Revenue modeling of energy assets 2.0: goodbye to Excel

What has changed in revenue modeling? 

Today, we find that the traditional way of modeling renewable income can and often does lead to misguided conclusions and poor financial decisions.  

The problem is not that traditional models were incorrect at the time; it is that the market for which they were designed no longer exists. 

Traditional revenue modeling 

There was a time, not so long ago, when evaluating a solar or wind project was a relatively straightforward operation. relatively simple: 

• You estimated a generation profile. 

• You applied an average price curve to it. 

• You adjusted for degradation, availability, and little else. 

For years, this approach was sufficient. It wasn't perfect, but it worked because the electricity market was relatively stable and revenues depended mainly on the spot market or regulated schemes. 

Today, that methodology is not only insufficient: it is even dangerous. 

The European electricity market, and the Spanish market in particular, has changed more between 2020 and 2025 than in the previous two decades. The massive influx of renewables has led to new phenomena: 

• collapse of solar prices, 

• negative price events, 

• local congestion, 

• extreme variability would radiate, 

• increasingly important flexibility services, 

• Shorter, lower PPAs with restrictive clauses. 

In this context, no static model can reflect the operational reality or the current financial risk. 

Welcome to the new phase of revenue modeling. 

 

Why has revenue modeling? 

What exactly did classic income modeling depend on? 

• Asset generation profile. Taking into account resource assessment using Typical Meteorological Data (TMY) and historical wind speed series. 

• Captured price curves, often monthly. 

• Capital costs and operating expenses of the project as quasi-fixed inputs. 

This means that the valuation of renewable projects was based on a very limited approach: combining a generation profile extracted from standard software for solar or wind power with a market price curve that, at best, reflected the average monthly price captured for the technology.  

These two elements were used to construct practically the entire financial model. 

To introduce some realism, the developers added two risk factors: 

• Volume Risk 

This risk is linked to the actual production of the asset. If the plant generates less than expected, all the financial indicators for the project deteriorate. For this reason, various generation scenarios were analyzed, although they were almost always based on relatively simple variations of the same profile. 

• Market/price risk) 

The second key component was energy price uncertainty. Lower-than-expected prices eroded revenues, while high-price scenarios were used to assess the advisability of signing PPAs or bilateral contracts, which were particularly important for ensuring stable revenues and facilitating bank financing. 

For years, this data allowed revenues to be estimated with a reasonable margin of error. Today, however: 

• Generation is no guarantee of income. 

• The average price is irrelevant for capturing spreads. 

• CAPEX does not determine profitability without understanding flexibility markets. 

Let's return to the big question: What has changed? 

Well, although it is still possible to model the income from a renewable asset using the classic approach (generation profile multiplied by an average price curve), the results tend to be less optimistic than a few years ago.  

Price cannibalization, especially in photovoltaic projects, has significantly reduced expected profitability and highlighted the limitations of the traditional method. 

But the real problem is that this model is far removed from operational reality. Today's markets require the incorporation of a variety of factors that were previously secondary or simply non-existent.

The emergence of the cannibalization effect of prices 

As solar and wind expansion grows, supply increases during peak production hours... but demand does not necessarily follow suit. The result is a systematic drop in prices during daylight hours, known as cannibalization, which reduces the income of newer and more vulnerable plants. 

Negative prices: an anomaly that has become the norm 

Although negative prices could be diluted within the average captured in a simple model, their effects go far beyond a lower average value. Today, they directly influence: 

• non-take clauses in PPAs, which may leave part of the energy without remuneration, 

• financial models that do not include revenue from capacity or services, 

• a reduction in the value associated with guarantees of origin (GOs). 

These impacts are difficult to capture if an average, aggregated curve continues to be used that does not reflect peaks, extremes, or intraday behavior. 

PPAs no longer offer the same protection as before 

The market has evolved towards shorter, cheaper PPAs with more complex clauses. Today, they include mechanisms that mitigate risk for the buyer but increase it for the generator, such as: 

• tolerance ramps, 

• Floor caps, 

• non-activation clauses in extreme prices, 

• partial indexation to spot markets. 

The electrical system can no longer be understood without markets such as: 

• intraday market, 

• balancing services (aFRR, mFRR, RR), 

• congestion management (Technical Constraints), 

• emerging capacity payments, 

• new flexibility mechanisms. 

A classic model does not control these nuances, and ignoring these dynamics can lead to lost revenue... or taking risks you didn't know you had. The entire traditional methodology based on averages has become obsolete because: 

• Extreme events account for much of the value. 

• hourly spreads widen, 

• Intraday trading gains prominence. 

• The correlation between generation and price is becoming increasingly volatile.

Current revenue modeling: a complex system requires a complex model 

The valuation of renewable assets is no longer an arithmetic exercise but has become a deeply technical analysis. Today, as we have seen, factors such as curtailment, grid constraints, hourly volatility, weather uncertainty, regulatory changes, and, in the case of storage, the operational complexity of the battery itself, interact with each other and determine the actual profitability of a project. None of this can be captured with an average price curve or a static model. 

Current revenue modeling requires understanding the system in all its complexity: how the network behaves, how prices move, how markets react, and how operational decisions, whether human or algorithmic, can change the financial outcome. Valuing an asset involves working with scenarios, probabilities, and dynamics that evolve on an almost daily basis. 

Modeling revenue today means embracing the complexity of the market and relying on methodologies capable of reflecting its true nature. Only then is it possible to make well-informed investment decisions in an electricity system that no longer resembles that of a few years ago. 

The light at the end of the tunnel 

Today, there are methodologies and platforms designed to integrate volatility, multiple markets, operational strategies, and future scenarios in a coherent manner.  

In this regard, approaches such as One Hub Analytics point toward a more realistic valuation model that is more aligned with market functioning and, above all, more useful for making investment decisions in renewable and flexible assets.  

Would you like to learn more about how One Hub Analytics works? Click here.  

You can also request a demo to see how it works firsthand.