--- id: VOLT-HOME-WP-088 title: "How much annual flexibility value comes from the best ten days?" slug: how-much-annual-flexibility-value-comes-from-the-best-ten-days description: "A concentration diagnostic for the ten largest zone-day wholesale price ranges in the frozen tariff-economics sample." published: 2026-08-30 cluster: "Tariff economics and consumer safeguards" status: measured evidence: /research-data/home-papers/how-much-annual-flexibility-value-comes-from-the-best-ten-days.json figure: /research-media/home-papers/how-much-annual-flexibility-value-comes-from-the-best-ten-days.webp figure_alt: "Chart for How much annual flexibility value comes from the best ten days?: share of annualized flexibility range in the best ten zone-days, shown as best ten, remainder." source_ids: - dynamic-tariff-viability - acer-retail-2025 - ec-retail-flexibility-2026 - energy-policy-flexibility - iea-demand-flexibility peer_reviewed: false --- ## Abstract Concentrated flexibility opportunities can make annual averages fragile: missing a few unusual days may erase a disproportionate share of modeled value. This working paper measures concentration in the frozen wholesale-price corpus by sorting all observed daily maximum-minus-minimum ranges and dividing the sum of the ten largest zone-day ranges by the sum of all 93,161 ranges. The registered share is 0.6360174222054445%. That is a pooled-sample concentration statistic, not the share of one household’s annual saving and not ten calendar days selected within each year. The family declares a 4,000 kWh synthetic household and 35% flexible energy, but neither quantity enters this percentage. No schedule, retail tariff, device efficiency, degradation, comfort, customer telemetry, or automation record is used. Any monetary interpretation would be a declared synthetic wholesale scenario, never a measured customer saving or retail bill. The result indicates low concentration in ten observations relative to the entire pooled denominator, while leaving open whether the best ten days within an individual zone-year are more consequential. ## Plain-language answer The ten largest zone-day price ranges account for 0.6360174222054445% of the sum of every daily range in the pooled sample. Rounded, that is 0.636%. Because the denominator contains 93,161 observations across markets and dates, the result should not be read as “the best ten days make up 0.636% of a household’s year.” The distinction changes the answer. Ten observations out of a very large multi-zone, multi-year pool can have a small share even if the ten best days inside one zone-year matter greatly. The registered metric does not group by household, zone, or calendar year before ranking. It therefore measures corpus-wide concentration, not annual household concentration. No euro saving is measured. The underlying quantities are wholesale daily ranges. Converting them to money using 4,000 kWh and 35% flexibility would still create a synthetic wholesale scenario and would not include taxes, network charges, supplier margin, VAT, device losses, or operating constraints. The plain answer is: concentration in the global top ten is 0.636%, but the title’s household-annual interpretation requires a different grouped analysis. ## Research question The broad research question asks whether modeled flexibility value depends on a small number of exceptional days. The implemented estimand asks what fraction of the pooled sum of daily wholesale price ranges is contributed by the ten largest zone-day ranges. This is a concentration question, not a causal or predictive one. “Best” means largest observed daily maximum-minus-minimum wholesale range. It does not mean a household could use that range, that an optimizer predicted it, or that the day produced a retail saving. “Annual” appears in the registered title and metric name, but the implementation does not first form annual groups. This paper preserves that limitation rather than inventing an annual result. ## Data and provenance The evidence comes from a frozen, SELECT-only production snapshot containing no customer telemetry. The registered family contracts are `day_ahead_prices`, `forecast_accuracy`, `bess_index_daily`, `zone_load`, and `zone_holidays`. The primary metric uses the daily day-ahead price records’ maximum and minimum values. The evidence sample size is 93,161. Daily prices cover 2021-01-01 through 2026-08-29. The corpus also records detailed intervals from 2025-10-01 through 2026-08-29 and a long-history boundary from 2015-01-01 through 2026-08-29. The publication cutoff is 2026-08-30T00:00:00Z. A zone-day is the observational unit in the primary concentration calculation. The frozen snapshot is identified by SHA-256 `7e97489fc8528c8cc8c38830e05b48d949ce1f67b98575dff26f5d7c321e4c67`. The protocol hash is `adb36bf6b447af9f96339249b8becaefc20422499cca1977242866347a97bd4b`, the paper-registry hash is `7bcb91d7476d0a69fe9fa75a5c7782f8117e0153f82f9112b7e1d307d3943717`, the source-registry hash is `07949550ac443ff673fda5c0209b99f137544f3ffecf6775f109bb9d09663bd6`, and the analysis-code hash is `57c57de79cdab2b5b6d6c54c485cb5162598c5ba0b0bfe995da40d75e6c52ba9`. The evidence-manifest hashes are `064be603c35597784016a7045bbd5b1ce6e80c6ecc4ca4bde45645411452cce7` for the JSON and `5d78eff13edd0f6a790716a9a5f94ae388b1bc4c253892a810b197614a36c4de` for the WebP figure. Extraction was performed in a read-only transaction with a 180-second statement timeout. The five references provide consumer-flexibility context and are not sources for the concentration percentage. ## Method For each included zone-day, the analysis calculates: `daily maximum wholesale price - daily minimum wholesale price`. It sorts all resulting ranges from largest to smallest. It sums the first ten values, sums all 93,161 values, divides the top-ten sum by the total, and multiplies by 100. The figure contrasts the resulting “best ten” share with the remainder. The operation is performed once over the pooled corpus. It does not select ten days per year, per zone, or per synthetic household. A single local date represented in multiple zones can contribute multiple zone-days. Conversely, an extreme day in a smaller or shorter-covered market receives the same ranking treatment as any other observation. The family assumptions state 4,000 kWh annual synthetic household load, 35% flexible energy, wholesale pass-through, and no customer telemetry. The first two factors cancel from a concentration ratio if applied uniformly to every observation, which is why they are absent from the implemented percentage. They would not cancel if flexible energy or load varied by day, but that case is not modeled. No asset schedule is used. Battery efficiency, degradation, capacity, and power are absent. EV charger power, efficiency, arrival, and departure are absent. Heat-pump COP, thermal state, and comfort are absent. Taxes, VAT, supplier margin, network charges, and device losses are excluded. If daily ranges were converted to euros, the output would remain a synthetic wholesale scenario, not customer savings. ## Results The frozen primary result is 0.6360174222054445% of the pooled flexibility-range sum in the ten largest zone-days. The metric uses 93,161 observations. The small percentage indicates that no set of just ten observations dominates the entire pooled denominator. It does not establish that opportunity is evenly distributed through each year. The denominator spans many zone-days, so even large outliers can have limited global weight. The evidence’s supplied interpretation says a concentrated opportunity set makes annual averages sensitive to automation reliability on a few days. That mechanism is reasonable in general, but this specific value does not by itself demonstrate strong annual concentration. A proper annual test would compute the top-ten share within each zone-year and summarize those shares. The figure divides the pooled total into exactly 0.6360174222054445% for the best ten zone-days and 99.36398257779456% for the remainder. The regenerated evidence reports `bootstrap_95_interval: null` and `interval_method: "not reported for this estimand"`, so it supplies no uncertainty interval for the concentration ratio. No monetary result is reported. Any conversion of the top-ten ranges to euros would be a declared synthetic wholesale scenario only, never a measured customer saving or retail bill. ## Robustness and placebo checks An equal-range placebo gives every zone-day the same positive range. The top-ten share then depends only on ten observations divided by the total number of observations. This establishes the minimum concentration expected from a perfectly uniform nonzero sample of the same size and makes clear why the denominator count matters. A one-outlier stress assigns nearly all aggregate range to one observation. The top-ten share approaches 100%. This verifies that the metric can detect extreme global concentration. Neither placebo is an empirical finding about the frozen market data. The essential robustness analysis is grouped concentration. Repeat the ranking separately for each zone-year, select the ten largest local days, and calculate their share of that zone-year’s total range. Report the median and dispersion across complete zone-years. A second specification could use the best ten dates across a fixed household’s local market. These analyses were not run, so no annual concentration value is claimed. An opportunity-use robustness check should replace raw daily range with value from a feasible schedule. A one-interval price spike is not fully usable by a multi-hour EV charge or thermal load. Battery power, energy, efficiency, and degradation may also change which days rank highest. The present result is a market-range diagnostic. The preregistered family uses Holm correction for inferential claims and blocked day or week bootstrap methods to preserve serial dependence. This paper presents a descriptive ratio and does not make a significance claim. ## Limitations The title refers to annual flexibility value, while the implementation pools the entire sample. This mismatch is the primary limitation. The result cannot answer how much of a particular household-year’s opportunity comes from its best ten days. The ranking unit is a zone-day, not necessarily a unique calendar date. Cross-zone market shocks may create several large observations on one date. The result does not report which zones or dates occupy the top ten, and this paper does not invent them. The raw daily range is an upper-bound market signal. It ignores duration and timing. Flexible load may be unavailable at the minimum or unable to avoid the maximum. The 4,000 kWh and 35% assumptions are not used in the ratio, and no daily load profile appears. No physical asset is modeled. Efficiency and degradation are unmodeled, not assumed to be zero. Likewise, no comfort, mobility, state-of-charge, or power constraint appears. Taxes, VAT, network charges, supplier margin, fixed fees, and device losses are excluded. Automation reliability is not observed. Although missing a high-value day can matter, this paper does not simulate missed notifications or controller failures. Historical pooled concentration may also change in future regimes. The evidence reports no interval for the primary percentage. ## Practical implication Do not design automation around a corpus-wide top-ten statistic. For operational planning, calculate concentration within the household’s bidding zone and a complete annual period, then use a feasible device schedule rather than the full daily range. The 0.6360174222054445% value is useful as a precise diagnostic of the frozen pooled sample. It says the global top ten do not dominate all 93,161 ranges. It does not support an annual savings claim. If a grouped analysis later finds concentration, reliability testing should focus on those high-value windows while still valuing every result as a synthetic scenario until compared with actual bills. ## Reproducibility Verify the evidence JSON referenced in the frontmatter: ID, title, slug, status, assumptions, source tables, cutoff, all five provenance hashes, and the evidence-manifest hashes for the JSON and figure. Confirm the read-only transaction and manifest snapshot match. For every frozen daily record, subtract `min_price` from `max_price`. Sort descending. Sum the first ten ranges, divide by the sum of all ranges, and multiply by 100. The result should reproduce 0.6360174222054445%. Do not group by year or zone and call the output a reproduction; that would answer the more literal annual question and should receive a new evidence record. Likewise, replacing raw ranges with feasible dispatch value, changing the cutoff, or weighting by load creates a new study. ## Disclosure Analysis and drafting were model-assisted. This public working paper is not peer reviewed. It discloses the pooled denominator, unit of observation, evidence hashes, and mismatch between the broad title and narrow metric. It is not financial, tariff-selection, investment, purchasing, or trading advice. No customer saving or retail bill is measured. Any monetary extension would be a declared synthetic wholesale scenario. Volt has no live traders or live capital; C0R is the only paper strategy, and this Home-energy concentration diagnostic is not trading research. ## References 1. Advances in Applied Energy, [Assessing the conditions for economic viability of dynamic electricity retail tariffs for households](https://doi.org/10.1016/j.adapen.2024.100174). 2. ACER and CEER, [Rewarding flexibility: How retail contract choice can help unlock consumer flexibility](https://www.ceer.eu/wp-content/uploads/2025/11/ACER-CEER-2025-Retail-monitoring.pdf). 3. European Commission, [Communication from the Commission on the Citizens Energy Package](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52026DC0115). 4. Energy Policy, [Welfare redistribution through flexibility — Who pays?](https://doi.org/10.1016/j.enpol.2025.114684). 5. International Energy Agency, [Scaling Up Demand Flexibility](https://www.iea.org/reports/scaling-up-demand-flexibility).