Revenue stacking in ancillary markets: balancing capacity, activation and wholesale exposure

Flexible assets no longer depend on a single market for their returns. Batteries, fast thermal units, and aggregated demand response portfolios now operate simultaneously across reserve, balancing, and wholesale markets. The key question is not whether to participate in ancillary services, but how to optimise revenue stacking from these services without mispricing the opportunity cost.

For battery operators, flexible asset managers, and portfolio managers, stacking revenue streams offers diversification benefits but also adds structural complexity. Capacity payments, activation revenue, and wholesale hedging exposures interact dynamically. Incorrect allocation can quickly reduce margins.

What revenue stacking means

Ancillary revenue stacking involves participating in multiple market layers simultaneously to maximise overall asset returns. For example, a battery can offer frequency containment reserve in one delivery window, engage in aFRR (automatic Frequency Restoration Reserve) capacity in another, and also benefit from intraday arbitrage across these commitments.

Stacking typically involves:

• Simultaneous participation across markets

• Opportunity cost associated with not engaging in energy trading.

The main issue is structural: when an asset allocates reserve capacity, it sacrifices some flexibility in wholesale markets. This opportunity cost is rarely fixed and varies with forward spreads, expected volatility, and activation risk.

In practice, stacking decisions are inherently probabilistic. Operators need to estimate the expected value of capacity payments and activation revenue compared to energy trading. When volatility increases, inaccuracies in these estimates can accumulate rapidly.

Capacity vs activation revenue risk

Reserve participation introduces two distinct revenue risk dimensions: availability and dispatch.

Capacity payments provide a degree of predictability, as revenue is secured during the delivery window if awarded. Nonetheless, this predictability conceals underlying risks. One such risk is availability commitment, which occurs if assets fail to meet the contracted response, potentially leading to penalties or revenue loss.

Activation revenue is less certain. It depends on system conditions and dispatch frequency. Uncertainty in activation frequency can dramatically alter realised returns, particularly in products where energy remuneration forms a meaningful share of total income.

The risk balance can be summarised as follows:

• Capacity payments provide income stability but reduce optionality

• Activation revenue introduces variability but can enhance overall returns

• The interaction between the two determines the effective margin.

When activation levels are low, capacity revenue is the main focus. As activation increases, wholesale exposure and imbalance interactions become more significant. Therefore, battery optimisation should consider both the likelihood of being awarded and dispatch probability, rather than just the headline auction prices.

Wholesale exposure interaction

Revenue stacking becomes most complex when wholesale dynamics shift quickly.

A battery set aside for reserve capacity might miss opportunities for high-value arbitrage. When day-ahead and intraday spreads widen, having capacity locked in can restrict potential gains. Conversely, during narrow spreads, reserve capacity can outperform energy trading.

Two interaction channels are particularly important:

• Missed opportunities for arbitrage during periods of high volatility

• Correlation considerations between reserve activation and spot price spikes.

Activation frequently occurs alongside stress events, during which wholesale prices can also rise. This leads to asymmetric exposure, as the asset might generate activation revenue but remains unable to fully benefit from wholesale price spikes due to pre-committed reserve capacity.

Understanding the correlation structure is essential. When activation risk coincides with wholesale volatility, exposure increases materially, particularly during renewable forecast errors or sudden outages. Conversely, when correlation weakens, ancillary revenue stacking can provide meaningful diversification against wholesale spread compression. These dynamics sit within the broader intraday volatility cluster that increasingly characterises high-renewable systems, where flexibility-allocation decisions directly influence short-term price acceleration.

Optimisation challenges

The idea of stacking ancillary revenue might seem very appealing theoretically, but in practice, there are often more challenges than it appears. It's important to consider these real-world constraints to get a full picture.

Forecast dependency is central to optimisation. Accurate predictions of award probability, activation frequency, wholesale spreads, and imbalance pricing are essential. Even minor forecast errors can significantly alter stack outcomes.

Data integration requirements are equally demanding. Effective battery optimisation depends on:

• High frequency activation data

• Procurement volume transparency

• Cross-market price feeds

• Degradation cost modelling.

Without integrated datasets, decision-making tends to be reactive instead of strategic. This is where advanced analytics and cross-market pricing datasets provide real value, directly supporting Montel’s focus on optimization.

The optimisation problem is dynamic. As more assets engage in multi-market participation, competitive behaviours evolve. Auction clearing prices fluctuate, and wholesale spreads either narrow or expand based on flexibility allocation. The process of stacking also impacts market structure.

Governance and risk controls

Revenue stacking isn't just about numbers; it's also shaped by governance frameworks that guide how bravely portfolios distribute across different markets.

Clear priority rules are crucial. Operators need to specify when wholesale exposure takes precedence over reserve commitments and vice versa. Lack of clarity during stress events raises operational risk.

Scenario stress testing should incorporate:

• Elevated activation frequency

• Reduced capacity award probability

• Extreme wholesale price volatility

• Correlated imbalance exposure.

Stack optimisation decisions should be reviewed against worst-case combinations rather than relying solely on base case expectations. Periods of elevated activation, compressed wholesale spreads and reduced award probability can align unexpectedly, amplifying downside exposure. Embedding these tail scenarios into governance frameworks strengthens resilience, particularly during market disruptions. This broader stress architecture is explored in more detail in Risk management during stress events, where correlation breakdown and liquidity tightening are examined systematically.

Conclusion

Ultimately, ancillary revenue stacking involves balancing stability and flexibility. Capacity payments ensure steady income, while activation revenue adds variability. Wholesale hedging can boost profits but also raises exposure to market volatility.

Battery optimisation isn't solely about aiming for the highest return in a single market. Instead, it involves accurately assessing the opportunity cost of pricing across interconnected markets. As ancillary and wholesale markets become more linked through flexible asset participation, disciplined management of the energy stack will help distinguish resilient portfolios from those that are overexposed.

In more volatile power systems, participating in multiple markets becomes a structural necessity rather than just an opportunity. The competitive advantage stems from better modelling the interactions among capacity payments, activation revenue, and wholesale exposure compared to the market average.