期刊
APPLIED ENERGY
卷 322, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119550
关键词
Rooftop PV systems; Performance evaluation; Energy performance index; Yield prediction
This paper analyzes long-term monitoring data of 246 identical rooftop PV systems and investigates the mechanism of PV performance variations. The study finds that PV performance variability is significant in the real built environment, not only between different locations but also between peer systems in the same neighborhood. To mitigate financial risks and reduce PV yield prediction error, it is recommended to commission and monitor PV systems in the field for at least one month with the largest range of solar elevation.
While the performance of PV systems and its associated uncertainties are well understood for standard test conditions in the laboratory, there is still limited knowledge about the magnitude and mechanism of the PV performance variability under actual operating conditions. This paper aims to identify the performance vari-ability between identical rooftop PV systems in the field and formulate risk mitigation strategies to reduce the error of annual yield prediction. To achieve the aim, long-term monitoring data of 246 identical rooftop PV systems in 19 sub-urban residential communities is analyzed. Through structured side-by-side comparisons, the mechanism of PV performance variations linked to location, module orientation, season, sky clearness, and system age is investigated. It is found that PV performance variability increases in the real built environment compared with the nameplate bandwidth declared by the manufacturers. Significant variation of PV operating performance is observed not only between different locations, but also between peer systems in the same neighborhood. Even in low-rise sub-urban settings, local shading and masking effects play a prominent role and can introduce great uncertainties. Due to the site-to-site and peer-to-peer uniqueness of PV performance, it is inappropriate to employ an identical empirical derate value for all cases. Commissioning and monitoring of PV systems in the field for at least one month with the largest range of solar elevation can significantly reduce PV yield prediction error and mitigate financial risks.
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