FCR
aFRR
Cross-Market
Aggregated revenue from FCR activities over the last 365 days for a 2h/ 2 cycle battery system.
Aggregated revenue from aFRR activities over the last 365 days for a 2h/ 2 cycle battery system.
Aggregated revenue from Cross-Market activities over the last 365 days for a 2h/ 2 cycle battery system.
ISEA Battery Revenue Index
The revenue potential for large-scale storage is diverse today. Above, we show revenues for individual markets and a cross-market approach in which the battery is active in several markets simultaneously. The desired battery configuration can be selected using the drop-down menus. Revenue is calculated transparently and can be verified using our Git repository for one day. More information on our methodology and the code can be found in our documentation.
Detailed Cross-Market Revenues
In todays electricity market, battery storages are typically active on multiple markets in parallel. We call this marketing strategy “Cross-Market”. As it is quite dificult to mimic the real-world activity of a storage system on all markets, we developed a simplified approach to estimate the revnues that can be generated with such a marketing strategy. What we basically do is to give every market pre-defined partitions of the overall system and then calculate the revenues similar to the single-market approach.
For now we include FCR, aFRR and intraday continous trading in this approach. For each 4h block, we decide if we want to be active on FCR or aFRR Capacity based on the possible revenue in each market. Then we use 50% of our capacity and power to trade aFRR Energy with a simple rule-based trading approach. Then we use the other 50% of the system plus unused capacity from aFRR Energy to trade on the intraday continous market.
Annualized Monthly Revenues
This plot shows the annualized monthly revenue potential off all different marketing options. We include balancing services like FCR and aFRR (Capacity + Energy) as well as wholesale markets. For the wholesale markets we consider the day-ahead market (DA) and intraday auction 1 (IDA1). To represent the intraday continous trading we offer two perspectives. One is the indexed-based revenues (ID1) and the other is the IDC-approach where we use transaction data from the orderbook to calculate more realistic revenues. Combining multiple of these services leads to the Cross-Market results (FCR+aFRR+IDC).
Top-Bottom Spreads
Top-Bottom (TB) spreads provide a useful benchmark for estimating the revenue a battery can generate through energy arbitrage. By comparing TB spreads across different timeframes and markets, stakeholders can assess the most attractive strategies and locations for deploying battery energy storage. The principle behind this is straightforward: “buy low, sell high.” Batteries can exploit daily fluctuations in electricity prices to create value. A TB spread offers a simple yet powerful way to quantify this opportunity. It represents the gap between the highest and lowest electricity prices within a day:
TB1 spread: the single highest hourly price minus the single lowest.
TB4 spread: the sum of the four highest hourly prices minus the sum of the four lowest.
In essence, TB spreads act as a reference point for the maximum daily revenue a battery could earn from arbitrage.
Methodology
The index we developed quantifies revenue potential for large-scale battery storage in Germany. Our methodology has the following characteristics:
- Transparency: Thanks to our open-source code, every user can understand and validate the calculation. This allows everyone to decide for themselves how suitable the calculation basis is for their own asset.
- Complexity: We avoid complex methodology in our calculations to ensure broad acceptance. This means that the index moves further away from industry methodologies, but remains understandable and comprehensible for everyone. We limit ourselves to selected system configurations and market assumptions. These must be taken into account when comparing the index values with actual revenues.
- Reproducibility: We provide sample data that can be used to recalculate a sample day. By using your own data in the same format, the methodology can also be extended to other days.
More information can be found in our documentation. We have also stored the code for calculating the index in our Git repository.
Assumptions for the battery system:
- Round-trip efficiency: 90.25%
- Capacity: 4/2/1 MWh (4/2/1h)
- Power: 1 MW
- Cycles per day: 1 or 2
Assumptions for the markets:
Day-ahead/intraday trading: the power limit (P
max) and state-of-charge limits (0-100%) of the battery must not be exceeded.
IDC (Intraday continous): These revenues provide a more detailed picture of the actual revenues from today’s intraday trading on the electricity exchange, because they reflect not only physical trading but also purely
financial trading. To do this, we use the rolling intrinsic method, which buys and sells products on the market on a rolling basis using a linear optimization model. The data used here is not a simplified index (such as ID1), but
the transaction data from EPEX Spot.
Frequency Containment Reserve (FCR): maximum marketable capacity is reduced by 20% according to the pre-qualification (PQ) conditions.
Automatic Frequency Restoration Reserve (aFRR): Symmetrical marketing is assumed for the positive and negative power market (50% state of charge). It is also assumed that the marketable power is available for one
hour. Therefore, 0.5 MW is marketed in both directions in the 2-hour system and 0.25 MW in the 1-hour system. The market entry barrier of 1 MW is disregarded. In the aFRR Energy Market, we bid based on IDA1 prices in all 1/4h auctions.
For a positive call, we place ourselves in the merit order with a 50% premium, and for a negative call, we place ourselves with a 50% discount on the intraday auction prices.
Cross-Market (FCR + aFRR + IDC): In this approach, the battery is divided virtually. First, a decision is made for the 4-hour slices of the day as to whether it is more attractive to participate in the FCR or
aFRR capacity market. Then, depending on the selected capacity market, the remaining power and energy of the storage facility is divided evenly between the aFRR Energy and IDC markets. The revenues from these markets are calculated
in the same way as the revenues from the individual markets.
ISEA Revenue Index Team
Jonas Brucksch
M. Sc. / Project leader
Developer of the revenue index and scientist at the Chair of Electrochemical Energy Conversion and Storage System Analysis
Jonas van Ouwerkerk
M. Sc. / Group leader
Head of Department at the Chair of Electrochemical Energy Conversion and Storage System Analysis
Dirk Uwe Sauer
Prof. Dr. rer. nat. / Director
Professor of Battery and Energy Systems Research and Director of the CARL Research Centre
