Battery storage revenues explained

What drives battery storage revenues

Battery assets earn money because they can buy power when it is cheap, sell when it is dear, and sell services that help the system stay balanced and reliable. The mix of revenues depends on local market rules, the volatility of prices through the day and the year, and how smartly the asset is operated. A strong business case stacks several streams at once, then manages wear and tear so value is sustained over the life of the battery.

Below we break the stack into four parts. For each, you will find what it is, how to estimate it, and pitfalls to avoid. A simple model you can copy into a spreadsheet follows, plus a worked pro forma for a typical site.

Energy arbitrage and price spreads

What it is

Arbitrage is the classic battery play. Charge in low price hours and discharge in high price hours. The revenue is driven by the spread between those prices, the usable energy of the battery, how often you cycle, and round trip efficiency.

How to estimate it

1. Choose a target cycle count per day. Many sites plan between half and one and a half cycles per day on average.

2. Measure an expected spread between charge and discharge windows. Use historical day ahead or intraday prices that match your planned operating pattern.

3. Apply losses. If round trip efficiency is 88 percent then each full cycle keeps 0.88 of the spread. Also subtract variable costs such as charging fees and route to market fees.

4. Multiply by energy. Usable energy equals nameplate energy multiplied by the operational state of charge window. Many operators keep a buffer, for example 85 percent usable.

5. Annualise. Cycles per day multiplied by 365 multiplied by usable energy in MWh multiplied by net spread gives expected annual arbitrage revenue.

Pitfalls

Chasing peak hours blindly can crowd returns if many batteries do the same thing. Use flexible strategies that adapt to weather, outages and interconnector flows. Allow for periods when spreads compress.

Frequency and balancing services

What they are

System operators buy rapid response to keep frequency within limits and to resolve imbalances. Batteries are well suited because they can ramp fast and accurately. Services include frequency containment, response to imbalances in near real time, and reserve for short notice events. Payment structures vary but usually include an availability fee for holding capacity and a performance or utilisation payment when energy is delivered.

How to estimate them

Look at recent clearing prices and acceptance rates for the services you plan to pursue. Build a simple stack with

• Hours offered per day

• Award rate as a percent of offered hours

• Awarded power in MW

• Availability price in pounds per MW per hour

• Expected utilisation energy and utilisation price in pounds per MWh

Multiply and sum for each service, then apply realistic discount factors for downtime and testing.

Pitfalls

Performance penalties can erase value. Test control systems, forecasting, and communications. Keep state of charge management consistent with service rules so you can deliver when called.

Capacity and constraint revenues

Capacity mechanisms

Many markets pay for dependable capacity that is available in stress events. Batteries can qualify if they meet duration and performance rules. These are usually annual or multi year payments per kW or per MW of de rated capacity.

Constraint and local services

In constrained zones a battery can earn by relieving network bottlenecks, absorbing surplus, or providing voltage and reactive support. Payments can be seasonal and may depend on bilateral tenders with the network company.

How to estimate them

For capacity, multiply de rated MW by the expected clearing price and contract term. For local services, use published tariffs or recent tenders and model expected hours of service.

Pitfalls

Do not double count. Some services block others for the same hours. Map exclusivity rules and stack hours carefully.

Degradation, cycling and life extension

Revenue matters only if the battery stays healthy. Lifespan depends on cycle count, depth of discharge, temperature, and charge and discharge rates. Treat degradation as a cost per MWh of energy throughput.

A simple approach

1. Start with a warranted cycle life at a reference depth of discharge.

2. Use the manufacturer curve to estimate life at your planned operating window.

3. Convert to a cost. Divide expected replacement cost of cells by lifetime energy throughput to get pounds per MWh.

4. Add auxiliary costs for cooling and compliance testing.

5. Use this degradation cost line in each scenario to see how tighter or looser cycling affects margin.

Techniques that extend life include limiting depth of discharge in low spread conditions, avoiding high C rates unless well paid, and active thermal management. A modest reduction in average depth of discharge can add years of life with only small revenue loss.

A simple model you can replicate

Create a sheet with the following inputs.

• Power rating in MW

• Energy capacity in MWh

• Usable state of charge window as a percent

• Round trip efficiency

• Cycles per day

• Average spread for target hours in pounds per MWh

• Variable cost per MWh charged and discharged

• Service prices for frequency and reserve

• Capacity price and de rating factor

• Local service price and expected hours

• Fixed operating cost per year

• Degradation cost per MWh throughput

Arbitrage calculation

Usable energy equals capacity multiplied by usable window. Net spread equals price spread multiplied by efficiency minus variable costs. Annual arbitrage revenue equals cycles per day multiplied by 365 multiplied by usable energy multiplied by net spread.

Frequency and reserve

For each service, availability revenue equals awarded MW multiplied by availability price multiplied by hours awarded. Utilisation revenue equals delivered MWh multiplied by utilisation price. Sum across services.

Capacity and local services

Capacity revenue equals de rated MW multiplied by the capacity price. Local service revenue equals service MW multiplied by service hours multiplied by service price.

Costs

Throughput MWh equals cycles per day multiplied by 365 multiplied by usable energy. Degradation cost equals throughput multiplied by degradation pounds per MWh. Add fixed operating costs and any route to market fees.

Margin

Gross margin equals total revenue minus variable costs and degradation. EBITDA proxy equals gross margin minus fixed operating costs.

Example site pro forma

Assume a 50 MW site with two hours duration, so 100 MWh capacity. Usable window is 85 percent. Efficiency is 88 percent. Average cycles per day are 0.9. The observed average spread for the chosen hours is 52 pounds per MWh. Variable costs total 3 pounds per MWh. Degradation cost is estimated at 7 pounds per MWh of throughput. Fixed operating cost is 900 thousand pounds per year. The site pursues a blend of intraday arbitrage, frequency response five hours per day on average, and a capacity payment.

1. Usable energy

100 MWh multiplied by 0.85 equals 85 MWh.

2. Net spread

52 multiplied by 0.88 equals 45.76. Subtract 3 for variable costs gives 42.76 pounds per MWh.

3. Arbitrage revenue

0.9 cycles per day multiplied by 365 multiplied by 85 multiplied by 42.76 equals about 1.18 million pounds.

4. Frequency services

Assume 20 MW awarded for five hours per day at 7 pounds per MW per hour availability. Availability revenue is about 255 thousand pounds per year. Assume 12 MWh per day delivered at 30 pounds per MWh. That adds about 131 thousand pounds.

5. Capacity

De rated at 70 percent, so 35 MW. At 12 pounds per kW per year this is 420 thousand pounds.

6. Local services

Assume none in the base case.

7. Degradation

Throughput equals cycles per day multiplied by 365 multiplied by usable energy which is 0.9 multiplied by 365 multiplied by 85 equals about 27,923 MWh. At 7 pounds per MWh the cost is about 195 thousand pounds.

8. Totals

This simple pro forma shows the value of stacking. Arbitrage plus services and capacity create a robust mix. You can adjust inputs to match your market, then add scenarios for high and low spreads or different cycle rates. Also test the effect of lower service awards or lower capacity prices.

Building confidence in your numbers

Use at least three scenarios. Base case with current observed prices and realistic availability. High case with stronger spreads and more awards. Low case with compressed spreads and limited awards. For each, stress the degradation cost by changing depth of discharge and cycles. Check sensitivity to fixed costs and route to market fees. The goal is a range of outcomes that you can compare with financing terms and risk limits.

Stacking energy arbitrage, balancing, capacity and constraint value is the path to resilient revenues. Use the model, refine inputs, and pressure test results.