Revenue stacking in energy storage: balancing utilisation, availability and opportunity cost

Defining revenue stacking in practice

Revenue stacking refers to the way multiple services are stacked on a single asset. This might include mixing and matching arbitrage, wholesale and high-frequency services. We can service different demands using different sections of a battery, or even switch which services we use and when, in a process called splitting.

Participating in multiple markets simultaneously

As mentioned above, stacking involves combining multiple services on a single asset. The wholesale market involves selling when energy prices are high and charging when they are low. Ancillary services are a high-paying part of the stack and are also low energy; baseline revenue tends to come from capacity markets. Transmission services are more bespoke services for the grid.

Allocating capacity dynamically

A battery's energy is best optimised by allocating capacity to the most profitable combination of markets at any time. We do this through dynamic allocation. Where this can become tricky is when we attempt to balance both utilisation and availability. State of charge (SoC) allocation for high-availability services needs to be prioritised, as well as balancing the high-profit, wholesale price increases. We do this through opportunity cost analysis. If selling to the wholesale market returns less than we would receive in high-availability services, we would deem the opportunity cost as too high. We can also engage in techniques such as avoiding rate management. Skip rate refers to a battery that is involved in several markets and is not dispatched even during low-cost periods, reducing profits.

Software solutions need to be engaged to manage dynamic allocation: they constantly monitor the market to identify opportunities amid market volatility.

Conflicts between revenue streams 

Conflicts can occur between different revenue streams, with ancillary availability vs intraday trading being one key conflict. This arises from a need to balance grid, or ancillary, services with energy arbitrage. When servicing the ancillary market, the battery needs to be at 50% SoC for instant response. Intraday, on the other hand, requires frequent cycling of the battery, which diverts it from ideal frequency response conditions. This can cause issues because operating in both markets can mean there isn't enough availability of baseline ancillary services, or not enough to capitalise on market price increases. To combat this, optimisation strategies may be used, such as forecasting when to swap between markets or splitting capacity to serve both markets.

Lock-in vs flexibility trade-offs

Different types of energy contracts have different levels of risk: short-term merchant revenue involves significant risk, while long-term, low-flexibility contracts involve lower risk. We call this latter form of revenue lock-in, or contracted revenue, as it locks in battery capacity. This offers stability with guaranteed income, but also means a battery is less nimble and able to react to changes in the wholesale market. Allowing flexibility for merchant trading is key because these are the most profitable markets to operate in. They do, however, introduce risk as income is not guaranteed and can be volatile. The best approach is to use a hybrid solution that balances both, allocating a specific portion to each market.

Opportunity cost framework

The opportunity cost framework helps balance battery usage, for example, ensuring it does not prioritise low-value markets over high-value ones. This is important because all sales decisions consume useful battery capacity. The degradation caused by frequent use of the battery also needs to be considered. An optimal dispatch strategy can help determine when the wholesale or frequency market should be prioritised to maximise returns. The balancing mechanism must also be taken into account: this high-risk but high-reward revenue stream can be impacted by skip rates, which can be mitigated by Open Balancing Platform tools. Ultimately, we compare the expected values of each market to build the stack that is most profitable.

Real-time decision-making

To make accurate, profitable, real-time decisions, dynamic optimisation systems must examine the following: Day-Ahead, Intraday and Balancing markets, as well as ancillary service requirements. Often, all of these opportunities can arise at the same time, so dynamic optimisation systems help determine the most attractive market to focus on based on SoC and profit margin.

Dynamic optimisation strategies

This multi-market approach relies on hourly or sub-hourly decisions about whether a battery should remain idle to respond to high-profit, high-frequency events or be used to serve the ancillary market.

Re-optimising positions as conditions change

Market conditions are often changeable and this means elements such as price spreads and grid demand make static schedules unsuitable, which is why dynamic optimisation is so crucial. Algorithms can make intraday adjustments as market conditions change, continuously updating the allocation of battery capacity.

Using forecast updates 

The key metrics we should follow for forecasting include: ancillary service demand, high-frequency markets, energy price updates and renewable generation expectations. Monitoring and acting on these signals enables operators to determine the optimal time to charge or dispatch battery capacity, taking into account factors such as SoC and battery health.

Portfolio implications

It's undeniable that stacking can help to increase revenue compared to single-service strategies. But all of these battery capacity strategies, combined, can affect energy portfolios and the decisions traders make about what to prioritise.

Making sure traders diversify across different markets, for example, combining polarising frequency vs wholesale markets, can help to stabilise portfolios if managed directly. This leaves a portfolio less open to the volatility of a single market or service.

It's important to remember that geography affects portfolio spread, as different regions offer different markets and regulations that may impact portfolio decisions.

Traders must also be aware of double-counting capacity: allocating capacity to one market that is already required by a different market or service.