Germany Methodology

Modo Energy provides benchmark data for battery energy storage systems across global energy markets, applying a standardized methodology to ensure consistency and transparency across all produced Benchmarks.

1. Introduction

The Modo Energy Germany Methodology Framework outlines the calculation and construction of Modo Energy's battery energy storage Benchmarks in Germany. This document provides detailed insights into:

• the assumptions that define a representative asset;
• the components that make up published revenues;
• the optimisation model that simulates asset dispatch; and
• the data inputs and transformations that feed the simulation

to ensure accurate and reliable benchmarking for Germany's battery energy storage sector.

1.1 Why Germany requires a simulated benchmark

The ME vBESS DE Benchmarks differ from Modo Energy's GB Index in one fundamental respect: they are simulated, not measured.

In Great Britain, utility-scale batteries are registered as Balancing Mechanism Units (BMUs). BMU status places them under strict data-sharing obligations, so Modo Energy can compute a BMU-level revenue aggregate from publicly reported physical notifications, bid-offer acceptances, ancillary contracts, and settlement metered volumes. The GB Index reflects what a representative portfolio of real assets did earn from the available data.

Germany has no equivalent asset-level data regime. Battery operational activity in the wholesale and ancillary markets is not publicly reported per asset.

To produce a benchmark under these conditions, Modo Energy simulates the dispatch of a representative battery asset against publicly available market data, using our Global Dispatch Model — a mixed-integer linear programme (MILP) that optimises battery revenues subject to market participation rules.

1.2 What the ME vBESS DE Benchmarks represent

The ME vBESS DE Benchmark family represents the simulated revenue performance of grid-scale lithium-ion battery energy storage systems in Germany. To capture differences in technical characteristics, the Benchmark family includes variants grouped by system duration. This allows revenues to be compared across different types of utility-scale battery systems. The Benchmarks are constructed to reflect revenues that a representative asset could earn.

1.3 Industry-wide Benchmarks and Custom Benchmarks

This methodology covers two families of benchmark:

• The industry-wide ME vBESS DE Benchmarks — the three published benchmarks (1H, 2H, 4H) set out in §2.1, built on the representative asset specification in §2.2.
• Custom Benchmarks — benchmarks built to a customer's own asset specification. A Custom Benchmark is produced by the same model, from the same input data, under the same market rules, and with the same calibration as the published Benchmarks. The only difference is that a defined set of asset and grid-connection parameters can be changed from their representative values (§2.7).

Every part of this document applies to both families unless the text says otherwise. Where a rule applies to only one of them, it is stated explicitly.

1.4 Definitions

2. Benchmark Construction

2.1 The ME vBESS DE Benchmarks

Modo Energy currently publishes a family of three industry-wide benchmarks for Germany:

All three benchmarks are constructed using the same published methodology, with differences only in the duration parameter of the representative asset.

2.2 Representative BESS specification for the ME vBESS DE Benchmarks

The representative asset underpinning the industry-wide Benchmarks is specified as follows. Custom Benchmarks start from this specification and vary the parameters listed in §2.7.

2.3 Benchmark calculation methodology

The Benchmark is calculated by:

1. Running Modo Energy’s Global Dispatch Model over the assessed period, using the applicable asset specification — §2.2 for the industry-wide Benchmarks, or the customer specification under §2.7 for a Custom Benchmark — and the input data sources in §4.2.
2. Computing the simulated revenue of the asset across each modelled market and direction, in EUR, at 15-minute resolution.
3. Summing revenues per settlement period (15 minutes) and normalising by the asset's rated power (50 MW for the ME vBESS DE Benchmarks, or the customer-specified rated power for a Custom Benchmark) to obtain a Benchmark value in EUR/MW per 15-minute settlement period.
4. Aggregating these values into monthly, quarterly, and annual benchmarks as described in §2.4.

The calculation follows a clearly defined, rule-based methodology, with a percentage calibration factor to account for imperfect foresight, asset availability and execution as a discretionary adjustment (see §5.9).

2.4 Benchmark value representation

All Benchmark values are reported as revenues per unit of rated power, net of charging energy costs and before operating costs (§3.2). Revenue and Benchmark values can be represented using the following units:

• EUR/MW (period): total Benchmark revenue for the assessed period (e.g. EUR/MW/month, EUR/MW/year).
• EUR/MW/year (annualised): total Benchmark revenue for the assessed period divided by the number of days in the period and multiplied by 365.

2.5 Publication cadence and revisions

Both the industry-wide ME vBESS DE Benchmarks and Custom Benchmarks are updated daily. Modo Energy ingests source data from all markets listed in §4.2 on a daily cadence and publishes Benchmark values for delivery day D-2 every calendar day. The two-day publication lag allows settlement, late submissions, and backfills on aFRR activated energy and other late-arriving data series to complete before the Benchmark for that day is computed.

Previously published Benchmark values may be revised if upstream data providers issue corrected values for periods already covered by a published Benchmark, if additional source data becomes available that materially improves the accuracy of a historical Benchmark value, or if a methodology change under §7 is applied with retrospective effect. Revised values replace the previously published values (§7.4).

2.6 Market evolution reflected in the Benchmark

The Benchmark reflects each market structure only for periods in which its native mechanism and data were in force. The key milestones are:

2.7 Custom Benchmarks: configurable parameters

Custom Benchmarks use the same methodology as the industry-wide ME vBESS DE Benchmarks, with a customer-specified asset. Custom Benchmarks use the same input data (§4.2), the same three-step optimisation sequence (§5.3), the same market rules and physical constraints (§5.4 to §5.8), and the same calibration factor (§5.9) as the industry-wide Benchmarks.

The parameters below can be customised within the ranges shown:

3. Revenue Components

3.1 Revenue components included in the Benchmarks

The ME vBESS DE Benchmarks capture the primary revenue opportunities available to a representative BESS in Germany's wholesale and ancillary markets. The table below details each component, the market from which it is modelled, and the price signal used.

3.2 Excluded revenues

Revenues from the following sources are excluded from the ME vBESS DE Benchmarks:

• Imbalance Penalties: Positions are assumed fully closed out in the intraday continuous market, reBAP price is not applied.
• Redispatch 2.0 / curtailment compensation: asset and site-specific; not modelled.
• Grid fees, network charges, and levies (e.g. Netzentgelte, KWKG, StromNEV §19): site-specific; default to zero in the representative asset since there is an exemption till 2029. If BNetzA grid fee reform introduces a BESS-applicable, publicly cleared fee schedule, grid fees may be made available in Custom Benchmarks.
• Capacity mechanism payments: Germany's capacity mechanism is in implementation. Capacity market revenues may be added through the methodology change process (§7) once the market clears and prices are published.
• Bilateral contracts (PPAs, tolls, floors): Not modelled as they don’t affect the optimal battery dispatch dynamics apart from in some co-located cases.
• mFRR (manual Frequency Restoration Reserve) capacity awards and activated energy payments are not currently modelled. They may be considered in a future methodology version if BESS participation in mFRR becomes significant.
• Battery operator fees, warranty costs, and O&M: not deducted. The Benchmark reports market revenue net of charging costs and before operating costs.

4. Data Inputs and Use of Discretion

4.1 Data visibility and quality

The ME vBESS DE Benchmarks are constructed exclusively from publicly available market data, with the exception of the representative asset in §2.2 and the parameters below, which are set by Modo Energy rather than observed. The table lists each parameter with its value, the reason for it and the section that defines it. Modo Energy's market owner for Germany sets and maintains both (§4.3).

For a Custom Benchmark, the parameters in §2.7 are set by the customer within the ranges shown. All other parameters are the same as for the ME vBESS DE Benchmarks.

4.2 Data inputs and sources

The ME vBESS DE Benchmarks are constructed from publicly available market data, sourced from the providers listed below. The parameters applied to these inputs are listed in §4.1 and described in §5.

4.3 Use of discretion and consistent judgment

Modo Energy applies discretion strictly within predefined parameters to ensure consistency and transparency. The Benchmark is rule-based. The parameters in §4.1 apply in the same way to every day and every duration, and no value is set by hand for a single day. Judgment is used only when a parameter is chosen or changed. Any change is reviewed internally before release and tested by the automated regression tests in §8.1, and follows the change process in §7.3. Sources are selected based on reliability, relevance to BESS operations, and the activity, depth and transparency of the underlying market.

5. Modelling Methodology

5.1 Overview of the Global Dispatch Model

The ME vBESS DE Benchmarks are produced by simulating the dispatch of the asset described in §2.2, or of the customer specification under §2.7 in the case of a Custom Benchmark, against historical market prices using Modo Energy's Global Dispatch Model (GDM) — the same proprietary optimisation framework that underpins Modo Energy's benchmarks in other regions. For each day in the assessed period, the GDM determines how the representative asset would have scheduled its power across the markets in §3.1 in order to maximise revenue, given the physical limits of the battery and the rules governing each market.

5.2 What the model is solving for

At its core, the GDM answers a single practical question: given the prices observed on a given day and the physical and regulatory limits of the asset, what is the highest revenue a well-run battery could have earned?

To answer this, the model allocates the battery's power and stored energy across three families of market opportunities:

• Energy markets — day-ahead, intraday auction, and intraday continuous.
• Ancillary capacity markets — FCR and aFRR, in which the battery is paid to hold capacity available for the TSO.
• Ancillary activated energy — aFRR activated energy, where the battery is additionally paid for the energy it actually delivers when called.

Revenue is the sum of what the battery earns from these streams, net of the charging energy cost. The model also applies a set of internal consistency rules that prevent it from scheduling trades that appear profitable on paper but cannot be executed by a physical battery (§5.4).

5.3 Three-step optimisation sequence

A real BESS trader in Germany does not make all decisions at the same moment. Capacity products are procured the day before delivery, day-ahead energy clears shortly after, intraday auctions clear at fixed points through the day, and the intraday continuous markets run up to near real time. Each market gate closes before the next, and traders have to commit at each gate using only the information available at that point.

The model reflects this by solving the dispatch problem in three sequential steps:

1. Day-ahead step — sets capacity commitments (FCR and aFRR) and day-ahead energy positions.
2. Intraday Auction step — rebalances those positions through intraday trading.
3. Intraday Continuous step — determines the final rebalancing in ID3 and aFRR activated-energy positions.

Each step takes the positions committed in earlier steps as fixed: once a decision is locked in, the model cannot unwind it retroactively. This sequencing is what makes the simulation directionally realistic rather than a single idealised all-markets optimisation.

5.3.1 Day-ahead step

• Granularity: 60 minutes before 2025-10-01; 15 minutes from 2025-10-01 onward (reflecting the SDAC transition to a 15-minute day-ahead auction).
• Foresight: perfect foresight of day-ahead energy prices, FCR capacity prices, and aFRR capacity prices over the full day.
• Markets optimised: day-ahead energy, FCR capacity, aFRR capacity.

5.3.2 Intraday Auction step

• Granularity: 15 minutes.
• Foresight: perfect foresight of intraday auction prices across the full day.
• Markets optimised: intraday auction (IDA2) only.
• Commitments carried in: day-ahead energy positions, FCR capacity, aFRR capacity from Step 1 are fixed.

5.3.3 Intraday Continuous step

• Granularity: 15 minutes.
• Rolling horizon: 2-hour windows, advanced sequentially.
• Foresight: imperfect price foresight — the underlying ID3 continuous-intraday and aFRRE activated energy series are replaced by a centred 2-hour rolling mean, representing a trader's blended view of near-term prices without perfect look-ahead.
• Markets optimised: intraday continuous (ID3) energy, aFRR activated energy (aFRRE).
• Commitments carried in: all prior steps' positions are fixed.

5.4 Market stacking and physical limits

Germany's market design permits a battery to hold simultaneous positions across day-ahead energy, intraday energy, FCR capacity, aFRR capacity, and aFRR energy — subject to physical limits on the battery and the grid connection. The model enforces stacking via the following families of constraints:

1. Sufficient SOC for ancillary delivery — the battery's state of charge at each timestep must be enough to deliver all simultaneously held discharging-direction ancillary products over their product-specific delivery horizons (FCR 30min, aFRR 60min, aFRRE 15min). These horizons are derived from the energy reservoir requirements set out in the German TSO prequalification conditions.
2. aFRR vs aFRRE double-counting prevention — aFRR (capacity reservation) and aFRRE (energy activation) are distinct products. The model applies a pair of parallel constraints so that each physical limit (grid, battery, SOC) is checked - once including aFRR, and once including aFRRE, but never double-counted in a single inequality. Reserved aFRR capacity has to bid into aFRRE but isn't necessarily activated, but cannot bid into other energy markets.

5.5 Battery physical model

The representative asset is modelled as a single lumped battery whose behaviour is governed by four technical characteristics:

• Round-trip efficiency of 88%. Efficiency losses are applied to the charging flow — 1 MWh drawn from the grid stores 0.88 MWh of usable energy.
• Continuous state of charge. The battery's stored energy in each timestep equals the previous timestep's level plus charging (net of efficiency) minus discharging. Expected energy flows from activated ancillary products (aFRRE and FCR) are added to these flows using actual activation ratios, so the model accounts for the SOC movement caused by actually delivering on held ancillary capacity.
• State of charge boundaries. To allow for day-by-day solving of the model, SOC is assumed to be 50% at midnight of each day. This is to allow for participation in ancillary markets in the first 4h block of the day, in line with SOC requirements for FCR and aFRR positive.
• Cycling limit per day. Total discharge throughput per day is capped at the daily cycling limit times the battery's usable capacity. Both wholesale-market discharge and ancillary activation count toward this limit, reflecting that warranties are typically written on total throughput rather than on the market into which the energy was sold.

Cell-level degradation is intentionally disabled in the published Benchmarks (§2.2) to keep the asset's duration constant throughout the full history, preserving comparability across years.

5.6 Ancillary services: capacity vs activated energy

Germany's balancing stack distinguishes between reserving capacity (capacity payments) and being called to deliver energy (activation payments). The model treats these as two separate but coupled products, bid into different gates:

• Capacity markets (FCR, aFRR): the battery is paid by the TSO for holding capacity available, with the bid set in the day-ahead step. Revenue accrues at the cleared capacity price (EUR/MW/h) over each delivery block. Capacity held reduces the power and SOC available for energy-market participation. Activation in FCR is not compensated, and held aFRR capacity is modelled with zero assumed energy throughput — holding aFRR does not by itself drain SOC or count toward cycling, so the capacity payment is the only revenue stream from aFRR.
• Activated-energy market (aFRRE): a separate 15-minute product, bid in the intraday continuous step. The battery is paid (or pays) at the activation price (EUR/MWh) for energy actually delivered when called, which moves SOC and counts toward the daily cycling limit.

aFRRE activation volumes are capped at actual historical system needs. Because aFRRE is only valuable if activated, the held aFRRE volume in a given settlement period is capped at the system aFRRE activation ratio for that direction multiplied by the battery's rated power:

The system aFRRE activation ratio is the volume of aFRRE that the German TSOs activated in that quarter-hour for direction d (positive or negative), divided by the aFRR capacity demand, computed from the Netztransparenz activation series and the Regelleistung demand series. The battery, as a price-taker, is assumed to always be activated to this ratio if it decides to bid - if 20% of the aFRR reserve is activated, up to 20% of the battery can be activated in aFRRE.

The cap is scaled to the battery's rated power, not to the aFRR capacity already won at day-ahead: aFRR (capacity) and aFRRE (energy) are bid and priced independently, and batteries can participate in aFRRE with more capacity than they have bid in the aFRR capacity market. Only one direction of aFRRE can be held in any given settlement period.

5.7 FCR-specific rules

• Symmetric product: German FCR regulation requires that positive (upward) and negative (downward) capacity be contracted in equal volumes.
• Symmetric derating: the total (positive + negative) held FCR capacity is capped at 80% of the battery's rated power, consistent with the pre-qualification rules.
• 150 MW per-asset cap: consistent with regelleistung.net regulatory rules, no single BESS in the model can hold more than 150 MW of FCR capacity at any time. The representative 50 MW asset is well below this bound.

5.8 aFRR capacity price ceiling (bid discipline)

The raw aFRR capacity price published by regelleistung.net is the volume-weighted average of all awarded bids. Because aFRR capacity in Germany is procured under a pay-as-bid mechanism, the average published price can be sharply distorted during scarcity events. In periods such as May, June, and September 2025, a handful of very high marginal bids were accepted and pulled the reported average well above the price that a typical BESS operator would actually bid at, as bidding high risks losing the capacity to cheaper bids and earning zero revenue.

To reflect this BESS-specific bid discipline, the model clips the aFRR capacity price at an upper bound of 50 EUR/MW/h before it enters the optimisation. The ceiling is set at approximately the P95 of the series' historical distribution. This means the cap binds only on extreme tail observations, roughly 5% of 4-hour blocks. The ceiling may be updated through the methodology change process (§7).

5.9 Revenue calibration

To bridge the gap between theoretical maximum and what a real BESS fleet can earn, we apply a calibration factor that can be interpreted as “percent of perfect” (PoP). Three residual gaps remain between real market conditions and a simulated revenue model:

• Foresight gap — within the day-ahead and intraday steps, the model sees intra-window prices that a real trader does not know at gate closure.
• Availability gap — modern assets are available ~98% of the time. Scheduled and unscheduled outages, maintenance windows, prequalification re-tests, and other downtime all reduce the hours in which a battery can actually earn revenue.
• Execution gap — residual trading frictions and slippage not already reflected in the above

To bridge this gap in a transparent, single-parameter way, the Benchmark applies a flat calibration factor of 0.80 to all simulated per-timestep revenues:

The 0.80 factor is anchored to observed fleet capture in Great Britain, where BMU-level disclosures allow a direct comparison between perfect-foresight modelled revenues (from the same Modo Energy Global Dispatch Model, using GB historical prices) and realised asset revenues. It is applied as a single scalar rather than a market-by-market calibration to preserve the internal revenue ratios produced by the optimisation.

The factor may be updated through the methodology change process (§7).

The same calibration factor is applied to Custom Benchmarks.

5.10 Additional constraints in Custom Benchmarks

The three constraint families below are available only in Custom Benchmarks. None is active in the industry-wide Benchmarks, where the representative asset ramps instantaneously and faces no ancillary restrictions or time-varying grid limits (§2.2).

5.10.1 Ramp time

Flexible Connection Agreements may limit how quickly a battery can change its power output. The model applies this as a fixed rate: the battery moves between power levels at a constant speed, so the time a move takes is proportional to its size.

The ramp time entered for a Custom Benchmark is the time needed to cover the full range from zero to rated power. A 100 MW battery with a 30-minute ramp time therefore takes the full 30 minutes to travel from standstill to rated power, and around seven minutes to move by 25 MW.

Ramp limits apply to energy market positions alone. They constrain the asset's day-ahead and intraday volumes, not the activations in ancillary services. This reflects how assets connected at the TSO level behave in practice: ancillary response is delivered against the TSO's signal rather than as a scheduled energy position, so it is not held to the ramp restrictions in the connection agreement. Where ramps are applied to ancillaries at the DSO level, they mainly limit the capacity that can be bid into ancillary services — for aFRR, bids are limited to whatever the asset can ramp to within 5 minutes in line with prequalification requirements, so a 10-minute ramp time would limit aFRR participation to 50%. This is entered separately, through the ancillary service limits in §5.10.2.

Because the asset cannot change power instantly, it delivers less over a settlement period than the level it is aiming for. The model therefore settles the asset on the average power achieved across each period rather than the level targeted, so the energy given up while ramping is reflected in revenues. As the rate is fixed, a ramp takes only as long as the size of the move requires, and the asset holds its target for the remainder of the period: large moves spend more of the period ramping and give up proportionally more of the target, while small moves finish ramping early and deliver close to the full amount.

5.10.2 Ancillary service limits

Ancillary participation in a Custom Benchmark can be restricted in three ways: by excluding ancillary markets, by a cap on each market individually, and by a per-timestep cap on all ancillary markets combined.

Both cap types are entered as the share of rated power that is eligible for ancillary services. A connection agreement permitting 30% of the asset in aFRR is therefore entered as 30%. Entering 100% applies no restriction; entering 0% removes the asset from that market entirely.

• Market-by-market caps are set separately for FCR, aFRR positive, and aFRR negative. Each caps the capacity held in that market alone, with aFRR restrictions applying to both capacity bookings and energy activation.
• The combined cap limits the total capacity held across all ancillary products in any single settlement period. It is applied independently to the charging and discharging directions.

Where both are set, both are enforced simultaneously and the more binding of the two governs in each period. A combined cap of 30% of rated power eligible, sitting on top of individual caps, means the model can stack individual ancillary markets only up to that 30% ceiling.

These caps sit on top of the market rules that apply to every asset, and cannot be used to relax them. FCR remains derated to 80% of rated power, the 150 MW per-asset FCR cap still binds (§5.7), and the state-of-charge energy reservoir requirements for ancillary delivery still apply (§5.4).

Worked example. A 100 MW asset has the aFRR positive cap entered as 30% eligible and the combined cap entered as 30% eligible. It can hold at most 30 MW of aFRR positive. If it holds that 30 MW, the combined cap is already fully used, so no FCR can be added on top in that period. The remaining 70 MW stays available for wholesale energy dispatch, subject to the grid constraints.

5.10.3 Grid curtailment profile

Flexible Connection Agreements may also limit how much a battery can import from or export to the grid at certain times. For example, an agreement may not allow any discharging between 09:00 and 16:00 in summer. A Custom Benchmark can reflect these limits through an uploaded grid curtailment profile.

The profile sets import and export limits in MW for each period of one year. It is applied to every year of the Custom Benchmark's history, so it suits agreements whose limits follow the same pattern each year. In leap years, 29 February takes the limits of 28 February.

In every settlement period, the model holds import and export within the limits in the profile. Where the profile and the grid import and export capacity in §2.7 differ, the lower limit applies. The limits apply to the grid connection as a whole, so they also cap the capacity the asset can hold for FCR and aFRR (§5.4).

The profile is fixed. It cannot reflect curtailment that changes from day to day, such as live curtailment instructions, unavailability, or limits linked to local wind and solar output.

6. Governance and Compliance

Modo Energy is committed to transparency by providing detailed explanations of calculation methodologies, revenue components, and benchmark updates. All key elements of the methodology are publicly available, ensuring stakeholders can fully understand the benchmark's structure and operation. Transparency measures include:

• Publication of methodology documents outlining calculation processes and revenue components.
• Historical data updates to maintain accuracy and consistency.
• Advance notice of material methodology changes in line with §7.3.
• A contact point for questions about the methodology: team@modoenergy.com.

7. Methodology changes

7.1 Model versioning: the Stable Version

The published industry-wide Benchmarks and all Custom Benchmarks are computed using a single designated version of the Global Dispatch Model (the "Stable Version"), regardless of when a benchmark was created. Modo Energy updates the Stable Version no more than once per calendar quarter. Market structure changes outside Modo Energy's control (e.g. new products, gate-closure or granularity changes) may require an update ahead of this cadence.

7.2 Review and update process

The methodology is reviewed when market rules, market conditions or data availability change (for example, the European transition to 15-minute day-ahead auctions), and when inputs or results look out of line. Any change that follows goes through §7.3.

Direct back-testing against observed transaction data is not possible because per-asset revenue is not publicly disclosed in Germany. Instead, Modo Energy applies a calibration factor of 0.80 (§5.9), anchored to observed fleet capture in Great Britain, where both perfect-foresight modelled revenues and realised asset earnings are observable.

7.3 Material and non-material changes

Changes to this methodology are classified by their effect on Benchmark values.

Non-material changes do not change Benchmark values. They include corrections and clarifications to this document. They may be made at any time without prior notice and are disclosed as soon as practicable.

Stable Version updates change Benchmark values. Modo Energy updates the Stable Version no more than once per calendar quarter, except as set out in §7.1.

A Stable Version update is material where the cumulative movement in the Benchmark across all Stable Version updates in that calendar quarter would exceed ±2%.

Material updates are announced with not less than 30 days' written notice before implementation, together with an impact analysis.

Emergency changes needed to protect the integrity of the Benchmark or to comply with law may be made without prior notice, with notice given as promptly as circumstances permit.

Revisions to published values when source data is corrected (§2.5) are not methodology changes.

All methodology changes are recorded in Appendix I to maintain historical comparability and transparency.

7.4 Recalculation and result versions

When a Stable Version update is implemented, Benchmark values for the preceding 365 days are recalculated under the new Stable Version. Only the current version of results is available at any time.

8. Consistency and continuity

8.1 Quality assurance

Modo Energy uses these checks:

• Continuous automated validation checks to identify discrepancies in input data.
• Automated regression tests on every change to the Global Dispatch Model.

8.2 Data integrity

Data integrity is maintained through:

• Secure data management protocols, including access controls and regular backups.
• Clear traceability from raw source (regelleistung.net, ENTSO-E, EPEX, Nordpool, netztransparenz.de) into the model feed.

8.3 Late, missing or distorted data

The Benchmark for a delivery day is published only once the inputs in §4.2 are available for that day; the two-day lag in §2.5 allows for this.

Each input has a single source, and no alternative source is substituted. Where a data issue is found, Modo Energy may check it with the upstream provider.

Inputs are used as published, including extreme prices. The only adjustment is the aFRR capacity price ceiling, which limits the effect of a few very high accepted bids (§5.8).

If a source corrects data for a period already published, the Benchmark for that period may be recalculated (§2.5).

8.4 Traceability and verification

Modo Energy ensures all benchmark calculations are fully traceable and verifiable through:

• Maintaining comprehensive records of input data, Stable Version, and calculation outputs.
• Reproducibility: every published Benchmark value can be reproduced from the archived input data and the Stable Version in use at the time of publication. Only the current version of results is published.
• Public disclosure of material methodology changes.

9. Limitations

The Benchmarks are simulations. These are their main limits:

Appendix I — Methodology changes

Methodology changes since first publication will be tracked here. Version numbers correspond to Stable Version updates of the Global Dispatch Model (§7.1).

Clarification updates

• August 2026 — Custom Benchmarks added. Customer-specified assets can be benchmarked under the same methodology, with the configurable parameters in §2.7 and the ramp time and ancillary limits in §5.10. The published industry-wide Benchmarks are unaffected.
• October 2026 — Grid curtailment profiles added to Custom Benchmarks. Customer-specified assets can include time-varying import and export limits through an uploaded profile (§5.10.3). The ME vBESS DE Benchmarks are unaffected.
• October 2026 — Ancillary service limits in Custom Benchmarks are now entered as the share of rated power that is eligible, rather than ineligible (§2.7, §5.10.2). Results are unchanged, and existing Custom Benchmarks are unaffected.
• October 2026 — Clarifications. This update aligns the methodology document with Modo Energy's standards for writing benchmark methodologies. Added definitions (§1.4), a table of the parameters Modo Energy sets (§4.1) and a limitations section (§9). Clarified the use of judgment (§4.3), the treatment of distorted data (§8.3), the definitions of non-material and emergency changes (§7.3), and wording and naming throughout. The methodology and published Benchmark values are unchanged.

Disclaimer

This document, including the methodologies and indices described herein, is the proprietary work of MODO ENERGY LIMITED (“Modo Energy”) and is provided solely for informational purposes. These indices are designed for use in financial analysis, benchmarking, and decision-making. However, they do not constitute investment advice or a recommendation regarding any specific financial instrument, asset, or strategy.

While Modo Energy strives to ensure the accuracy, reliability, and transparency of the indices and methodologies, all information is provided “as is”, without any express or implied warranties, including but not limited to warranties of merchantability or fitness for a particular purpose. Users should be aware that the benchmarks are derived from publicly available market data that may be subject to revisions, delays, or inaccuracies, and that the ME vBESS DE Benchmarks are simulated from a representative asset rather than observed from underlying transactions. Past performance is not indicative of future results, and external factors such as regulatory changes, market conditions, and asset-specific characteristics may impact index performance.

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