When a facility manager looks at the annual bill total and says "we need a battery of this many MWh," the feasibility study has taken a wrong turn in its very first sentence. A BESS feasibility study is not a capacity estimate; it is an assessment of the investment's technical and financial viability by modeling consumption, generation, price, grid and technology data together. The five mistakes below commonly arise not in the calculation itself but in the assumptions that feed it.
1. Sizing from annual or monthly data
What determines storage power (MW) is the peak behavior at the demand peaks, not total energy. Monthly bill data averages out these peaks and makes the real peak requirement look smaller than it is. Looking at the same facility's 15-minute series, the short but sharp peaks hidden by the monthly average become visible.
| Input resolution | Apparent peak | Suggested power | Result |
|---|---|---|---|
| Monthly average | ~1.7 MW | ~0.6 MW | Undersized |
| 15-minute series | ~2.4 MW | ~0.9 MW | Realistic sizing |
When the power is undersized, the peak shaving target is missed on the first hot summer day; the investment is then said to "not work," when the problem lies not in the battery but in the resolution of the input.
2. Confusing capacity with power
Peak shaving is defined by two independent quantities: power (MW) determines how much of the peak you can shave, and capacity (MWh) determines for how long. A battery installed with sufficient power but insufficient capacity at a facility whose peak lasts two hours runs out before the peak is over. Sizing is a scenario analysis that sweeps both axes together; it is not a single "MWh figure."
3. Deciding on a single price scenario
Arbitrage and self-consumption revenues depend directly on price differences. A model built on a single market price series or a single tariff assumption ignores price volatility and shows the payback as either too optimistic or too pessimistic. A sound feasibility study reports the payback not as a single year but as a range tied to price and tariff scenarios — and states clearly which variable that range is most sensitive to.
4. Covering degradation with first-year capacity
Battery capacity declines with cycling and time. Entering first-year capacity as a fixed input in a ten-year revenue calculation makes performance in the later years of the project look higher than it is. Degradation is not a fixed number but a curve that depends on the operating profile (number of cycles, depth, temperature), and it should enter the model that way.
5. Being unable to say "this is not suitable for this facility"
This fifth shortcoming is not technical but attitudinal. At some facilities the existing tariff structure, a small peak difference or limited flexible load does not support storage. The job of an independent feasibility study is not to validate the investment under every condition; it is to say where the data points. Postponing an unsuitable investment or narrowing its scope is also a valid outcome.
The common denominator
The common thread in these five mistakes is that the conclusion comes before the methodology: a payback expectation is set and the data is interpreted to fit it. The right order is the reverse — validated data and scenarios first, then the decision they point to. Building a feasibility study as a decision framework rather than an approval document closes off most of these mistakes from the start.