• Energy Research

PV system modeling is primarily done on hourly timescales and so cannot capture subhourly effects, including inverter saturation. Inverter saturation occurs when the potential dc power, Pdc, produced by the collectors is greater than the inverter capacity, and some of the PV power is lost or “clipped.” The inverter clips power rapidly, and calculations based on hour-averaged Pdc will overestimate ac power output for hours in which clipping occurs intermittently. Clipping is greater in systems with high ratios of collectors to inverter capacity, “dc:ac ratio.” We studied this modeling error using minute-scale Pdc measurements from a test site in Birmingham AL that had four different mounting configurations equipped with oversized inverters; i.e., 0.8 dc:ac ratio. PV output was calculated using minute- and hour-averaged Pdc and modeled inverters with dc:ac ratios up to 2.0. The modeling errors due to short term inverter saturation were approximately 2% of the annual output for 1.4 dc:ac ratios. We present the effect of mounting type, dc:ac ratio, season, and hour-of-day on these errors for the study site.

PV system modeling is primarily done on hourly timescales and so cannot capture subhourly effects, including inverter saturation. Inverter saturation occurs when the potential dc power, Pdc, produced by the collectors is greater than the inverter capacity, and some of the PV power is lost or “clipped.” The inverter clips power rapidly, and calculations based on hour-averaged Pdc will overestimate ac power output for hours in which clipping occurs intermittently. Clipping is greater in systems with high ratios of collectors to inverter capacity, “dc:ac ratio.” We studied this modeling error using minute-scale Pdc measurements from a test site in Birmingham AL that had four different mounting configurations equipped with oversized inverters; i.e., 0.8 dc:ac ratio. PV output was calculated using minute- and hour-averaged Pdc and modeled inverters with dc:ac ratios up to 2.0. The modeling errors due to short term inverter saturation were approximately 2% of the annual output for 1.4 dc:ac ratios. We present the effect of mounting type, dc:ac ratio, season, and hour-of-day on these errors for the study site.

AbstractThe Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane‐of‐array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

AbstractThe Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane‐of‐array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.