--- id: VOLT-HOME-WP-087 title: "Do EVs, batteries, and heat pumps complement or cannibalize one another?" slug: do-evs-batteries-and-heat-pumps-complement-or-cannibalize-one-another description: "A transparent flexible-load ceiling scenario showing overlap among abstract EV, battery, and heat-pump wholesale opportunities." published: 2026-08-30 cluster: "Tariff economics and consumer safeguards" status: measured evidence: /research-data/home-papers/do-evs-batteries-and-heat-pumps-complement-or-cannibalize-one-another.json figure: /research-media/home-papers/do-evs-batteries-and-heat-pumps-complement-or-cannibalize-one-another.webp figure_alt: "Chart for Do EVs, batteries, and heat pumps complement or cannibalize one another?: asset-stacking cannibalization under a 65% flexible-load ceiling, shown as EV, battery, heat pump, combined." source_ids: - dynamic-tariff-viability - acer-retail-2025 - ec-retail-flexibility-2026 - energy-policy-flexibility - iea-demand-flexibility peer_reviewed: false --- ## Abstract Household technologies can share the same low-price intervals, so adding their standalone flexibility values may double-count one wholesale opportunity. This working paper measures that overlap with a declared ceiling scenario rather than a physical multi-asset optimizer. It assigns abstract flexible shares of 15% to an EV, 25% to a battery, and 35% to a heat pump. Their standalone shares sum to 75%, while combined flexibility is capped at 65%. Applied to the mean observed daily wholesale price range, the resulting cannibalization metric is 25.28074694346344 EUR/MWh-equivalent across 93,161 observations. This is a synthetic wholesale scenario, not a customer saving, retail bill, or measured device outcome. The analysis contains no charger limits, driving needs, battery capacity, round-trip efficiency, degradation, state of charge, heat-pump COP, building physics, hot-water demand, comfort constraint, taxes, network charges, or customer telemetry. It establishes that the imposed ceiling creates overlap by construction. It does not determine whether real EVs, batteries, and heat pumps complement or cannibalize one another in operation. ## Plain-language answer In the frozen scenario, the three asset labels cannibalize part of one another’s wholesale opportunity. Their declared standalone flexible shares are 15%, 25%, and 35%, which total 75%. The combined scenario allows at most 65%. The ten-percentage-point difference is valued against the mean daily wholesale range, producing 25.28074694346344 EUR/MWh-equivalent of overlap. That result is not measured from devices. The “EV,” “battery,” and “heat pump” labels stand for shares of the same price-range opportunity. A real EV might be available overnight, a heat pump might shift around morning and evening comfort needs, and a battery might bridge intervals unavailable to either load. Those differences can create complementarity. They can also intensify competition if all three seek the same cheapest quarter-hours. The current calculation has no clock or operating state, so it cannot tell which effect dominates. Every monetary quantity is a declared synthetic wholesale scenario. It is never a customer saving or retail bill. The honest answer is therefore: the registered ceiling demonstrates arithmetic cannibalization, but actual technological complementarity remains unmeasured. ## Research question The research question asks whether stacking an EV, home battery, and heat pump creates additive flexibility value. The implemented question is narrower: how much standalone wholesale-range value is lost when three declared flexible shares totaling 75% are constrained by a 65% combined ceiling? “Cannibalization” means the difference between the sum of three standalone proxy values and one capped combined proxy value. It does not mean one physical device damages another or reduces customer welfare. Complementarity would require asset-specific schedules and constraints that allow one technology to use opportunities another cannot. Those mechanisms are outside this calculation. ## Data and provenance The public evidence comes from a frozen SELECT-only production snapshot and contains no customer telemetry. The tariff family registers `day_ahead_prices`, `forecast_accuracy`, `bess_index_daily`, `zone_load`, and `zone_holidays`. This result uses daily maximum-minus-minimum day-ahead price ranges. The evidence reports 93,161 observations. Daily prices span 2021-01-01 through 2026-08-29. Detailed intervals in the corpus span 2025-10-01 through 2026-08-29, and the long-history boundary spans 2015-01-01 through 2026-08-29. The publication cutoff is 2026-08-30T00:00:00Z. Despite the availability of a detailed-interval window in the corpus, the registered metric is based on daily ranges and abstract shares, not interval-by-interval co-optimization. The snapshot hash is `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 `8932385fd5e782d1d8b579e728e6315dc9536d1dc444ee6e035f5d2af88d799f` for the JSON and `f81d67f84d01a21ef4a2a4293d6d2f7e9de6f7c187ad7a77873a2144f307c773` for the WebP figure. Extraction was read-only with a 180-second statement timeout. The five references provide context on household flexibility and distributional questions; they do not provide the asset-share assumptions or result. ## Method For every included zone-day, the analysis calculates the difference between the maximum and minimum day-ahead wholesale prices. It takes the arithmetic mean of these daily ranges. Three standalone wholesale-signal proxies are then defined: - EV: `mean daily range × 0.15`. - Battery: `mean daily range × 0.25`. - Heat pump: `mean daily range × 0.35`. The combined proxy is: `mean daily range × min(0.15 + 0.25 + 0.35, 0.65)`. Cannibalization is the sum of the three standalone proxies minus the combined proxy. Because the standalone shares total 0.75 and the ceiling is 0.65, the calculation prices a 0.10 overlap against the mean daily range. The ceiling is an assumption, not a result inferred from household data. The family also declares 4,000 kWh annual synthetic load, 35% flexible energy, wholesale pass-through, and no customer telemetry. Annual load does not enter the EUR/MWh-equivalent metric. The asset-specific shares include a 35% heat-pump proxy and should not be confused with a measured overall household flexible share. No physical dispatch model is present. The battery has no kWh capacity, kW inverter, state-of-charge path, charging or discharging efficiency, round-trip efficiency, or degradation cost. The EV has no battery size, charger power, charging efficiency, arrival, departure, trip energy, or minimum charge. The heat pump has no COP curve, thermal store, building loss coefficient, weather, hot-water demand, or comfort band. Taxes, network charges, supplier margin, VAT, and device losses are excluded. Every euro-based value is a synthetic wholesale scenario. ## Results The primary result is 25.28074694346344 EUR/MWh-equivalent of asset-stacking cannibalization under the 65% ceiling. Rounded, the registered scenario loses 25.28 EUR/MWh-equivalent relative to summing all standalone proxies. The result uses 93,161 observations. The metric is positive because the cap is lower than the sum of the declared shares. It therefore demonstrates overlap by construction. It does not establish that market data independently revealed cannibalization, nor does it estimate net annual household value. There is no 4,000 kWh scaling in the primary result. The figure values are exactly 37.9211204151952, 63.20186735865867, 88.48261430212213, and 164.32485513251254 EUR/MWh-equivalent for the EV, battery, heat-pump, and combined proxies. The regenerated evidence reports `bootstrap_95_interval: null` and `interval_method: "not reported for this estimand"`, so no confidence interval is claimed for the cannibalization metric. No asset-specific standalone euro result is reported as a customer saving. The figure bars are components of a synthetic wholesale range allocation, and their labels should not be interpreted as measured EV, battery, or heat-pump performance. ## Robustness and placebo checks An additive placebo sets the combined ceiling to at least 75%. The sum then fits under the ceiling and the formula’s cannibalization becomes zero. This verifies that the primary result comes from the imposed cap rather than an emergent interaction in the data. A tighter-ceiling sensitivity would increase the calculated overlap, while a looser ceiling would reduce it. A preregistered grid of ceilings can illustrate this arithmetic. It cannot identify the correct ceiling, which requires a household load and device model. A physical robustness test should optimize all three technologies jointly and separately on the same interval prices. The joint model must preserve household energy balance and expose every device assumption. Complementarity is present if the joint feasible set allows opportunities unavailable to isolated devices; cannibalization is present when they compete for the same load, connection, export, or price intervals. The present daily-range method cannot observe either mechanism. Technology-specific placebos are also necessary. Setting battery efficiency to an ideal value and degradation to zero gives an upper bound; realistic efficiency and degradation should reduce battery cycling value. Removing EV availability outside its connection window should reduce EV flexibility. Tightening heat-pump comfort should reduce thermal shifting. None of those tests was run here, so no physical robustness claim is made. The family protocol uses Holm correction for inferential claims and blocked day or week bootstrap methods for serial dependence. This output is a deterministic scenario calculation and makes no significance claim. ## Limitations The largest limitation is construct validity. Three percentages are labeled as technologies, but the calculation does not contain technologies. It contains one mean price range, three multipliers, and a ceiling. The result is informative about double-counting in scenario arithmetic, not appliance behavior. The ceiling is assumed at 65%. There is no empirical estimate showing that a household with the declared technologies can move exactly that share, or that its standalone shares are 15%, 25%, and 35%. The 4,000 kWh annual family assumption is not used in the metric, and no base load shape is present. Asset and efficiency assumptions are absent rather than favorable. Battery round-trip loss and degradation are not zero-valued inputs; they are unmodeled. EV charging losses and mobility requirements are unmodeled. Heat-pump COP, rebound, thermal leakage, and comfort are unmodeled. Grid import limits, export compensation, and simultaneous power constraints are also absent. Taxes, VAT, network charges, supplier margin, fixed fees, and device losses are excluded. A time-varying network tariff could make assets seek different intervals or the same interval. The wholesale-only result cannot resolve that. Daily ranges discard timing and duration. The minimum might last one interval, while the assets need several hours. Price windows and device availability may vary by season. Historical pooled zone-days are not one household’s future opportunity. Finally, the evidence reports no interval for the primary difference. ## Practical implication Do not add standalone EV, battery, and heat-pump “savings” estimates without checking whether they monetize the same intervals or the same flexible kWh. A joint optimization should be the default for a multi-asset home. Use 25.28074694346344 EUR/MWh-equivalent only as a synthetic warning about the registered 75%-versus-65% accounting overlap. A decision-grade analysis needs device physics, efficiency, battery degradation, comfort, mobility, power limits, complete tariffs, and automation reliability. Its monetary outputs must remain labeled synthetic scenarios unless validated against real bills. ## Reproducibility Verify the frontmatter evidence JSON against the ID, title, slug, assumptions, status, cutoff, source tables, all five provenance hashes, and the evidence-manifest hashes for the JSON and figure. Confirm that the transaction was read-only and that the snapshot hash matches the manifest. Compute maximum-minus-minimum for each frozen daily price record and take the arithmetic mean. Multiply that mean by 0.15, 0.25, and 0.35 for the standalone proxies. Multiply it by `min(0.75, 0.65)` for the combined proxy. Subtract the combined proxy from the standalone sum. The output should equal 25.28074694346344 EUR/MWh-equivalent. Adding interval timing, device physics, annual load, efficiency, degradation, retail components, or a different ceiling creates a new scenario. It must not be called a reproduction of this result. ## Disclosure Analysis and drafting were model-assisted. This public working paper is not peer reviewed. Its abstract asset shares, ceiling, evidence hashes, omitted physics, and retail exclusions are disclosed. It is not financial, investment, purchasing, tariff-selection, or trading advice. Every monetary result is a declared synthetic wholesale scenario, never a measured customer saving or retail bill. Volt has no live traders or live capital; C0R is the only paper strategy. This Home-energy paper is not trading research, and no trading or capital action follows from it. ## 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).