AleaSoft Energy Forecasting, July 21, 2026. Low prices captured by renewables, hours with zero or negative prices, curtailment and growing volatility are changing the criteria for financing renewable projects. Bankability no longer depends solely on technology cost or the financial model. It depends, above all, on whether revenue forecasts reflect how the asset will perform throughout its useful life.
A more demanding market for merchant renewables
The growth of solar and wind capacity is transforming the price structure of electricity markets. Renewable technologies produce when the resource is available, pushing prices down precisely during the hours when they sell their energy. This cannibalisation effect has significantly reduced captured prices, especially for photovoltaic energy.
This situation is compounded by hours with zero and negative prices and the increase in curtailment, caused both by abundant renewable production and by grid limitations. As a result, the average market price can remain relatively high while the revenue obtained by certain renewable plants is insufficient to meet the expectations on which their financing was structured.
Volatility represents a threat for renewable plants without management capacity, but also an opportunity for storage. The greater the difference between low-price and high-price hours, the greater the potential value of shifting energy from one hour to another.
Revenue is the true foundation of the financial model
Net present value (NPV), the internal rate of return (IRR) and the debt service coverage ratio (DSCR) are some of the main indicators used by investors and financial institutions. However, all of them are outputs of the model. Their reliability depends directly on the quality of the assumptions used to calculate revenue.
Investment and operating costs can be estimated with a reasonable level of certainty. Forecasting revenue over twenty or thirty years, on the other hand, requires anticipating the evolution of prices, captured prices, volatility, curtailment and the different sources of remuneration available.
An apparently small error in the captured price forecast can accumulate over the entire life of the project, reduce cash flow and push the debt coverage ratio below the required levels. In that case, the debt would need to be resized or become more expensive. For this reason, the key question for a lender should not only be whether the financial model is well built, but whether the revenue forecasts feeding it are reliable.
Batteries cannot be valued using fixed factors
Revenue estimation is especially complex in energy storage projects. A renewable plant produces when the resource is available and sells the energy at the price prevailing at that moment. A battery, however, is an asset that must be actively operated, deciding every hour when to charge, when to discharge and in which market to participate.
Its revenue can come from energy arbitrage, balancing services, capacity mechanisms and other services associated with flexibility. These sources cannot be analysed independently and added together afterwards, because the same energy or capacity cannot be offered simultaneously in several markets. Revenue must be calculated through joint optimisation.
Applying a fixed annual spread, assuming a constant number of daily cycles and using a flat degradation rate can produce results that are easy to interpret, but they do not adequately represent the operation of a battery. The number of cycles depends on actual market opportunities, and degradation depends both on the passage of time and on the asset’s effective use. Degradation also reduces available capacity, changes future operation and, therefore, subsequent revenue as well.
The valuation must reproduce this process hour by hour throughout the project’s useful life. Hourly price simulations, combined with optimisation algorithms, allow cycling, degradation and revenue to result from expected operation rather than from simple assumptions fixed in advance.
Contracts and hybridisation to reduce risk
Market risk can be mitigated through contracts. For renewable plants, fixed-price PPAs, floor-price contracts or price-band structures partially or fully protect revenue against unfavourable scenarios. This certainty improves debt coverage and can support a higher level of leverage.
For batteries, agreements with optimisers serve an equivalent function. A revenue-sharing contract retains merchant exposure and upside potential. A floor agreement protects a minimum revenue level while preserving part of the additional potential. A tolling contract shifts market risk to the counterparty in exchange for a fixed payment, increasing certainty but giving up potential revenue upside.
Hybridisation is another fundamental tool. Adding a battery to a renewable plant allows energy that would otherwise be curtailed to be stored, shifts production towards higher-price hours, shares the connection point and creates a more stable profile that is more attractive to an energy buyer. In studies carried out by AleaSoft Energy Forecasting during 2025 for hybrid photovoltaic plants with two-hour batteries in Spain, the average revenue increase was around 40%.
Bankable forecasts to turn uncertainty into financing
Forecast quality will be one of the main factors differentiating projects able to secure financing. A conservative scenario should not consist of arbitrarily reducing revenue, but of calculating a probabilistic distribution based on multiple coherent and realistic futures. In a more volatile and complex market, uncertainty will not disappear. However, it can be quantified, structured and financed. A risk that the lender can identify, reproduce and size ceases to be an unknown and becomes a manageable element within the operation.
AleaSoft Energy Forecasting analysis for project financing
The methodology developed by AleaSoft, with more than 27 years of track record, combines econometrics, regression, time series analysis, market simulation and artificial intelligence. For storage projects, it includes around 2000 hourly simulations, on which the battery’s operation is optimised throughout its useful life. This makes it possible to estimate revenue distributions and obtain metrics such as P50 and P90, essential for assessing risk, sizing debt and testing the project’s resilience against unfavourable scenarios.
For energy storage projects, AleaStorage, the division of AleaSoft Energy Forecasting specialising in storage, focuses on the strategic analysis of batteries and hybridisation projects, providing revenue estimates for stand-alone batteries in energy and balancing services markets, hybridisation analysis with renewables to maximise revenue and reduce risk, and modelling of long-term price and volatility scenarios.
Source: AleaSoft Energy Forecasting