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 Indices.

1. Introduction

The Modo Energy Germany Methodology Framework outlines the calculation and construction of Modo Energy's battery energy storage Indices 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 index

The Modo Energy Germany Index differs from Modo Energy's GB Index in one fundamental respect: it is 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 Germany Simulated Index represents

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

2. Index Construction

2.1 The ME BESS DE Indices

Modo Energy currently produces a family of three indices for Germany:

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

2.2 Representative BESS specification

The representative asset is specified as follows:

2.3 Index calculation methodology

The Index is calculated by:

1. Running Modo Energy’s Global Dispatch Model over the assessed period, using the representative asset specification in §2.2 and the input data sources in §4.2.
2. Computing the simulated revenue of the representative 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 rated power of the representative asset (50 MW) to obtain an Index value in EUR/MW per 15-minute settlement period.
4. Aggregating these values into monthly, quarterly, and annual indices as described in §2.4.

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

2.4 Index value representation

All Index values are reported as net revenues per unit of rated power. Revenue and Index values can be represented using the following units:

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

2.5 Publication cadence and revisions

The Modo Energy Germany Indices are updated daily. Modo Energy ingests source data from all markets listed in §4.2 on a daily cadence and publishes Index 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 Index for that day is computed.

Previously published Index values may be revised if upstream data providers issue corrected values for periods already covered by a published Index, if additional source data becomes available that materially improves the accuracy of a historical Index value, or if a methodology change under §7 is applied with retrospective effect. Revisions are logged with the affected period and the reason, and are communicated to users in line with §7.2.

2.6 Market evolution reflected in the Index

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

3. Revenue Components

3.1 Revenue components included in the Indices

The Modo Energy Germany Indices 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 Modo Energy Germany Indices:

• 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 will be made available in custom indices.
• Capacity mechanism payments: Germany's capacity mechanism is in implementation. The Index will incorporate capacity market revenues once the market starts clearing and prices are published publicly.
• 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. Modo Energy will review incorporation in a future methodology version if we see major market participation.
• Battery operator fees, warranty costs, and O&M: the Index reports gross market revenue, not net-of-opex.

4. Data Inputs and Use of Discretion

4.1 Data visibility and quality

The Modo Energy Germany Index is constructed exclusively from publicly available market data, with the exception of the representative asset parameters in §2.2, which are set by Modo Energy.

4.2 Data inputs and sources

The Modo Energy Germany Index is constructed from publicly available market data, sourced from the providers listed below. All transformations are documented in §4.3.

4.3 Use of discretion

Modo Energy applies discretion strictly within predefined parameters to ensure consistency and transparency. Sources are selected based on reliability, relevance to BESS operations, and activity, depth and transparency of the underlying market. All discretionary decisions are made in accordance with the benchmark methodology and subject to internal governance oversight.

5. Modelling Methodology

5.1 Overview of the Global Dispatch Model

The Germany Index is produced by simulating the dispatch of representative assets described in §2.2 against historical market prices using Modo Energy's Global Dispatch Model (GDM) — the same proprietary optimisation framework that underpins Modo Energy's Indices in other regions as well as forecast. 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 auctions, intraday continuous, and real-time.
• 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 POS.
• 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 Index (§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 (POS or NEG), 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 POS (upward) and NEG (downward) capacity be contracted in equal volumes.
• Symmetric derating: the total (POS + NEG) 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 is reviewed periodically by the Modo Energy team and may be updated in accordance with 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 — real assets are not available 100% 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 are not already reflected

To bridge this gap in a transparent, single-parameter way, the Index 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 is reviewed annually by Modo Energy's benchmark oversight function and updated in accordance with the methodology change process (§7).

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 notification of significant changes with a two-week consultation period.
• Documentation of stakeholder feedback and responses, available upon request by emailing team@modoenergy.com.

7. Methodology changes

7.1 Review and update process

The methodology undergoes a structured review process to ensure it remains aligned with evolving market conditions and regulatory requirements. Reviews are conducted:

• Annually by the Benchmark Oversight Function.
• Quarterly manual audits to assess data accuracy and consistency.
• Upon identification of material changes in market conditions or data availability (e.g. EPEX transition to 15-minute day-ahead auctions, future incorporation of 15 min FCR and aFRR capacity markets, etc).
  • Direct back-testing against observed transaction data is not possible because per-asset revenue is not publicly disclosed in Germany. Instead, Modo Energy applies an 80% capture rate (§5.9) inspired by actual fleet performance in markets where both perfect-foresight modelled revenues and realised asset earnings are observable (e.g. Great Britain, ERCOT).

Each review follows a documented approval process, ensuring updates are thoroughly evaluated before implementation.

7.2 Notification of changes

Significant methodology changes are communicated to stakeholders with sufficient advance notice and a clear timeline for review and feedback. The notification process includes:

• Publishing proposed changes with a detailed impact analysis.
• Allowing stakeholders a two-week consultation period to provide comments.
• Providing formal responses to stakeholder feedback and incorporating adjustments where appropriate.
• Maintaining an archive of all changes to ensure historical comparability and transparency.

8. Consistency and continuity

8.1 Quality assurance

Modo Energy employs rigorous quality assurance processes to ensure benchmark integrity. These include:

• Continuous automated validation checks to identify discrepancies in input data.
• Automated regression tests on every change to the Global Dispatch Model.
• Quarterly manual audits to verify data sources and methodology compliance.
• Internal audits to ensure alignment with regulatory standards.

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, netztransparenz.de) into the model feed.

8.3 Handling data quality issues

Modo Energy has clear procedures to address instances where the quantity or quality of input data falls below the standards required for accurate and reliable benchmark determination:

1. Data issue verification: when data quality issues are identified, Modo Energy confirms the issue with the upstream data provider.
2. Customer communication: customers are informed of any confirmed data issues and corrective actions taken to maintain transparency within 48 hours of confirmation.
3. Data unavailability: in cases where the data provider is unable to supply the required data, Modo Energy notifies customers of impacts and publishes the Index only once finalised data is available.

8.4 Traceability and verification

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

• Maintaining comprehensive records of input data, model version, and calculation outputs.
• Reproducibility: every published Index value can be reproduced from the archived input data and the model version in use at the time of publication.
• Public disclosure of material methodology changes.

Appendix I — Methodology changes

Methodology changes since first publication will be tracked here.

Clarification updates

None at initial publication.

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 indices are derived from publicly available market data that may be subject to revisions, delays, or inaccuracies, and that the Germany Indices 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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