Speaker
Description
The geographic scope of a reduced-scope electricity market model, how many zones it represents, and which ones, is a basic design choice. It is also a costly one: data preparation and simulation runtime scale sharply with the number of zones included, and can rise by several orders of magnitude when a full continental model is simulated in place of a single-zone one. Yet how this design choice propagates into simulated outcomes has not been systematically examined. This study provides the first such test, specifically asking whether the resulting scope sensitivity is driven by the number of zones simulated or by which zones are included, using a PowerACE campaign spanning connected-zone subsets (N = 1–48), seven bidding strategies, and three weather years. In total, more than 8,000 simulations are conducted, ranging from short runs of a few minutes to long runs of several days. Zone count has a statistically significant effect on simulated prices but explains only 30–60 % of scenario-to-scenario variance at fixed N. Classifying each scenario by the target zone's real, physically connected neighbours (Tier 1 of a graph constructed from simulated interconnector flows) recovers most of this variance on its own; including neighbours-of-neighbours or more distant zones adds little further explanatory power. The test applied here is deliberately conservative: complexity-matched linear and quadratic specifications, evaluated by cross-validated out-of-sample R², and required to meet the campaign's own precision target. Under this test, connectivity outperforms zone count in 54 of 70 statistically reliable cells (77 %), by a median margin of 20.2 percentage points of explained variance among the cells it wins. This advantage is not uniform. It is unanimous for well-connected zones (Germany, 12 real neighbours: 0 of 5 reliable cells reversed), but considerably less reliable at the periphery of the network, where zones with up to five real neighbours show reversals in 32–38 % of cells. Most reversals are attributable to a linear specification's failure to capture genuine curvature in the Tier-1 relationship, as confirmed by cross-validated quadratic terms. The single exception, Portugal, reflects a genuine but structurally narrow economic effect. The variable relevant to interpreting a reduced-scope model is therefore a target zone's real network embeddedness, rather than the model's overall size. The reliability of this diagnostic itself depends on how embedded the zone already is. A second outcome variable, intraday price variance, exhibits the same pattern, with an even stronger connectivity advantage (54 of 62 reliable cells, 87 %), indicating that the result is not specific to the average price level.