---
id: VOLT-HOME-WP-081
title: "How much can standardized household profiles save on a dynamic wholesale tariff?"
slug: how-much-can-standardized-household-profiles-save-on-a-dynamic-wholesale-tariff
description: "A transparent upper-bound study of the gross wholesale flexibility available to a standardized synthetic household profile."
published: 2026-08-30
cluster: "Tariff economics and consumer safeguards"
status: measured
evidence: /research-data/home-papers/how-much-can-standardized-household-profiles-save-on-a-dynamic-wholesale-tariff.json
figure: /research-media/home-papers/how-much-can-standardized-household-profiles-save-on-a-dynamic-wholesale-tariff.webp
figure_alt: "Chart for How much can standardized household profiles save on a dynamic wholesale tariff?: annualized gross wholesale flexibility envelope, shown as 10%, 20%, 35%, 50%."
source_ids:
  - dynamic-tariff-viability
  - acer-retail-2025
  - ec-retail-flexibility-2026
  - energy-policy-flexibility
  - iea-demand-flexibility
peer_reviewed: false
---

## Abstract

This working paper estimates an intentionally optimistic gross wholesale flexibility envelope for a standardized synthetic household. The frozen scenario assumes annual electricity use of 4,000 kWh, declares 35% of that energy flexible, and applies wholesale prices directly. Across 93,161 price observations in the public-safe evidence snapshot, the resulting annualized envelope is €353.930457 per synthetic household-year. That monetary result is a synthetic wholesale scenario only. It is not a measured customer saving, a retail-bill estimate, or a promise about any tariff. The calculation excludes taxes, value-added tax, supplier margin, network charges, contract fees, device losses, behavioral constraints, and customer telemetry. It also treats the daily maximum-to-minimum price range as available to the declared flexible share, making the estimate an upper bound rather than an attainable dispatch result. The paper’s contribution is therefore not a retail savings claim. It is a reproducible scale calculation showing how much gross wholesale variation exists before real-world frictions are introduced.

## Plain-language answer

Under the frozen assumptions, the gross annual wholesale-price envelope is about €353.93 for a synthetic 4,000 kWh household with 35% flexible energy. “Envelope” is the important word. The scenario effectively asks what the declared flexible energy would be worth if it could repeatedly capture the full daily difference between the highest and lowest wholesale prices. A real home cannot generally do that. An EV has arrival, departure, charger-power, and energy requirements. A battery has capacity, inverter, state-of-charge, efficiency, and degradation constraints. A heat pump has thermal losses, coefficient-of-performance variation, hot-water needs, and comfort limits. None of those asset constraints is represented here.

The estimate also stops at wholesale energy. It excludes every retail-bill component listed in the evidence: taxes, supplier margin, network charges, VAT, and device losses. A fixed charge would not disappear when a household moves consumption. A time-varying network tariff could favor a different interval from the wholesale market. An inefficient device would need more input energy than an ideal load shift. For these reasons, €353.93 is not a customer saving. It is the measured output of one declared synthetic wholesale scenario and should be used as a ceiling for further analysis, not as a purchasing or tariff decision.

## Research question

The question is narrowly defined: given a standardized 4,000 kWh annual load and a declared 35% flexible share, what gross annual wholesale-price variation is available if the flexible share is valued against observed daily price ranges? This is not a causal question and does not compare customers on fixed and dynamic retail contracts. It does not ask whether a particular household would save money after fees. It asks for the scale of an idealized wholesale opportunity before physical, contractual, and behavioral constraints.

That distinction makes the result useful as a benchmark. Any realistic simulator using the same load must produce less value once it restricts operating windows, applies conversion losses, prices degradation, includes all-in tariffs, or misses intervals because of forecast and automation errors. If a purported retail result exceeds this envelope without adding a separately identified revenue stream, its assumptions deserve scrutiny.

## Data and provenance

The evidence was generated from a SELECT-only production snapshot with no customer telemetry. The registered source contracts are `day_ahead_prices`, `forecast_accuracy`, `bess_index_daily`, `zone_load`, and `zone_holidays`. For this calculation, the operative quantity is the daily day-ahead price range: the maximum price minus the minimum price for each included zone-day. The evidence reports 93,161 observations.

Three windows are frozen in the matching JSON. Daily price aggregates run from 2021-01-01 through 2026-08-29. Detailed intervals run from 2025-10-01 through 2026-08-29. The longer historical boundary runs from 2015-01-01 through 2026-08-29. The publication cutoff is 2026-08-30T00:00:00Z. These windows should not be blended conceptually: the primary metric uses the daily range collection represented in the snapshot, while the other windows describe the wider evidence corpus available to the registered family.

The 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 `caf838c4e433ba99dbb539a4d37226fdf072826cc128de1cdd0f3fd2b9bc6b31` for the JSON and `f6be71e6b54373e20f6f819da10096e9600b46d7c1f9b8bce3f65c9e4ecae8a0` for the WebP figure. The extraction ran in a read-only transaction with a 180-second statement timeout. The five references below provide external policy and research context; they are not the source of the numerical result.

## Method

The method is a declared-profile tariff simulation. First, the analysis computes each observed zone-day’s wholesale range as its maximum day-ahead price less its minimum. Second, it averages those daily ranges. Third, it scales that mean range by annual synthetic consumption and by the flexible share:

`mean daily price range × 4,000 kWh × 0.35 ÷ 1,000`.

Division by 1,000 converts the price basis from EUR/MWh to a kWh-scaled annual amount. The code also expresses the same calculation on a daily basis and then annualizes it, but those factors cancel. The resulting primary metric is the “annualized gross wholesale flexibility envelope.”

The figure varies only the flexible-share assumption, showing 10%, 20%, 35%, and 50%. It does not vary household load, asset type, tariff components, or execution quality. The 35% case is the registered primary scenario. The method gives the flexible energy the full mean daily price range. In practical terms, it acts as if the flexible quantity can be removed from the daily maximum and placed at the daily minimum without duration, power, availability, rebound, or intertemporal constraints.

No device is modeled. There is no battery round-trip efficiency, no battery degradation charge, no inverter limit, and no state-of-charge path. There is no EV charging efficiency, charger-power limit, arrival time, departure deadline, or required terminal charge. There is no heat-pump coefficient of performance, thermal store, building loss rate, or comfort band. Accordingly, the 35% flexible share is an accounting assumption, not the output of an appliance model. The monetary result is a synthetic wholesale scenario before all retail and asset costs.

## Results

The frozen primary result is €353.93045720848846 per synthetic household-year, based on 93,161 observations. Rounded for communication, that is €353.93 per synthetic household-year. Every euro in that sentence refers to the declared gross wholesale scenario. It is not a measured customer saving and not a retail bill reduction.

The matching evidence describes the result as an upper-bound scenario and explicitly says it is not attainable measured savings. That interpretation follows directly from the method: capturing the complete daily range with all declared flexible energy is more permissive than a constrained schedule. The scenario is useful because it puts the opportunity on an annual scale, but it does not tell us what fraction survives automation failures, forecast error, device physics, tariff design, or fees.

The figure values are exactly €101.12298777385388, €202.24597554770776, €353.93045720848846, and €505.61493886926934 for flexible shares of 10%, 20%, 35%, and 50%, respectively. The regenerated evidence reports `bootstrap_95_interval: null` and `interval_method: "not reported for this estimand"`. No uncertainty interval is available for the annualized primary result, so none is claimed.

## Robustness and placebo checks

The first robustness check is the flexible-share grid in the frozen figure. Because the formula is linear in flexible energy, moving from 35% to another declared share changes the envelope proportionally. This tests arithmetic sensitivity, not physical feasibility. A household whose flexible share is estimated from appliance-level constraints needs a separate calculation rather than selecting whichever displayed share gives a preferred result.

The second check is a zero-flexibility placebo. Under the registered formula, a 0% flexible share must produce a zero gross wholesale envelope. This is an identity check on the scaling logic, not an empirical finding. A related invariant-price placebo would also produce zero because every daily maximum would equal its minimum. These placebos clarify that the measured quantity comes from price dispersion interacting with an imposed flexible quantity.

The third check is to compare the idealized envelope with a constraint-aware schedule. Adding one constraint at a time—availability windows, maximum power, energy balance, efficiency, degradation, comfort, forecast information, then retail charges—should weakly reduce the directly comparable gross wholesale value. Such a sequence would show which assumption consumes the envelope. It was not run in this paper, so no retention percentage is claimed.

Within the ten-paper tariff family, the preregistration applies Holm control to inferential claims and calls for blocked day or week bootstrap intervals to preserve serial dependence. This paper is descriptive and makes no unadjusted significance claim. Zone-days are not independent household outcomes, and repeated market observations should not be treated as 93,161 independent customers. The large observation count improves coverage of the price corpus; it does not remove scenario-model risk.

## Limitations

The primary limitation is that the word “save” appears in the registered question while the measured object is a gross wholesale envelope. The analysis observes no customer bills, no customer actions, and no counterfactual retail contracts. It cannot establish that a household saved money or that one retail product outperformed another.

The load profile is standardized only by annual energy. Intraday shape, weekday behavior, seasonality, occupancy, and appliance ownership are absent. The 35% flexible share is fixed rather than estimated. All flexible energy is treated as if it can access the same daily price extremes. This ignores whether the low-price interval occurs while an EV is connected, whether a building can preheat then, or whether a battery has room to charge.

Taxes, VAT, supplier margin, network charges, subscription fees, balancing pass-through, and contractual price adjustments are excluded. A retail tariff may transform wholesale prices, cap them, lag them, or combine them with time-varying network charges. Device losses are also excluded. No asset efficiency or degradation assumption is embedded because no asset is actually simulated.

The calculation averages a multi-zone, multi-regime collection. It is not a forecast for a named bidding zone or year. Historical price dispersion need not persist. The study does not account for households responding simultaneously, so it cannot estimate market feedback or rebound peaks. Finally, the evidence reports no interval for the annualized primary result.

## Practical implication

Use €353.93 only as a synthetic gross wholesale ceiling for the declared 4,000 kWh, 35%-flexible scenario. A useful next step is to build a household-specific lower estimate by starting from the same evidence and subtracting constraints explicitly: actual availability, appliance power, required energy, efficiency, degradation or comfort cost, forecast error, automation reliability, and the complete retail tariff.

This framing makes comparisons auditable. It also prevents a wholesale scenario from being sold as a customer benefit. The practical decision is not “expect €353.93.” It is “measure how much of the declared envelope remains after the household’s real constraints and contract are applied.”

## Reproducibility

Reproduction begins with the matching evidence JSON named in the frontmatter. Verify its paper ID, slug, title, status, assumptions, publication cutoff, source tables, all five provenance hashes, and the evidence-manifest hashes for the JSON and figure. Confirm that the snapshot hash matches the corpus manifest and that the transaction is marked read-only.

From the frozen daily rows, compute `max_price - min_price` for every included zone-day. Take the arithmetic mean. Multiply by 4,000 kWh, multiply by 0.35, and divide by 1,000. The result should reproduce 353.93045720848846 EUR per synthetic household-year under the analysis code identified above. Re-run the same formula with flexible shares 0.10, 0.20, 0.35, and 0.50 to reproduce the figure series.

A reproduction is not equivalent if it silently adds retail taxes, uses customer telemetry, substitutes another load, changes the date cutoff, removes negative prices, or optimizes an actual asset. Those are legitimate extensions, but they require new declared assumptions and should not overwrite this result.

## Disclosure

Analysis and drafting were model-assisted. Sources, code hashes, assumptions, evidence, and limitations are disclosed. This public working paper is not peer reviewed. It is not financial, investment, tariff-selection, purchasing, or trading advice. Volt has no live traders or live capital; C0R is the only paper strategy, and this Home-energy study is not trading research.

All monetary statements in this paper are declared synthetic wholesale scenarios. None is a measured customer saving or a retail-bill result. The external references provide context only; the frozen Volt evidence supplies the numbers.

## 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).