Solar resource variability

Overview

Statistics provide a detailed understanding of solar energy availability, helping solar project stakeholders make informed decisions about site selection, system design, and technology choices. These insights enable better financial models and performance predictions over the lifecycle of solar power plants.

Interannual variability, driven by cyclical weather patterns and stochastic variations, results in annual solar radiation deviating by a few percent from long-term averages.

Statistical analyses of yearly and monthly data provide valuable information on trends, extremes, and seasonal variations, enabling stakeholders to identify patterns and anticipate potential challenges.

Additionally, detailed evaluations of daily, hourly, and sub-hourly data ensure energy production aligns with consumption patterns while addressing fluctuations caused by short-term weather events. This multi-scale analysis supports robust system optimization and enhances the reliability of solar energy generation.

Interannual variability

Interannual variability quantifies the year-to-year fluctuations in solar irradiance at a specific location, typically expressed as a percentage of the long-term average. These fluctuations result from natural weather cycles and stochastic variations, causing annual solar radiation to deviate by a few percent from the mean. Understanding interannual variability is essential for developers and investors to assess the reliability and predictability of solar resources over the long term.

For a specific solar project, interannual variability is calculated using the historical series of annual solar irradiance or PV output values. While variability is often calculated for a single year, the analysis can extend to longer periods, such as N years, to provide insights into multi-year performance trends. Assuming that interannual variability follows a normal distribution, the following steps are commonly used:

1. Calculate the standard deviation of annual values over the available data period.

2. Divide the standard deviation by the square root of N (for a single year, N=1).

3. Divide the result by the average value of the dataset to express it as a percentage.

4. Convert the value to any desired PXX confidence level, such as P90, by multiplying it by the appropriate factor (e.g., 1.282 for P90).

Interannual variability can be calculated for solar irradiance or expected PV output. For PV output, the calculation should also follow the steps described above. However, a simplified approach assumes that the interannual variability of PV output is identical to that of solar irradiance. This approximation has limitations, as it neglects the effects of factors like cell temperature, system losses, and non-linearities caused by partial shading of modules.

Table of GHI interannual variability of a period of 1, 5, 10, and 25 years for several sample sites

Sum, average, max, and min

The analysis and representation of solar irradiance and meteorological parameters through various statistical summaries and visualizations provide valuable insights into solar power development.

Yearly and monthly irradiation sums are essential for creating accurate financial models, including revenue projections, return on investment (ROI), and payback periods. Facilitates comparisons across different locations or years, helping to identify the best-performing sites and assess long-term trends in solar resource availability. Minimum and maximum sums show the range of solar irradiance values highlighting the variability of solar energy availability and helping developers assess the predictability of solar resources, which is crucial for financial modeling and risk assessment.

Understanding the extremes of operating conditions (both low and high) is also required for designing the system to handle the worst-case scenarios, ensuring reliability and preventing over/under-sizing. On the other hand, identifying periods with potential low irradiance can be used to plan maintenance schedules or storage needs.

Analyzing the average hourly and sub-hourly irradiation throughout the day for each month with built daily profiles helps understand the typical daily solar radiation patterns across different months. It offers a precise view of how solar irradiance varies throughout the day and year, which is essential for optimizing system performance and ensuring that energy production matches consumption patterns. Looking at daily profiles also supports the sizing of energy storage systems, ensuring enough capacity to store excess energy during peak sunlight hours and discharge it when irradiance is low.

Daily, hourly, and sub-hourly fluctuations

Looking at daily, hourly, and sub-hourly data frequency and fluctuations allows a detailed and statistical view of solar energy distribution over time.

This type of analysis, typically done through the representation of histograms and frequency distribution charts, can provide insight into the most common levels of daily solar exposure. Knowing how many days in a year are likely to have high, medium, or low irradiance allows for better long-term planning and helps developers assess the probability of meeting energy production targets and to plan for periods of lower-than-expected output.

Analysis of fluctuations, typically caused by factors such as passing clouds or weather events,  helps in designing robust systems that can handle the most frequent irradiance levels, while also accommodating occasional extreme values and ensuring that systems are ready for sudden drops or spikes in energy production.

It also helps manage the risk of curtailment (when excess energy cannot be used or stored), allowing for better planning of when to curtail production and minimize losses.