PV Extreme Capacity Factor Analysis

This paper proposes a novel approach based on k-means clustering and extreme value theory (EVT) to spatiotemporally analyze photovoltaic (PV) extreme capacity factor (ECF). Through correlation coefficient analysis, the effects of meteorological factors on PV output are quantified into different weights. These weights are then used in a k-means clustering solver to partition the utility service territory into k geographical zones such that PV systems within each individual zone will behave similarly in terms of peak capacity factors. The processes involved are presented in great detail such that the correlation coefficients between PV output and meteorological variables are calculated; weights and normalized meteorological variables are calculated; representative PV and weather data are selected; and the value of k is determined. Extreme value theory is subsequently utilized to obtain the probabilistic distribution of the ECFs for PV systems located in a specific zone within a specific time interval. A case study based on the PV and weather data in the State of Connecticut is presented to validate the effectiveness and efficiency of the proposed approach.