Prospect
光伏站点太阳能潜力快速评估
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Branislav Schnierer
Aug 8, 2023
No photovoltaic (PV) power plant designer sets out to design an underperforming project. However, decision making during the early design phase of a solar PV plant is often reliant upon imperfect information, which increases the technical and financial risks of a project, shortens its lifetime, and reduces its efficiency.
To design optimal PV projects, designers must consult 1-minute data which paint the most accurate picture of a plants’ PV power potential and output, while providing increased financial certainty to solar investors.
In this blog, we outline 4 reasons why PV project designers must use 1-minute data during the design phase of a solar plant. All simulated values presented in charts are based on 1-minute stochastic data, prepared by the SYNGEN algorithm, 15-minute satellite data and 60-minute aggregated satellite data.
PV modules rarely produce power equal to their output capacity. Therefore, it is common practice and often economically advantageous to size an inverter to have lower performance than the PV array.
The ratio of a PV module's maximum power reported at Standard Test Conditions (STC) to maximum AC power rating of inverter (s) at reference conditions is measured as the DC/AC ratio. Optimal DC to AC ratio values depend on PV module technology and mounting, inverter efficiency, geographic location, a level of radiation and temperature.
Influenced by imperfect information during the design phase, many solar PV projects incorporate unnecessarily large inverters, which adds to the overall costs of a project.
As can be seen in figure 1.1, 1-min solar data provide a more accurate reading of produced PV electricity (PVout) when compared to 15-min and 60-min data. 1-min data therefore facilitate more accurate decisions on inverter purchases, reducing overall project costs.
Figure 1.1: Simulated PVout during a specific day: calculation based on 1-min, 15-min and 60-min input data
Simulated annual electrical energy at the point of connection (POC) based on 15-min and 60-min data might lead to an overestimation of electrical energy by about several percent in comparison with the calculation based on 1-min data (Figure 1.2).
Figure 1.2: Difference between simulated annual electrical energy at POC based on 15-min and 60-min input data in comparison to 1-min input data
When the DC/AC ratio of a solar system is too high, the likelihood of the PV array producing more power than the inverter can handle, increases.
Consequently, the inverter will drop the power output in a process called clipping. For example, a DC/AC ratio of 1.5 will likely see clipping losses of 2-5 percent. In some cases, this is even more, impacting the overall efficiency and financial performance of a project.
In most cases, PV simulations typically underestimate clipping losses when using 15-min or 60-min data – a concerning trend that’s becoming more common with larger DC to AC ratios.
1-min data, as demonstrated by figure 1.1, not only help designers accurately capture a solar plant’s optimal DC/AC ratio, but, in turn, refine that ratio to reduce financial and power losses caused by frequent clipping.
Annual clipping losses for different DC to AC ratio calculated based on different time resolution can be seen in figure 2.1.
Figure 2.1: Annual clipping losses for different DC to AC ratio and time resolution
Because 1-min data provide more accurate solar prediction of power output and insight into power fluctuations, they facilitate a more harmonious relationship between PV systems and grid operators’ technical standards and regulations. Generally, power quality issues relating to grid connected PV systems include:
In comparison to hourly or 15-min data, as shown in figure 1.1, 1-min data can accurately show PV power fluctuations. 1-minute data, represented in orange, precisely captures the shifts in PV output, providing grid operators with an accurate representation of power available.
In turn, 1-min data allow designers to better understand what equipment will be necessary for a project, helping reduce the liabilities, fees and other charges that can arise if grid-connected systems do not meet strict grid requirements.
In the same vein, 1-min data help designers design systems that provide ancillary services, which help grid operators ensure a reliable supply of quality electricity to their consumers.
A possible solution to mitigate power fluctuations is to use a suitable system (e.g., energy storage system) to smoothen out the PVout variations. To ensure effective smoothing control, it is necessary to take into account rapid changes in PVout. Here, 1-minute data play a crucial role. An example of Pvout smoothing to reduce power variations at POC is presented in figure 3.1.
Figure 3.1: Example of PVout smoothing to reduce power variations at POC
Finally, 1-min data facilitates better decision making when choosing an appropriate solar PV plant site.
The benefits of this are most acutely felt in areas with frequent occurrence of intermittent clouds. This is because of cloud edge effects, which are a common phenomenon in PV power generation caused by the sudden increase in irradiance due to the reflections of passing clouds. Such situations are typical for the peripheries of atmospheric cyclone systems, which often develops in the tropics, monsoon regions, and also mid-latitudes regions.
By providing a more granular picture of site potential, as well as the solar irradiance fluctuations and variabilities of any given site, 1-min data helps designers pick locations that best unlock a project’s potential to extract the most energy from the sun.
Annual clipping losses for variable DC to AC ratio calculated based on different time resolution is shown for selected sites in figure 4.1. Where a level of variability was calculated via the standard deviation of Global Titled Irradiance (GTI).
Figure 4.1: Example of clipping losses for sites with different GTI variability and a level of PVout
Difference between predicted annual electrical energy at POC based on 15-min and 60-min input data in comparison to 1-min input data is shown for different sites in figure 4.2.
The higher PVout and GTI variability, the higher percentage clipping losses (figure 4.1). The highest percentage inaccuracy in annual PVout (difference between PVout calculated using 1-minute data and using 15-minute data or 60-minute data) can be seen in the site with middle PVout and middle GTI variability (figure 4.2).
Figure 4.2: Example of differences in PVout at POC for sites with different GTI variability and a level of PVout
To read our 1-min data blog series, please click on one of the links below.
To find more about Solargis’ 1-min data, click here: https://solargis.com/docs/methodology/1-minute-solar-data
