Spectrally Selective Sensors for PV System Performance Monitoring

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component nor on system level. One key figure to assess a PV plant as a whole is the. Performance Ratio (PR), an internationally introduced measure for the ...

All about PV Power Plants: Challenges for technical bankability Boris Farnung1, Björn Müller1, Peter Bostock², John Sedgwick² and Klaus Kiefer1 1 Fraunhofer ISE, Freiburg, Germany ²VDE Americas, San José, USA Abstract — The Photovoltaic market is growing, rapidly, and globally. Ongoing R&D coupled with economies of scale drives cost reduction and efficiency. Competitive price levels of PV power plants have led to solar energy being less dependent on government support to provide attractive levels of investor returns. More than ever, the global PV market provides attractive new investment opportunities. However, the elements driving such rapid expansion also increase the risk of solar financial assets meeting longterm fiscal and performance goals. In order to achieve bankability and differentiation, a premium level of certification and quality assurance at the system level are required. Importantly, it is necessary to go beyond the standards with new and customized quality assurance products that address quality on a system-wide level. Such an approach leads to lower technical risk and increased trust and confidence for a PV system as a secure investment. Real world experience highlights the importance of system design, proper planning, engineering, component selection and construction work for successful PV systems. Thus, comprehensive quality assurance for PV power plants covers all phases of the completion process from the planning to system operation and maintenance [1]. This work addresses major quality assurance measures and its challenges today to achieve bankability of utility scale PV plants.

a declining trust of investors in the reliability of photovoltaic power plants. Beside the modules, the inverter as interface between generator and grid is an essential component with regard to reliability and technical bankability. Based on efficiency, availability and long term repair or replacement cost the inverter can decide between success and failure of an investment. Even if an inverter itself passed tests according to all current standards, the conditions at a specific location may cause noticeable yield losses. For instance, a noisy grid or interaction with other inverters can cause problems in the field. Thus, for the technical bankability of a system, looking not only at single components is of utmost importance. Performance ratio (PR) and levelized cost of energy (LCOE) are the key figures to evaluate the quality of large scale PV power plants. Recently developed approaches allow for an independent assessment both of component and of design quality in order to maintain best figures of PR and LCOE over the system’s lifetime. II. QUALITY ASSURANCE FOR PV PLANTS

I. INTRODUCTION In general, technical risks arise from components, construction and operation of PV power plants. Over past years, the modules have been the key component for bankability. Now, as the investment share for the modules decreased, the inverters, other BOS components as well as the system as a whole get more into focus. Today, quality of large-scale PV plants is also differentiated by design and construction. On the other hand, components such as modules are produced under enormous cost pressure, at different locations worldwide, with frequently changing Bill of Materials (BOM), but indicated as the same module type. These trends are additionally challenging the quality assurance for the manufacturers as well as for the customer. Furthermore, recently observed failure mechanisms such as potential induced degradation, micro-cracks, snail-trails, discolorations, etc. may lead to

Utility scale PV Plants today are multi MW Plants from 10 MWp up to 500 MWp. That means, the quality assurance has to cover millions of modules, installed on miles of metal rails, connected with bunches of cables to hundreds of inverters over an area of thousands of acres. That makes clear why quality of large-scale PV plants is also differentiated by design and manufacturing. State of the art system engineering requires standardized plant units with sophisticated design for efficient and flawless construction since 100% testing is possible neither on component nor on system level. One key figure to assess a PV plant as a whole is the Performance Ratio (PR), an internationally introduced measure for the degree of utilization of an entire PV system [2]. The PR is defined in IEC 61724 and can be derived directly from plane-of-array irradiance (GPOA) and produced AC energy. Thus, it indicates the efficiency of system operation taking into account losses on the PV system’s rated output due to temperature, incomplete use

of the irradiation (soiling, spectral or reflection losses), component efficiencies or failures. Another key figure is the levelized cost of Energy, the sum of all costs from construction to operation and maintenance compared to the sum of produced energy (1).


cost of produced electric energy (1) produced electric energy

Is the equation of the levelized cost analyzed in detail (2), several quality sensitive parameters can be found. t ( 1+ i) I 0 + C0 ∑ t t =1 (1 + r ) LCOE = n (1 + d )t RP ⋅η STC ⋅ E y ∑ t t =1 (1 + r ) n


The parameters are (with quality sensitive highlighted in bold): initial investment for power plant • I0 annual operation & maintenance cost • C0 • n service life • i annual inflation rate • r annual discount rate initial Performance Ratio (PR) of • RP power plant • ηSTC initial module efficiency (STC) • EY energy irradiated on module plane (POA) • d annual degradation rate To ensure the levelized costs of Energy and thus the return of investment (ROI), the quality sensitive parameters have to be predicted as accurate as possible (e.g. Ey) or guaranteed as stable (e.g. ηSTC or Rp).

for quality assurance testing in different project phases is shown in Figure 1. In the following major quality assurance measures will be introduced and relating challenges to assess utility scale PV plants addressed. III. THE BASIS – ACCURATE YIELD ASSESSMENT A primary practical challenge is that a PV system is realized as assumed in the yield prediction. Experience shows that this does not necessarily hold if no acceptance tests are performed [10]. These deviations may not have an influence on the expected energy yield at the first look, but they could (e.g. a decrease in installed power may be realized by reducing inter row distances and so increasing shading losses). A yield prediction without any check that the system is built as expected is more than less worthless. Major input data and their uncertainties are shown in Figure 2. It is obviously, that the weather data has the highest contribution to the uncertainty budget for yield prediction. State of the art yield predictions usually use satellite derived irradiance time series as a basis for system modelling. The quality of these time series was considerably improved over the last 10 years. This applies to the overall mean bias deviation as well as to the irradiance distribution compared to ground measurements. The mean of the bias deviations computed over multiple locations moves against zero, while for single locations deviations in the range of about 3% can be expected. For a detailed analysis, the reader is referred to [3].

Figure 2: Input parameters for yield predictions and typical uncertainties

Figure 1: Quality assurance for different phases of a project.

For the quality of PV plants the appropriate quality measure can be derived from this key figure. An example

A recent topic for the quality of yield predictions, which possibly has gained insufficient attention up to now, is the existence of long-term trends in solar irradiance, which could influence expected energy yields. These multi-decadal trends are known as “global

dimming and brightening” [4-6] and are observed in most parts of the world (with different extents). In general after a dimming phase from the 1950s to the 1980s, a brightening phase started at the mid- 1980s. In [7] the influences of these trends on solar resource assessments are analyzed for Germany. Resulting uncertainties of about 4-5% are estimated for irradiance in south facing planes with 30° tilt angles. For recent solar resource assessments an increase of up to 5% percent is expected, when only the last 10 years of irradiance data are used for the estimation. An expansion of these findings to other parts of the world is still missing. PV system modelling itself seems to introduce relatively low overall uncertainties (at least for time periods of a year or more) [8], [2]. Other modelling steps that may introduce higher uncertainties under certain conditions are shading and soiling losses. Also the calculation of the effective irradiance that is received by the module (angle of incidence effects, spectrum) is up to now not fully understood. However, at least for silicon modules it seems that the overall effect can be estimated by using relatively simple models and the deviations are within measurement uncertainties [8]. Big challenges with input parameters exist for PV modules with regard to the behavior of PV modules at conditions different from STC. It has been shown that data sheet information on low light behavior of PV modules is usually not sufficient for reliable yield assessing [9]. Therefore the parameters used for yield prediction should be determined independently by application of the power rating standard IEC 61853-1, or by measurements of temperature coefficients at 1000 W/m² and low light behavior at 25°C. Usually, the characteristics are measured for several modules. Figure 3 shows an e.g. for an evaluation of the measured low light behavior for two different manufacturers. For manufacturer 1, the nominal values are in excellent agreement with the mean value (average of 5 modules) measured in the laboratory. For manufacture 2, there are strong deviations between nominal and measured values. Such deviations can lead to significant overestimation of the yield [9, 10]. The same evaluation was done for the temperature coefficients as shown in Figure 4. Especially the temperature coefficients given for the open circuit voltage (Voc) have shown large deviation to the expected range (90% quantile). The 90% quantile, shown as green area in Figure 3 and Figure 4, represent more than 100 measurements performed at Fraunhofer ISE in the last two years. This data allows a first validation of data sheet and manufacturer data without any measurement before the data is take as input data for yield predictions.

Figure 3: Measured irradiance dependency (average of 5 modules) compared to nominal and typical values (typical=range covered by 90% of all modules measured at Fraunhofer ISE during the last year).

Figure 4: Measured temperature dependency compared to nominal and typical values (typical=range covered by 90% of all modules measured at Fraunhofer ISE during the last year).

Besides a validation of the input data by Laboratory measurements, confidence in a yield prediction can be increased by on-site testing and performance evaluation as described in section IV. III. LABORATORY TESTING Laboratory testing is valuable at different steps of the project, starting at planning and design phase as shown in the previous section. But planning and design phase is also where the cornerstone is laid for confidence in the product. A quality benchmarking, with pre-defined quality criteria, will help to • exclude systematic underperformance • provide independent parameters for yield assessments • detect sensitivity of modules to known failure mechanisms (e.g. snail trails, yellowing, potential induced degradation, …) • compare the products to state of the art results The final testing procedure, especially for the reliability testing should be derived from the quality criteria of the customer, the experiences from the field as well as the environmental conditions (installation site, system layout, etc…). The goal of the Laboratory testing is not to repeat

testing of the standards without any possibility to extrapolate the data to an estimated lifetime. The goal must be to prevent known failure mechanisms occurring in the field (e.g. snail trails, yellowing, PID, …) and to gain confidence that this module type is not sensitive to these degradation mechanisms. During implementation, an independent performance check of the modules is recommended to prevent a systematic underperformance of the purchased module lot. In this process, the flash list values should be evaluated based on a selected sample. It is of particular importance to select modules from different time serial number and power ranges as shown in Figure 5.

Figure 5: Evaluation of a flash list (15,000 modules) based on a small sample. p:all – power value of all modules in this flashlist, p:ms – selected modules for measurement, ise1 – power valule of the selected module “out of the box”, ise2 – power value of the selected module after LID (20 kWh sun exposure).

Often a specific number of modules is required for testing by the bank or investor. To simplify matters modules are selected randomly. In most cases that means, if 50 modules are required, two boxes are sent to the laboratory testing without specific selection. In this case, most of the modules are from the same serial number range and thus form the same time frame of production. An example is shown in Figure 6. This example shows clearly the small range. Actually there is no value in measuring 25 modules from one box with regard to prevent a systematic underperformance of the total quantity of purchased modules. To exclude the risk of a systematic underperformance of the modules, the sampling needs to be done carefully and the evaluation of the results requires a high accuracy. At Fraunhofer ISE, measurements are carried out with an industry leading uncertainty of 1.8% [11] in the case of crystalline modules or slightly greater uncertainty for thin film.

Figure 6: Randomly selected modules. Two boxes selected from three containers (520 modules each).

For the evaluation it is furthermore essential to take into account initial effects with impact on the performance in the field. Crystalline modules lose up to 3% of their power in the first hours of operation [12]. This degradation is usually within 10 to 20 kWh/m² completed and the module stabilized. In accordance to the standard DIN EN50380:2003-09 „Datasheet and nameplate information for photovoltaic modules“ the modules must comply with rated power at STC given on the nameplate and data sheet after pre-conditioning with ≥ 20 kWh/m². For thin film PV modules, determining the power representative for field operation demands technology specific know how [12] [13]. Depending on technology, initial degradation or dark storage effects alter power. Preconditioning procedures have to be applied prior to the I-V curve measurement to bring the module to a state representative for field operation (CIGS, CdTe). IV. SYSTEM TESTING Most of the acceptance testing, initial performance and safety evaluation or plant certification takes place in the commissioning phase of a project. As mentioned before, the performance ratio (PR) is a key figure to assess a PV plant as a whole. The PR indicates how well a PV system performs. Beside a visual inspection, safety and component testing, the actual PR of the system should be validated. By comparing actual (measured) and expected (simulated) PR, one can obtain valuable information on whether the system performs as expected. Important input data for the calculation of PR are the actual irradiance and the system output. Thus, both values have to be measured accurately during operation. It has been observed that in many cases unreliable and inaccurate measurement equipment is used, however.

With this approach, available monitoring data is validated by comparison with calibrated and high-quality measurement equipment that is installed for a defined time frame. If required, monitoring data is corrected to the calibrated instruments. Higher accuracy can be reached if the actual power loss due to soiling is measured. The tests are conducted on selected strings with and without soiling and cleaning procedures are reviewed to provide an estimate of the impact of soiling for a specific site. After validation and correction, existing monitoring can be used to determine the actual Performance Ratio (PR measured). For comparison with expected PR (PR modelled), the measured weather data (irradiance, temperature) are used to simulate the PR with an established procedure and the system model and parameters from the original yield prediction.

PV plants worldwide. It has been shown, that the performance can be accurately evaluated within just a few weeks and as the plants monitoring system is validated, third party evaluation of existing and future yield data is possible.

Figure 8: Comparison of actual and nominal PR values for July 2013 for a utility scale PV system in southern Spain. On 24th, a failure in the system caused a drop of PR by almost 10%.

Another important aspect is that performance evaluation covers all components of the PV and their behavior. Especially the inverter, as interface between generator and grid is an essential component with regard to reliability and technical bankability. Based on efficiency, availability and long term repair or replacement cost the inverter can decide between success and failure of an investment. Even if an inverter itself passed tests according to all current standards, the conditions at a specific location may cause noticeable yield losses. For instance the operation of hundreds of inverters in parallel and the interaction with other inverters or a noisy grid o can cause problems in the field. Thus, for the technical bankability of a system, looking not only at single components is of utmost importance. V. EXPERIENCES OF PV PLANTS IN OPERATION

Figure 7: Process for performance verification. On-Site measured and validated irradiance and temperature data is used for (1) calculation of the expected (modelled) yield by using a plant model as well as (2) calculation of the actual (measured) plant PR based on the data from the energy meter.

This procedure was developed over the last years and corresponds to the relevant state of science and technology. Among all the specific plant parameters (e.g. inverter efficiency, cable losses, …) the results from power rating measurements performed at modules selected in the plant are included in the model. Over the last years, this procedure for performance verification has been applied successfully for utility scale

Appropriate monitoring and control of operation is mandatory for commercial and utility scale PV plants. Failures during operation have to be detected by sure methods to avoid major yield losses. But accurate monitoring also shows if the performance is stable which will ensure the ROI and is in addition basic data for track records of the system layout, workmanship and used components. Therefore, independent third performance reports are required for the bankability of projects. Benchmarking of the more than 300 Fraunhofer ISE monitored PV Plants has shown annual PR’s for the year 2013 between 60 and ~90% (Figure 9). For most new PV plants with basic initial quality assurance and continuous O&M contracts PR’s from > 80% were reported. In central Europe initial PR’s > 85% can be expected for high quality PV plants today.

degree of certainty. Thus, it gives a clearer picture of the financial returns of a system. For component suppliers and system integrators, quality can help to achieve differentiation in the competitive market, where the various stakeholders involved now have different criteria for evaluating investment in projects. Finally, technical bankability is attractiveness of a project from the perspective of the financing institution. If in the past assessments of bankability were often derived from the particular components selected, today the quality of the Plant as a whole is more and more important. Figure 9: Measured performance ratios for 300 PV plants by Fraunhofer ISE. Blue bars represent new plants with basic initial quality assurance and continuous O&M.

But also for plants in operation for 15 to 20 years PR’s of 75 to 80 % were found. The performance development of a 4.88 kWp plant in operation since 1993 in the northern part of Germany is shown in Figure 10. This system has an average PR of 77 % for the last 20 years and very small variations from year to year of just ± 2.7 %.







Figure 10: PR of a 4.88 kWp system installed in the in the northern part of Germany. This system is in operation for 20 years now.

And there are many other examples showing, that Solar Energy today is a reliable source of energy if appropriate quality assurance measures are applied. VI. CONCLUSIONS Quality is the core factor for technical bankability. This implies state-of-the art system design and standardization. Appropriate quality assurance measures such as plant certification reduces technical risk of component or system failure and validates performance with a higher



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