Quantification of solar variability

Weather and cloud movements cause fluctuations in solar energy resources on short time scales ranging from seconds to tens of minutes. see picture 1. Sun motion and cloud cover changes are the main causes of variability. We can accurately predict variability due to the motion of the sun, but not the variability due to cloud motion. Solar geometry informs our predictions: the movement of the sun across the sky causes changes in resources. These changes are not noticeable for short periods of time (seconds to minutes), but can have significant effects over long periods of time, especially around sunrise and sunset. The “impact” of cloud movement and change is the hard-to-predict part of the variability.

Short-term variability is related to the operation of solar power plants and also has an impact on the solar power plants and the grid connected to them: even a small cloud passing in front of the sun can cause severe shocks in the load of the photovoltaic system in a very short period of time, thus Severe damage to the system is a problem that the grid operator is very concerned about. There is a view that the variability of solar power generation shown in Figure 1 can lead to serious problems in electricity distribution and transmission networks.

Figure 1 - Selected horizontal global radiation GHI and clear sky radiation GHI for a day with high variability and a time scale of 20S (data from the ARM Extended Facility Network in Oklahoma)
Figure 1 – Selected horizontal global radiation GHI and clear sky radiation GHI for a day with high variability and a time scale of 20S (data from the ARM Extended Facility Network in Oklahoma)

Short-term variability was studied by Skartveit and Olseth (1992). Their knowledge of short-term variability and parameter settings have long provided one of the few references on this topic. This situation did not improve until research on photovoltaic topics became increasingly popular (initially in Europe) (Wiemken et al., 2001, Woyte et al., 2007). Over the past few years, this topic has spawned a large number of new studies (e.g. Frank et al., 2011; Hinkelman et al., 2011; Hoff and Perez, 2010, 2012; Hof, 2011; Jamaly et al., 2012; Kankiewicz et al, 2011; Kuszamaul et al, 2010; Lave and Kleissl, 2010, 2013; Lave et al, 2011, 2012; Mills and Wiser, 2010; Mills et al, 2009; Murata et al, 2009; Norris and Hoff, 2011 ; Perez and Hoff, 2011; Perez et al., 2011a, 2011b; Perex and Fthenakis, 2012; Sengupta, 2011; Stein et al., 2011).

We often use ramp rates to describe the variability of solar energy. It is derived from the power industry and is used to describe the off-grid and on-grid operation of power plants based on demand (increase or decrease). It is widely used in the wind power industry to describe the sudden uncontrollable on-grid or off-grid operation of a large number of installations due to local changes in wind speed, such as those caused by fronts. Similar to the wind power ramp rate, it applies to the upper-limit longer time scale of the field covered in this chapter, that is, under the influence of the front, the local power output may rise or fall for 1 h or even longer. However, to describe short-term changes over a few seconds to minutes as shown in Figure 1, the term fluctuation may be more appropriate.

  1. Quantification of solar variability

Reasonable quantification of variability needs to determine: ① the physical quantity of change; ② the time interval of the quantitative change; ③ the time period of change involved.

The physical quantity (P) of the power output of a solar system or combination of solar systems is a matter of greatest concern to energy manufacturers and grid providers. P is a function of solar generator specification and solar resource. We typically measure the solar energy resource of a non-focused flat-panel ① solar system structure using the total horizontal irradiance (GHI). Short-term GHI variability is the effect of predictable factors due to changes in the sun’s position and unpredictable factors due to weather/cloud cover. We use the clear sky index (kt*) (the ratio of GHI to GHIclear②) to represent the influence of unpredictable factors.

Since we can infer GH1 from kt* and the sun position, which in turn infers P, this parameter requires our attention. The time interval refers to the change time (Δt) of the selected physical quantity. It can range from a few seconds to a few hours, depending on the specific aspect of the user. As shown below, the relevant time intervals are closely related to the geographic footprint of the solar resource under study, i.e. the impact of the solar resource on the transformers on the distribution lines and the grid in the local control area.

Time period refers to the number of time intervals over which we define variability; that is, it is a multiple of Δt. A measure of variability at a single site refers to the standard deviation of power output variation. This variability is proportional to the change in the clear sky index at a given time interval at a location over all given time periods (Hoff and Perez, 2010). That is, (power output) variability is proportional to it.

Read more: What is Concentrated Solar Energy (CSP)?

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