The direct normal component of the solar resource is variable in time and space. The value of the DNI incident on a site varies throughout the day and depends on the climate of the location. Its value has a deterministic and a stochastic component. The deterministic component is caused by the apparent daily motion of the sun in the sky and the Earth-Sun distance. This component can be calculated by means of the Earth-Sun geometrical equations. The stochastic and less predictable component is caused by several agents but the main one is the motion of the clouds. Clouds have a stochastic behavior difficult to forecast at high temporal resolutions. In the absence of clouds, the atmospheric components such as aerosols also have an influence, although these parameters are slowly varying and do not significantly influence solar radiation at high temporal resolutions. Both agents are dependent on the climatic features of the area and, for high resolutions, on the microclimatic features of the location. So, the variability of the DNI could be quantified through estimating dispersion during a specific period (temporal variability) or over a specific area (spatial variability).
The variability can be expressed as the coefficient of variation (COV) for variables having a normal distribution, as the variance for any other known statistical distribution, or as the interquartile range when the distribution is unknown. COV is obtained by dividing the standard deviation by the mean of the population or sample (Sengupta et al., 2017).
(C) Due diligence
In this stage, in addition to the dataset of the most representative scenario of DNI, other datasets that represent different probabilistic scenarios are evaluated. These scenarios account for extreme situations in order to evaluate the investment risks. These scenarios should statistically consider the annual and the monthly variability of the long-term dataset. So, a statistical analysis of the annual values of a long-term dataset is required to estimate the annual DNI value of each scenario and monthly variability metrics are used for the generation of the dataset. With the assessment of the production of the CSP plant through these series, a similar statistical behaviour between the resource and the production is assumed. An alternative method to statistically characterize the production of the plant is the generation of multiple annual datasets, multiyear or plausible meteorological annual data series (Larrañeta et al., 2019) that statistically represent the resource of the site. These annual data series should include the interannual variability of the DNI and for their generation monthly and daily variability metrics should also be considered.
The recommended features for the solar radiation variability for this application are the following:
- Solar radiation variables for the variability assessment: DNI.
- Time resolution: hourly.
- Spatial resolution: on site data.
- Period of records: >10 years.
|Proyect developers needs at diferent plant stages||Other users|
|Category of product service||(A) Pre-feasibility||(B) Feasibility & Design||(C) Due Diligence Financing||(D) Plant Accep-tance Test||(E) Systems or Plant Operations||(F) Grid operators||(G) Policy makers||(H) Education / Outreach|
|SOLAR RADIATION VARIABILITY||✅||✅||✅||❌||❌||✅||✅||✅|
DNI variability maps
In the context of a Solar Energy project, the spatial variability of the DNI is mainly used in the pre-feasibility stage to assess the suitability of datasets from nearby locations when a quality dataset on-site is not available.
Time series variability
The time variability is used in different stages of the project and for different time resolutions, from annual to daily resolution. As the stages of the project progress, the resolution of the variability evaluated increases. Figure 9 shows how the variability of a GHI time series at a North American location varies depending on the integration time (Perez et al., 2016).
The relevant features for the solar radiation variability are:
- Solar radiation variables for the variability assessment.
- Time resolution (from annual to hourly).
- Spatial resolution.
- Period of records.