New and Improved Methods to Characterize, Classify, and Estimate Daily Sky Conditions for Solar Energy Applications

Abstract

Firstly, this dissertation proposes a new characterization and classification method for daily sky conditions by using the daily sky clearness index (KD) and the daily probability of persistence (POP-KD) that can be derived from ground-based irradiance measurement data. Quality of daily solar irradiance is characterized by a newly proposed parameter, POP-KD. This characterized daily quality is varying and uncertain at the middle level of the quantity, but high and more certain at very high and low quantity levels. In addition, the proposed characterization method shows interesting results for KD and POP-KD: a statistical consistency for multiple years and similarity for their seasonal trends. The classification results also indicate an existence of dominant classes, and transitions between the dominant classes are significant for all locations. This dissertation also generates annual synthetic sequences of KD and POP-KD using a Markov approach. The generated sequences show statistical similarities with observed sequences.

Secondly, this dissertation proposes methodologies to estimate day-ahead solar irradiance using the National Weather Service (NWS) sky cover forecast. For model development, this paper splits up a direct estimation process from the sky cover forecast to solar irradiance into two stages: forecast verification and cloud-to-irradiance conversion. Uncertainty for each stage and for the overall estimation process is quantified. NWS forecast uncertainty (about 20%) is identified as the main source of uncertainty for the overall process. In addition, verification of the sky cover forecast shows approximately 20% overestimated bias at days with a high irradiance level. Thus, the NWS sky cover forecast needs to be adjusted based on the type of day. This dissertation also proposes a conversion equation relating daily quantity of cloud information and daily quantity of solar irradiance. The proposed conversion equation achieves accuracy with simplicity. Five day-ahead solar irradiance quantity estimation methods are proposed in this dissertation. The proposed methods incorporate different schemes for dealing with the bias discovered in the cloud forecast. The observed data are regularly found within the 95% confidence intervals of the estimated values. Estimation results demonstrate the effectiveness of the conditional adjustment schemes at different irradiance levels.

Lastly, this dissertation proposes a methodology to estimate day-ahead solar irradiance using fluctuation information of the NWS sky cover forecast. POP-KD was used as a parameter for the quality of daily solar irradiance. POP-KD efficiently represents the quality of daily solar irradiance. In addition, POP-KD indicates the probability that solar irradiance variability is within the ramp rates of common generators in power systems at a certain photovoltaic penetration level. This dissertation also proposes a new equation for the conversion from cloud fluctuation information to daily quality of surface solar irradiance. The proposed equation achieves accuracy. The proposed day-ahead solar irradiance quality estimation method is based on fluctuation information provided by the NWS sky cover forecast. This method uses a normalization approach to relate fluctuation of cloud forecast and fluctuation of cloud observation. The observed data are regularly found within the 95% CIs of the estimated values.