N.H. Reich

Spectrally selective sensors for PV system performance monitoring

The main purpose of PV system performance monitoring is to determine whether the system is operating as expected. This requires measuring the actual output of the system as well as the conditions under which it is operating. Solar radiation intensity in the plane of the array (POA) is by far the most important operating condition and is the basis for calculating performance ratios (PR). However, differences in spectral and directional response between pyranometers and PV modules lead to intraday as well as seasonal fluctuations in the performance ratio, even though the system may be operating without faults or degradation. For c-Si PV systems, a reference cell can be used instead of (or in addition to) a pyranometer to measure POA irradiance. Since reference cells have spectral and directional responses that are similar to PV modules, their output signals correlate better with system output. As a result, it is becomes easier to identify abnormal system operation. In this paper, we present a practical alternative to using a spectrally matched reference cell for measuring POA irradiance. The method uses dual sensors with different spectral responses whose outputs are combined to produce a composite signal. That composite signal can effectively match the spectral response of any PV module type over a wide range of irradiance conditions. As a result, this method makes possible the rapid and accurate detection of abnormal system operation for thin-film systems.

System performance analysis and estimation of degradation rates based on 500 years of monitoring data

Fraunhofer ISE has been involved in monitoring of PV systems since the “1000-roofs-program” in the 1990s. In a few of these “old systems” equipment is still in place, metering PV electricity output and plane-of-array irradiation. The majority of ~300 PV power plants in our monitoring campaign today, however, is large-scale and built in the past 10 years. In this paper, we briefly review the historical development of the Performance Ratio (PR) and how average PR for newly built systems increased to almost 90%. This rather high PR of 90% only holds, however, if calculated by on-site irradiation acquired with c-Si reference cells and for climates comparable with those in Germany. Next, we use about 500 years of monitoring data on aggregate to perform an analysis of variations of the PR over time. To this end, data points at similar environmental conditions are extracted from long-term time series as to calculate so-called “rates-of-change” of the PR. Highly scattered “rates-of-change” are obtained, however, not allowing for the estimation of specific degradation rates on the system level yet. To this end, we finally revisit uncertainties of irradiance sensors and in particular revisit our irradiance sensor re-calibration data.

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.