Can Optimal Dispatch Models Recreate Reality? A Retrospective Analysis of Europe's 2022 Energy Crisis Using PyPSA-Eur

Electricity prices result from the complex interplay of supply and demand, which depends on variable renewable energy production, fuel costs, CO₂ price, and grid bottlenecks.Between 2020 and 2024, COVID-19 shifted demand and disrupted supply chains and operations in Europe, while Russia's invasion of Ukraine constrained gas supply, causing exceptional volatility in gas prices. It remains unclear whether optimal dispatch models can reliably replicate historical hourly prices during crises, given the rapid fluctuations in fuel prices and operators' limited foresight. In this work, we ask whether an optimal dispatch model, parametrised with historical data on demand, fuel, and CO₂ prices, can reproduce the observed market outcomes during this period. We perform hourly hindcasts of electricity generation in 35 countries from 2020 to 2024 using PyPSA-Eur and compare the nodal marginal electricity prices with historical ENTSO-E market prices using the Symmetric Mean Absolute Percentage Error (SMAPE). The scenarios compare static vs dynamic assumptions on fuel and CO₂ prices, as well as perfect foresight vs a two-week rolling-horizon optimization. Combining high-resolution fuel and CO₂ price time series with limited-foresight substantially improves hindcast accuracy, yielding an average SMAPE of 20.76% based on the daily load-weighted average price for the entire Europe. While improvements relative to the scenario with perfect foresight and static price inputs occur, they are most pronounced during periods of high fuel-price volatility, when marginal-cost swings propagate to electricity prices. In 2022, the optimal generation mix in most countries shows substantially less natural gas and more coal than historically observed. Other discrepancies can be attributed to the model's omission of real-world policy interventions, other dispatch constraints, and generator outages.