Energy asset revenue modelling 2.0: goodbye to Excel

Why your spreadsheets are no longer enough to value flexibility

Today, the traditional way of modelling renewable revenue can, and often does, lead to wrong conclusions and poor financial decisions.

The problem is not that traditional models were incorrect in their day; it is that the market they were designed for no longer exists.

Traditional revenue modelling

There was a time, not so long ago, when valuing a solar or wind project was relatively simple:

• You estimated a generation profile.
• You applied an average price curve.
• You adjusted for degradation, availability and little more.

For years this approach was enough. It was not perfect, but it worked because the electricity market was relatively stable and revenue depended mostly on the spot market or regulated schemes.

Today that methodology is not just insufficient: it is dangerous. The European, and particularly the Spanish, electricity market has changed more between 2020 and 2025 than in the two previous decades. The massive entry of renewables has brought new phenomena: collapsing solar prices, negative-price events, local congestion, extreme irradiation variability, increasingly important flexibility services, and shorter, lower PPAs with restrictive clauses.

Why revenue modelling has changed

Classic revenue modelling relied on: a generation profile (based on Typical Meteorological Year data and historical wind speed series), captured-price curves, often monthly, and capital and operating costs as near-fixed inputs.

To add some realism, developers included two risk factors:

• Volume risk: tied to the asset's actual output. If the plant generates less than expected, every financial indicator deteriorates.
• Market/price risk: the uncertainty of the energy price. Lower-than-expected prices erode revenue, while high-price scenarios were used to assess whether to sign PPAs or bilateral contracts.

Today, however: generation is no guarantee of revenue, the average price is irrelevant for capturing spreads, and CAPEX does not determine profitability without understanding flexibility markets.

Price cannibalisation, especially in PV, has notably reduced expected returns and exposed the limits of the traditional method.

The rise of price cannibalisation

As solar and wind expand, supply rises in high-production hours but demand does not necessarily follow. The result is a systematic drop in prices during solar hours, cannibalisation, which reduces the revenue of newer, more vulnerable plants.

Negative prices: an anomaly turned norm

Negative prices influence: no-take clauses in PPAs that can leave part of the energy unpaid, financial models that ignore capacity or service revenue, and a reduction in the value of guarantees of origin (GoOs). These impacts are hard to capture with an average, aggregated curve that ignores peaks, extremes and intraday behaviour.

PPAs no longer protect as before

The market has moved towards shorter, cheaper PPAs with more complex clauses: tolerance ramps, floor caps, non-activation clauses at extreme prices and partial indexation to spot markets. The power system can no longer be understood without intraday markets, balancing services (aFRR, mFRR, RR), congestion management (technical constraints), emerging capacity payments and new flexibility mechanisms.

The whole average-based methodology has lost relevance because extreme events explain much of the value, hourly spreads widen, intraday gains prominence and the generation/price correlation is increasingly volatile.

Today's revenue modelling: a complex system demands a complex model

Valuing renewable assets is no longer an arithmetic exercise but a deeply technical analysis. Curtailment, grid constraints, hourly volatility, weather uncertainty, regulatory change and, for storage, the operational complexity of the battery all interact and determine real profitability. None of this can be captured with an average price curve or a static model.

The light at the end of the tunnel

There are now methodologies and platforms designed to integrate volatility, multiple markets, operational strategies and future scenarios coherently. Approaches like One Hub Analytics point towards a more realistic valuation model, better aligned with how the market actually works and, above all, more useful for making investment decisions on renewable and flexible assets.