Variable power outputs are one of the largest challenges facing the widespread adoption of renewable energy systems. The inherent variability of solar resources makes it challenging to integrate large amounts of solar energy into the electric grid. Furthermore, the weather factors that influence solar production are often very local in nature. Previous studies have demonstrated that the overall variability of the output can be reduced by aggregating the outputs of wind turbine systems over a large geographical area. The current study seeks to use real-world solar system output data from various locations in central Illinois to demonstrate the degree to which the same is true for solar photovoltaic systems. This topic is of great significance to the future widespread integration of solar energy, and it therefore made an excellent independent research project topic for two students who were completing their degrees in areas related to renewable energy. The work presented in this paper is a collaboration between independent research study projects from two different students guided by two faculty members. An undergraduate student began this work in the spring semester of 2019, and a graduate student completed the work in the fall of 2019. Eleven solar photovoltaic systems with publicly-accessible historical data were identified for analysis in this study. The systems are spread out within a circle with a diameter of approximately 80 miles. The historical power output data for each system over the previous one to four years have been acquired, and quality control measures have been applied. A comparison is made between the variability of the time-varying power output from individual systems compared to the variability of the time-varying aggregated output of the eleven systems combined. Next, the effect of increasing the geographical spread of the aggregated systems is investigated. This is done by comparing the variability of the aggregated time-varying power output from four closely-spaced systems against the variability of the aggregated time-varying power output from four systems spread out over a large geographical area. Finally, the correlations between the outputs from each of the individual systems are explored. The data show that systems located far apart exhibit lower correlation values than systems located close together. Lower correlation values are beneficial for electric grid integration because the aggregation of multiple systems with low correlation values will result in a combined system power output with lower variability.
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