Bridging the Gap Between Theory (FCAs) and Practice (BESS Optimization)
The energy transition is accelerating at an ever-faster pace, and nowhere is this more evident than in the rapid expansion of battery energy storage systems (BESS). To accelerate this expansion, grid operators across Europe are increasingly turning to Flexible Connection Agreements (FCAs). These agreements promise faster (or even the first-ever) grid connections in exchange for allowing the distribution system operator (DSO) or transmission system operator (TSO) to curtail or restrict the facility under certain conditions.
On paper, FCAs are efficient, pragmatic, and conducive to innovation. In practice, however—especially when they are dynamic rather than deterministic—they lead to operational uncertainty that can significantly complicate the optimization of battery energy storage systems (BESS).
At the BATCON-1 conference in Munich in March 2026, our COO Christoph Gardlo explained what many in the industry are gradually coming to realize: „Dynamic FCAs are extremely difficult to model and are therefore practically unusable for billing in real-world BESS optimization.“ This article summarizes why that is the case.
Why FCAs Exist and Why „Dynamic“ Is Important
The impact of FCAs on BESS optimisation
There are several factors that investors and owners of battery storage systems should consider when choosing an FCA:
- Every FCA affects the optimization and there are no exceptions: Even seemingly minor restrictions (such as adjustments to the ramp rate or temporary feed-in restrictions) reduce the range of options. Batteries monetize this range of options. A restriction reduces the value.
- Communication timing drives the revenue impact: The timing of feed-in restriction signals is where the „dynamic“ element of FCAs becomes the greatest challenge. If the grid operator issues a restriction 24 hours before the start of trading, the battery can adjust its optimization strategy accordingly. However, the closer the feed-in restriction signal is to the close of trading, the greater the risk of financial losses, as there is less time to react and less flexibility for optimization.
- A typical decline in revenue is around 20 %: Even with moderate constraints, such as a ramp rate limit of 0.025 % per second (approximately 1 hour for both up and down ramps), our analysis shows a typical loss of value of around 20 %, assuming that balancing energy remains available. Ramp rates have a significant impact on battery optimization and can limit usable capacity, particularly in balancing energy markets. For example, the primary reserve (PRL/FCR) requires full activation within 30 seconds. Smaller battery systems are often unable to ramp up to the minimum bid size of 1 MW within this timeframe and are therefore unable to participate in the PRL market. Secondary reserve (SRL/aFRR) allows up to 5 minutes for full activation and thus offers greater operational flexibility. However, ramp rate restrictions continue to limit the capacity that can be offered on the ancillary services market, which ultimately reduces revenue potential.
- FCAs are similar to industrial restriction management: For decades, the industry has been dealing with grid congestion, whether due to load profiles, peak load coverage, load shedding, or the optimization of grid tariffs. The concept is not new, but its application to BESS optimization—where decisions must be made within milliseconds—makes the complexity of FCAs far more significant.
From the perspective of BESS investors: Reliability is essential
Investors can live with FCAs, but not with surprises. Once capital has been deployed and the revenue model established, the introduction of additional uncertainties—particularly dynamic feed-in restrictions—creates risks that even sophisticated hedging models have difficulty quantifying.
Christoph summed it up this way: „FCAs can certainly work, but not after the capital has already been invested.“ However, this view is not universally shared in the market. Some argue that tolling with FCAs is still possible and that the key lies in risk structuring, not in the restriction itself. Well-organized flexibility marketers should therefore be able to handle this.
While that may be true, in our view, only a few players in Germany have the capability to structure tolling under dynamic FCAs. And in our experience, poorly designed FCAs lead to underperforming battery storage systems, since no amount of optimization can compensate for a structurally flawed grid access agreement.
Christoph agrees that tolling is possible under FCAs, but he emphasized the key point: The challenge is not FCAs themselves, but dynamic FCAs—that is, those that cannot be planned for. This distinction is crucial. A static or clearly communicated restriction can be priced and managed. A volatile, unpredictable one, on the other hand, cannot.
Conclusion: How ESFORIN believes FCAs should be handled today
Drawing from our operational experience, we offer the following perspective on how to navigate FCAs in the current regulatory and market landscape:
Treat dynamic FCAs with caution
If an FCA contains restrictions that are unforeseeable, not clearly communicated, or highly volatile, investors should assume a significantly lower value and adjust their optimization strategy accordingly.
Do you prefer transparent, predictable, and clearly communicated restrictions for your battery storage system?
A clear, albeit strict, set of restrictions is more reliable than a flexible, unpredictable one.
FCAs must be evaluated before, not after, commissioning
Once the battery storage system is completed, it is virtually impossible to make subsequent adjustments to the optimization or to renegotiate the terms. Risk assessment is not optional.
In summary, Christoph’s expert opinion is: „FCA restrictions are manageable. Dynamic, unpredictable restrictions can be managed operationally, but modeling them in advance is difficult, and therefore there is a risk of significantly—or even excessively—reducing the value of the battery storage system. For now, FCAs should be structured with clear operating rules and predictable communication so that they can be responsibly integrated into the optimization process.“
