Björn Müller

Angle Dependence of Solar Cells and Modules: The Role of Cell Texturization

The angle-dependent spectral response is measured for six differently textured silicon solar cells before and after encapsulation. Deviations from Lamberts cosine law and differences due to the textures can be clearly determined by means of an uncertainty analysis. This effort in highly accurate angle-dependent measurement is needed for reliable prediction of annual performance. Energy rating using common simulation models for three different climate zones shows differences of more than 1% in yearly energy yield solely due to the different angle-dependent behavior of the textures. Modules featuring solar cells with random upright pyramids exhibit the largest angular losses of the studied textures.

Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior

The installed capacity of PV plants has increased dramatically in the past years. A common approach to determine the actual power of an ensemble of PV systems within a specific region typically employs data from measured reference plants. Obviously the precision of the power estimation depends on having representative reference plants, which are not influenced by strong individual characteristics. The goal of this contribution is to detect such apparently atypical behavior of PV systems by comparing their measured power to simulations based on a nearby weather station and clear sky irradiance. Deviations are studied in the course of each day for the year 2012 and 48 PV systems, indicating systematic characteristics independent from meteorological conditions. Additionally, an approach is presented to detect such unexpected deviations automatically. This can be the basis for a dynamic nowcasting algorithm, which selects the reference units based on their (temporal) suitability.

Investment risks of utility-scale PV: Opportunities and limitations of risk mitigation strategies to reduce uncertainties of energy yield predictions

Investment risks of utility-scale PV systems may arise from a wide range of sources: political stability in a region, interest rate levels and currency exchange rates or future energy price. However, the presence of stable political and economic conditions and feed-in tariffs or power purchase agreements may limit interest and price risks to acceptable levels. The technical risk of deviations between expected and actual life-time energy yield of a PV power plant is mostly influenced by the quality of energy yield predictions in case that system components correspond to their datasheet and guaranteed values and the maintenance concept is applied as expected. Recent publications estimate the standard uncertainty of life-time energy yield predictions to about 8%, which directly contributes to overall investment risk. In this paper we analyze two different strategies to reduce the influence of uncertainties of energy yield predictions on investment risks. The first strategy is diversification of risk, i.e. investing in a portfolio of systems. The second strategy is related to adjusted investment periods. It is concluded, that both strategies as well as the combination of these strategies are able to significantly reduce uncertainties. The resulting uncertainty of the lifetime energy yield for the combination of both approaches is estimated to about 3%.

On-site performance verification to reduce yield prediction uncertainties

In this paper, we describe a number of quality assurance procedures for PV performance evaluations using data that has been acquired with commercially operating PV power plants. Summarized under the term “performance verification”, these procedures aim to make reliable use of data gathered by PV operators, despite inaccuracies in such data. To this end, short-term measurements with independent equipment and/or data sources are factored in. This ensures both meteorological and PV performance data gathered by operators have sufficient quality. Given a sufficient quality, site-adaptation of satellite based solar irradiation time series and optimizations of PV models may allow for significantly reduced uncertainties of yield predictions. This we anticipate to be of great interest to a broad range of PV stakeholders. The prerequisite for this, however, are appropriate quality of on-site sensors and strict maintenance of the PV performance monitoring systems in place.