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Long-term estimation (typically for project life of 25 years) of energy generation from solar projects is critical for technofinancial viability of the project. Energy Yield Assessment (EYA) of solar projects forms the basis for project developers and financial institutes for financial modelling of the project. Several modelling softwares are available in the market for the EYA of solar projects, including PVsyst, PV*SOL, RETScreen, HOMER, HelioScope, PlantPredict, Archelios, and SAM amongst others. Over the years, PVsyst has emerged as the most bankable modelling software for the EYA of utility scale solar PV projects while SAM has been used by few developers to predict energy yield from utility scale Concentrating Solar Power (CSP) projects. The objective of employing these modelling softwares is to predict actual energy generation with maximum possible accuracy. The solar PV industry in general follows established norms to carry out the EYA of projects worldwide. However, the CSP industry is yet to identify such common norms.


Usually, simulation softwares for predicting energy yield from solar projects broadly involves the following steps:

I. Importing weather data: Solar irradiance, wind speed, ambient temperature, etc., form a part of weather data. The weather data for the project location can be satellite based, ground measured, or a combination of both. Prior to importing weather data, the most relevant data source is identified through the exercise of solar radiation resource assessment. Users can also import weather data from the software library.

II. Input/import technical parameters: The technical parameters to these softwares will mostly rely on the suppliers of major components. For instance, the technical parameters of PV modules and inverters would be captured in .pan and .ond files certified by respective suppliers. These files are imported directly to PVsyst for simulation. Users can also choose equipment of a different make for project design from the software database.

III. Applying losses: The application of loss parameters for simulation requires some level of technical expertise. Some losses are quantifiable while others have to be assumed with appropriate basis. The calculation or assumption of loss values will depend on the present status of the project (planning, execution, commissioned, etc.)


An underestimation or overestimation of energy yield may significantly impact risks imposed on project finance. For instance, the solar resource of any project reduces by a variable (unknown) factor every year for the project life. Consequently, the annual energy yield from the project would be less than the planned yield. Revenues from the project would be lower than expected, the expenses (e.g., O&M cost) would remain unchanged and hence the project would pay less dividends as planned.

Even so, the return on equity from the project would reduce. The project would eventually turn out to be less attractive financially and this may raise concerns to the lenders.

A few critical aspects of EYA of solar projects have been discussed below:

  • Weather (solar resource) data: Unavailability of bankable solar resource data is one of the key barriers in the development of CSP projects in India. Godawari Green CSP Project in India with parabolic trough technology struggled to achieve the estimated output which led to re-engineering of the project; 80 loops of parabolic mirrors were increased to 120 loops.
  • Consideration of losses: The consideration of loss values in simulation will significantly impact the performance ratio1 of the project. In case of turnkey projects, the project developer will take over and accept the project from EPC contractor on the basis of a guaranteed PR% value. The input loss values will depend on the current development stage of the project and level of expertise of the designer. Some key loss values input to PVsyst software for simulation have been discussed in Table 1.

1 Performance ratio is a percentage to express performance of PV plant. This also provides a benchmark to compare plant performances over a given time period.

Predicted energy yield

The predicted yield values from EYA exercise are expressed at P50, P75, and P90 confidence level. These values indicate that the predicted energy yield value will exceed with 50%, 75%, and 90% probability, respectively. Lenders/financing institutions or other stakeholders rely on consultants to furnish bankable EYA reports to feed information into their financial models. A bankable report would generally provide P50, P75, or P90 values as minimum.

Quantification of uncertainty

The predicted annual yield values for the project would be associated with uncertainties, which would propagate throughout the predication period. Till date, there is no standard framework for calculation of uncertainty of predicted yield values. The associated uncertainty would propagate in predicted annual yield values for the project lifetime. Parameters, such as solar resource measurement, modelling software, module characteristics, performance of inverter, transformers, cables, module degradation, inter-annual variability, shading and soiling, etc., would tend to introduce uncertainty in the EYA. Research and developmental work are under progress to establish a better understanding of uncertainties which can assist in portfolio assessment, design and execution, O&M activities, etc.


The EYA plays a critical role in decision making of not only project developers but other stakeholders, such as suppliers, investors, lending institutions, and insurers. There is a common approach worldwide for the EYA of solar PV projects. However, for CSP projects, a generalized approach is essential to significantly reduce risks associated with project financing.

Mr Saurabh Motiwala, Engineer– Renewable Energy, Tractebel Engineering Pvt. Ltd., Gurugram, Haryana. Email:

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