regulator, as it removes the stations' ability to exploit differences in demand composition across hours.

Deep Dive

Technical Deep Dive: The Optimal Reset-Hour of a Once-Daily Petrol Price Increase Limit

Overview

The study examined the optimal timing of a proposed policy that would limit petrol stations in Germany to only one price increase per day. The goal was to determine the reset-hour that would lead to the lowest average price throughout the day.

Problem & Context

  • In March 2026, the German government proposed a policy that would allow petrol stations to increase prices only once per day, while allowing unlimited price decreases.
  • The motivation was to mitigate the impact of rising crude oil prices and petrol-station prices on consumers.
  • The key question was: At what time should the price reset be allowed in order to lower price levels the most throughout the day?

Methodology

  • The study used a simple spatial-competition model to infer the hourly share of price-sensitive consumers (λ_t) from observed petrol station prices.
  • It then evaluated every possible reset-hour (r = 0 to 23) to determine the resulting equilibrium price path under the once-per-day increase constraint.
  • The optimal reset-hour was selected by minimizing the traffic-weighted average price across the day.
  • A theoretical result showed that reset-hours inducing a flat equilibrium price path (i.e., no intraday price discrimination) yield the lowest possible average price.

Data & Experimental Setup

  • Petrol price data: Weekday E5 prices from 15 days in February 2026, sourced from the Tankerkönig data portal.
  • Traffic data: Hourly passenger-car counts on German federal roads in February 2025, from the BASt institute.
  • The analysis focused on weekdays, as commuter demand is less flexible than weekends.

Results

  • The optimal reset-hour was found to be 11:00.
  • This resulted in a flat equilibrium price path throughout the day, with an average price of 180.67 cents/liter.
  • Other reset-hours led to non-flat price paths and higher average prices, up to 180.88 cents/liter.
  • The flat 11:00 reset eliminated the ability of petrol stations to engage in intraday price discrimination.

Interpretation

  • The 11:00 reset prevented stations from lowering prices in the evening to compete for price-sensitive consumers, while also raising prices in the morning to extract more from price-insensitive commuters.
  • This led to a constant intermediate price that benefited price-sensitive evening consumers but harmed price-insensitive morning commuters and firms.
  • The theoretical result showed that inducing a flat price path is optimal for the regulator, as it removes the stations' ability to exploit differences in demand composition across hours.

Limitations & Uncertainties

  • Hourly petrol sale quantities were not available, so passenger-car traffic was used as an imperfect proxy.
  • The model assumed no substitution of purchase timing by consumers under the new price paths.
  • The analysis focused on weekdays; the optimal reset-hour may differ for weekends.

What Comes Next

  • The study could be extended with more precise hourly sales data to refine the quantity weighting.
  • Incorporating station-level heterogeneity and spatial competition could improve predictions for specific locations.
  • Empirical evaluation of the policy's actual effects once implemented would be valuable.

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