George Makrides

Performance of Photovoltaics Under Actual Operating Conditions

Amongst the various renewable energy sources, photovoltaic (PV) technologies that convert sunlight directly to electricity have been gaining ground and popularity, especially in countries with high solar irradiation. Over the past years PV has shown rapid development and a wide variety of new technologies from different manufacturers have emerged. For each PV module type, manufacturers provide typical rated performance parameter information which includes, amongst others, the maximum power point (MPP) power, efficiency and temperature coefficients, all at standard test conditions (STC) of solar irradiance 1000 W/m2, air mass (AM) of 1.5 and cell temperature of 25 °C. As this combination of environmental conditions rarely occurs outdoors, manufacturer data-sheet information is not sufficient to accurately predict PV operation under different climatic conditions and outdoor PV performance monitoring and evaluations are necessary. The objective of this chapter is to provide an overview of different PV technologies ranging from crystalline silicon (c-Si) to thin-film and concentrators. Subsequently, a summary of the main outdoor evaluation performance parameters used to describe PV operation and performance is outlined. An overview of the effects of different environmental and operational factors such as solar irradiance, temperature, spectrum and degradation is also provided along with the results of previously published research efforts in this field. In the last section of the chapter, the installed PV and data acquisition infrastructure of a testing facility in Cyprus is presented and a thorough analysis of the climatic conditions and the performance of different grid-connected PV technologies that have been installed side-by-side and exposed to warm climatic conditions, typical of the Mediterranean region are given.

Outdoor efficiency of different photovoltaic systems installed in Cyprus and Germany

The energy produced by different photovoltaic (PV) systems depends to a great extent on the inverter and photovoltaic module outdoor efficiency which in turn vary according to the irradiance and temperature of the field conditions. The objective of this paper is to describe the investigations performed to evaluate the outdoor efficiencies of thirteen (13) different types of PV modules which have been exposed to real conditions in Stuttgart, Germany and Nicosia, Cyprus. The inverter and PV array efficiencies have been evaluated by using direct outdoor measurements and data analysis on the measured metereological and operational data collected by the installed sensors present at the two test sites. The measured efficiency of the PV modules has been further evaluated on a seasonal basis reflecting the obvious effects of the seasonal variations of incident irradiation, operating temperature and seasonal spectral shift on the outdoor operating efficiency. The average annual inverter efficiency measured in Stuttgart was 89.8 % while the corresponding inverter efficiency in Nicosia was 90.9 % for the same period and for identical inverters that are rated at a European Efficiency of 91.6 %.

Degradation of different photovoltaic technologies under field conditions

Over the past years a number of testing facilities have been monitoring the performance and degradation of PV systems according to the established standards of indoor and outdoor testing. The objective of this paper is to present the initial first year and longer-term rate of degradation of different PV technologies installed at the testing facility of the University of Cyprus, based on outdoor field measurements and methodologies. The first year degradation of the technologies was obtained using a data filtering technique of DC generated power at Maximum Power Point (MPP) at irradiation points of higher than 800 W/m2 and normalising the measured power to Standard Test Conditions (STC). Over the first year, mono-crystalline silicon technologies showed degradations in the range 2.12 % – 4.73 % while for multi-crystalline technologies the range was 1.47 % – 2.40 %. The amorphous silicon system demonstrated the highest first year decrease in power with an average degradation of 13.82 %. For validation purposes the first year degradation was also obtained using a second technique by evaluating outdoor measured data-sets under Air Mass (AM) 1.5 (morning and afternoon) conditions and during noon (high irradiance and temperature). In this case the evaluated results showed deviations of up to 6 % and 3 % for mono-crystalline and multi-crystalline technologies respectively whereas for thin-film this was 5 %. Finally, the longer-term degradation rates were evaluated by using the least-square fit method on average monthly data-set blocks of (i) Performance Ratio (PR), (ii) PR evaluated by filtering outage data-sets and restricting to high irradiance conditions and (iii) the Photovoltaic for Utility Systems Applications (PVUSA) rating methods, for the period June 2007 – June 2009.

Rating of annual energy yield more sensitive to reference power than module technology

At present, many different photovoltaic (PV) technologies share the market. Especially investors want to know how much energy each of the PV technologies produces. This paper discusses the measured annual energy yield EAC of twelve PV technologies under different climatic conditions in Germany and Cyprus over three years of operation. In order to compare the annual yield of different PV technologies, the EAC data are normalized to the rated power PN, to the flasher power Pflash, and to the measured field power Pfield. An error analysis is done for both, the energy measurement EAC and the nominal power PSTC. It is found that the typical uncertainty for an energy yield comparison is ±5 %. This means that a difference of 10 % in the annual energy yield between PV technologies can not be traced back to the technologies themselves. The performance analysis of all PV systems shows that the differences in the energy yield are smaller than the error bars on the reference power. Therefore it is not yet possible to decide which PV technology is the best. Moreover, despite obvious trends on the data, we can not unambiguously conclude that PV modules with a better temperature or low light behavior will ensure a higher energy yield in general, since the propagation of state-of-the-art nominal power rating errors outbalances the well recognized effects of low light and temperature dependencies.

ARIMA modeling of the performance of different photovoltaic technologies

In this paper, the performance of different technology photovoltaic (PV) systems was modeled using autoregressive integrated moving average (ARIMA) processes. Measurements from mono-crystalline (mono-c-Si), multi-crystalline (multi-c-Si) and amorphous (a-Si) silicon, cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) systems were used to construct monthly dc performance ratio (PR) time-series, from outdoor measurements. Each PR time-series was modeled a) with multiplicative ARIMA, b) with linear regression and c) with Seasonal-Trend Decomposition by Loess (STL) using the first 4 years of each time-series in order to compare the accuracy of the different methods. The models were used to forecast the PR of the 5th year of the different PV technologies and the results from the aforementioned statistical methods were compared based on the root-mean-square error (RMSE). The results showed that ARIMA produced the lowest RMSE for crystalline silicon (c-Si) technologies, whereas for thin-film technologies, STL was more accurate. The results from ARIMA also showed that thin-film technologies were optimally modeled with identical model orders, whereas for c-Si, each technology required a different optimal model order.

Comparison of trend extraction methods for calculating performance loss rates of different photovoltaic technologies

In this work, the performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated by applying linear regression (LR) and trend extraction methods to Performance Ratio, RP, time series. In particular, model-based methods such as Classical Seasonal Decomposition (CSD), Holt-Winters (HW) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA), as well as non-parametric filtering methods such as LOcally wEighted Scatterplot Smoothing (LOESS) were used to extract the trend from monthly RP time series of the first five years of operation of each PV system. The results showed that applying LR on the time series produced the lowest performance loss rates for most systems, but with significant autocorrelations in the residuals, signifying statistical inaccuracy. The application of CSD and HW significantly reduced the residual autocorrelations as the seasonal component was extracted from the time series, resulting in comparable results for eight out of eleven PV systems, with a mean absolute percentage error (MAPE) of 6.22 % between the performance loss rates calculated from each method. Finally, the optimal use of multiplicative ARIMA resulted in Gaussian white noise (GWN) residuals and the most accurate statistical model of the RP time series. ARIMA produced higher performance loss rates than LR for all technologies, except the amorphous Silicon (a-Si) system. The LOESS non-parametric method produced directly comparable results to multiplicative ARIMA, with a MAPE of -2.04 % between the performance loss rates calculated from each method, whereas LR, CSD and HW showed higher deviation from ARIMA, with MAPE of 25.14 %, -13.71 % and -6.39 %, respectively.

Estimation of annual performance loss rates of grid-connected photovoltaic systems using time series analysis and validation through indoor testing at standard test conditions

This paper presents the results of an extensive testing campaign for validating the time series analysis approach to the estimation of the linear field performance loss rates (PLR) of grid-connected photovoltaic (PV) systems of different technologies operating side-by-side at the PV Technology test site of the University of Cyprus since June 2006. Fifteen-minute average measurements of the array power at the maximum power point, PA, were used to construct time series of the performance ratio, RP, of each array. The time series were analysed with regARIMA and classical seasonal decomposition (CSD) in order to extract the trend. Then, linear regression (LR) was used to calculate the slope. To validate the results, all arrays were disassembled and every module was tested at Standard Test Conditions (STC) in a class A+A+A+ solar simulator, in order to calculate the nominal array degradation rate. Comparison of both methods has shown good agreement between the time series analysis approach and the indoor testing approach, for PV arrays with no identified failures through electroluminescence (EL). On the contrary, for modules with identified failures through EL, the nominal array degradation rate was higher in comparison to the field PLR. Differences between the two methods have been shown to be due to cracked cells, hotspots and spectral response mismatch. Lastly, the comparison has shown that amongst the time series analysis methods, regARIMA produced statistically significant PLR with low uncertainty and the best agreement with the nominal array degradation rates.

Effective dynamic tariffs for price-based Demand Side Management with grid-connected PV systems

In isolated electricity networks the penetration of renewable energy sources offers, among others, the advantage of distributed electricity. At the same time, as penetration levels increase, controls and regulations need to be imposed in order to alleviate rising grid integration issues. Some of these can be prevented through the use of dynamic tariffs enabling Demand Side Management. In this work, a new tool for the optimization of dynamic tariffs is developed. This is based on statistical analysis of the consumption profiles and optimization procedures, aiming to derive the most appropriate Time-of-Use (ToU) tariffs. The consumption profile analysis performed on three hundred prosumers in Cyprus showed strong correlation between the measured and the average consumption profiles while the self-consumption rate of existing prosumers is averaged to 30%. The developed dynamic ToU blocks in comparison with the load curve exhibit a mean absolute percentage error and root mean square error of 6.22% and 12.32%, respectively.

Definition and Computation of the Degradation Rates of Photovoltaic Systems of Different Technologies With Robust Principal Component Analysis

Grid-connected photovoltaic (PV) systems have become a significant constituent of the power supply mix. A challenge faced by both users and suppliers of PV systems is that of defining and computing a reliable metric of annual degradation rate while in service. This paper defines a new measure to calculate the degradation rate of PV systems from the PV field measured performance ratio (PR). At first, the PR time series is processed by conventional principal component analysis, which yields seasonality as the dominant data feature. The environment, operating conditions, uncertainty, and hardware used for monitoring influence the outdoor measurements unpredictably. These influences are viewed as perturbations that render the dominant feature obtained by PCA unsuitable to be used in a degradation rate definition. Robust principal component analysis (RPCA) is proposed to alleviate these effects. The new measure is defined as the area enclosed by the time series of the corrected by the RPCA annual monthly PR values. The degradation rates obtained for different technologies are compared with those obtained in previous studies. The results have shown that the degradation rates estimated by RPCA were in good agreement with previous investigations and provided increased confidence due to mitigation of uncertainty.

Chapter II-1-B – Energy Yield in Photovoltaic Systems

Abstract This chapter provides an overview of the effects of environmental and operational factors on the energy yield of photovoltaic (PV) systems; the levels of solar irradiance, temperature, spectrum, and soiling on the energy yield of PV systems are discussed within the context of previous research findings. In this context the focus is on the results obtained from the performance assessment of a number of grid-connected PV technologies that have been installed side-by-side under warm climatic conditions at a testing facility in Cyprus. Current research toward improvement of the accuracy of PV production modeling is illustrated, along with comparative analysis of the prediction accuracy.