Photovoltaic Degradation Rate Research Articles

Integrated techno-economic and life cycle assessment of shared circular business model based blockchain-enabled dynamic grapevoltaic farm for major grape growing states in India

Grape farms face many climate risks during cultivation, and regulatory and sustainability-related challenges after the harvest. To support the farmers, Government of India (GoI) aims to promote adaptive capacity throughout the grape value chain by improving resilient infrastructure. This paper proposes a novel Grapevoltaics farm design based on shared circular business model principles where resources like energy, water, and land are shared between grape farmer and solar plant operator. The resilience (in the near term) and sustainability (in the long term) of such multifunctional systems may be influenced by the decisions taken by the involved stakeholders in terms of resource sharing and support extension when facing climate risk events. To address stakeholder engagement, the proposed Grapevoltaic farms leverage blockchain smart contract, making them Blockchain-enabled Grapevoltaics (BCeDGVs). To understand the feasibility, 1-ha Grapevoltaic farm is modeled for 12 grape growing regions accross India and analysed using Resilience Performance, Life Cycle Analysis, and Techno-Economic (RePLiCATE) approach. The results show that the solar panels provide resilience support based on the rules of engagement written on blockchain smart contracts. The BCeDGVs has 1295.28–2908.08 kWh/kWp/day electricity generation potential across India with a lifetime average of 15,380.65 MWh even after considering the national average photovoltaic degradation rate of 1.9%. The observed grape yields vary between 2 and 31.25 Mt/ha with an average yield of 45 Mt/ha (table grape) and 40 Mt/ha (wine grape), and the rainwater harvest varies between 1.23 to 7.32 Ml. The economic assessment revealed that grape farmers and photovoltaic plant operators across India under BCeDGVs could have revenues ranging between INR 56.25 to 225 million and INR 29.48–66.19 million, respectively, suggesting to relook into the feed-in tariffs and crop selling prices. The environmental sustainability results suggest that 1.984 ha/MW land-use mitigation and 12–100% reduction in groundwater dependency are possible while maintaining the minimum and maximum national average global warming potentials as 0.074 and 0.088 kg CO2-eq./kg grape, respectively. Overall, the integrated assessments find that the BCeDGVs may promote low carbon, climate resilient grape cultivation in India and has possible integration with GoI's GrapeNet platform. This study highlights a new research direction symbiotic grape farms and energy networks.

Enhancing the Performance of Photovoltaic Panels by Cooling: a Review

Recently the world moves toward high utilization of renewable energy application due to environmental and economic issues. Photovoltaic (PV) is one of the most common renewable technologies, which produces electricity from solar irradiance. Due to environmental friendliness, sustainability, and lack of fuel costs, the number of applications and studies relating to PV plants is rapidly growing. Apart from the numerous benefits provided by PV, it faces some issues. Dust and surface temperature are the most important issues which cause a detrimental impact on the conversion system's performance. The cell temperature also, reflects on the PV degradation rate and the system life time. PV surface Cooling is an important way to be considered while running solar photovoltaic systems in order to obtain improved efficiency. There are different cooling techniques are suggested in literatures including active cooling, passive cooling and hybrid cooling techniques up to the most recent researches. The common methods for PV cooling are reviewed, studied, and evaluated in this paper. Different features and capabilities of cooling technologies are presented, in addition to highlight the opportunities to enhance or upgrade the cooling techniques.

Impact of duration and missing data on the long-term photovoltaic degradation rate estimation

Accurate quantification of photovoltaic (PV) system degradation rate (RD) is essential for lifetime yield predictions. Although RD is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on RD by analyzing the performance of different techniques applied to synthetic PV system data at different linear RD patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. The analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower RD deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated RD is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate RD results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate RD estimation are provided.

Techno-economic Analysis of Renewable-based Stand-alone Hybrid Energy Systems Considering Load Growth and Photovoltaic Depreciation Rates

The increasing trend in power consumption, mainly due to the rapid population growth, has resulted in grid outages and low-reliability grid connections. Renewable-based hybrid energy systems are one of the emerging alternatives for traditional and low-reliability grid connections. In this paper, a stand-alone hybrid energy system is proposed for a remote residential house. HOMER software is used for the optimisation of the proposed energy system. The main contribution of the paper is focused on considering two influential parameters, such as annual load growth and photovoltaic (PV) degradation rates in the optimal planning of the hybrid energy system. Simulation results indicate that considering influential parameters more realistic results, including system configuration, total net present cost (NPC) and optimal operation of the energy sources are achievable. Total NPC of the system obtained as 70,072 US$, which shows 52,029 US$ growth in comparison to the case neglected annual load growth and PV degradation rates. The optimum configuration benefits from higher penetration of renewable energy sources (RESs). Moreover, according to the comparison made with only-grid system, the proposed hybrid renewable-based energy system saves a large number of emissions. Based on the results, around 292,049.4202 kg emissions have been saved over 25 years of the project.

Assessment of uncertainties and variations in PV modules degradation rates and lifetime predictions using physical models

Prediction of the service lifetime of Photovoltaic (PV) modules is crucial for all PV stakeholders. PV manufacturers benefit from setting realistic warranties and researchers can improve the durability of PV materials and components in order to achieve a desired lifetime. To develop reliable predictive models, a thorough understanding of the different sources of uncertainties in degradation rates determination and lifetime prediction is indispensable. In this paper, we assess different sources of uncertainties in PV degradation rates and lifetime predictions using physical models. Three major sources of uncertainties are discussed: due to climate variables estimation, due to PV modules reliability and due to degradation rate models. Degradation rates evaluated using modelled and measured climatic variables are benchmarked to assess the uncertainties due to climatic input variables. It is found that module temperature estimation leads to the highest uncertainties in degradation rates of 1.7% to 14.5% (depending on the temperature model and also on the location) followed by relative humidity 2.4% to 12.2%. UV modelling showed the least effect on degradation rates variations of only 0.1% to 5%. The variations due to the different PV module reliability and degradation rate models are evaluated using measured PV performance data. It is shown that these variations can be as high as 65.5% and 59.4% respectively, even if the models are calibrated using the same data.

Nonlinear Photovoltaic Degradation Rates: Modeling and Comparison Against Conventional Methods

Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, “nonconventional” models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.

Feasibility of water-cooled photovoltaic panels under the efficiency and durability aspects

Photovoltaic (PV) panels can increase their efficiency and durability by using water-cooled systems. In this paper, the authors present a methodology to establish the degradation rate (DR) of PV panels related to their durability and efficiency under cooled versus no-cooled conditions. A long-term feasibility analysis of PV power plants with some suggested modifications are discussed in detail. A standard system was designed to cool down the PV modules. The simulations used with this purpose are based on several publications with relevant aspects carried out in the literature using Matlab-based modeling data. Such results were then confirmed by Homer program to predict the efficiency and durability of PV panels within a lifetime period of 25 years. To illustrate the methodology proposed here, the simulation parameters were used for the city of Santa Maria-RS, Brazil. The results allowed to conclude that cooling combined with heated water for domestic usage can decrease the PV degradation rate by 3% and provide an increased efficiency by almost 30% with respect to the PV panels without such cooling approaches. Therefore, the final results conclude that installation of water-cooled PV panels is an attractive and feasible option for a short- and long-term efficiency improvement with great impact on the expansion and durability of PV power plants.

Performance Ratio and Degradation Rate Analysis of 10-Year Field Exposed Residential Photovoltaic Installations in the UK and Ireland

As photovoltaic (PV) penetration of the power grid increases, accurate predictions of return on investment require accurate analysis of decreased operational power output over time. The degradation rate in PV module performance must be known in order to predict power delivery. This article presents the degradation rates over 10 years for seven different PV systems located in England, Scotland, and Ireland. The lowest PV degradation rates of −0.4% to −0.6%/year were obtained at the Irish PV sites. Higher PV degradation rates of −0.7% to −0.9%/year were found in England, whereas the highest degradation rate of −1.0%/year was observed in relatively cold areas including Aberdeen and Glasgow, located in Scotland. The main reason that the PV systems affected by cold climate conditions had the highest degradation rates was the frequent hoarfrost and heavy snow affecting these PV systems, which considerably affected the reliability and durability of the PV modules and their performance. Additionally, in this article, we analyse the monthly mean performance ratio (PR) for all examined PV systems. It was found that PV systems located in Ireland and England were more reliable compared to those located in Scotland.

Photovoltaic Degradation Rate Affected by Different Weather Conditions: A Case Study Based on PV Systems in the UK and Australia

This article presents the analysis of degradation rate over 10 years (2008 to 2017) for six different photovoltaic (PV) sites located in the United Kingdom (mainly affected by cold weather conditions) and Australia (PV affected by hot weather conditions). The analysis of the degradation rate was carried out using the year-on-year (YOY) degradation technique. It was found that the degradation rate in the UK systems varies from −1.05% and −1.16%/year. Whereas a higher degradation ranging from −1.35% to −1.46%/year is observed for the PV systems installed in Australia. Additionally, it was found that in the Australian PV systems multiple faulty PV bypass diodes are present due to the rapid change in the ambient temperature and uneven solar irradiance levels influencing the PV modules. However, in cold weather conditions (such as in the Northern UK) none of the bypass diodes were damaged over the considered PV exposure period. Furthermore, the number of PV hot spots have also been observed, where it was found that in the UK-based PV systems the number of hot spotted PV modules are less than those found in the Australian systems. Finally, the analysis of the monthly performance ratio (PR) was calculated. It was found that the mean monthly PR is equal to 88.81% and 86.35% for PV systems installed in the UK and Australia, respectively.

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting “routine” degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods.

Ash-Covered State Evaluation and Alarm of Distributed Photovoltaic System Considering Full Lifetime Degradation

With the widespread attention and research of distributed Photovoltaic (PV) systems, the state evaluation of distributed PV system, especially the evaluation of ash-covered state, has become increasingly prominent. To this end, an ash-covered state evaluation method for the distributed PV systems, which considers the full lifetime degradation, is proposed. First, the PV lifetime degradation rate is calculated based on the historical data of the PV system. Second, the fuzzy C-means (FCM) is used to cluster PV measured AC output power data into four weather types: sunny, cloudless, cloudy, and rainy. According to weather conditions, the Levenberg-Marquardt backpropagation (LMBP) Deep Neural Network (DNN) is adopted to establish four distributed PV AC output power fitting models by using measured meteorological data and measured output power data. Afterward, distributed PV output power fitted value is obtained by reducing the DNN output proportionally according to the PV degradation rate. Moreover, by analyzing the difference between the PV output power fitted value and the measured output power data, the ash-covered state of the system is evaluated, and a state alarm mechanism is established based on the system state. Finally, the validity and rationality of the proposed method are verified by the analysis of examples.

Novel Photovoltaic Hot-Spotting Fault Detection Algorithm

In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

Case study of PV output power degradation rates in Oman

To meet the increase in peak electricity demand, reduce fossil fuel emissions in Oman, and as an initiative taken by the government, by 2030 15% (3000 MW) of the total energy mix (20,000 MW) should be generated from renewable energy resources. It is crucial for the stakeholders and photovoltaic (PV) enthusiast to predict the return on investments and its performance in the local climatic conditions. In this study, a case study has been presented where different factors under local climatic conditions are studied. The results showed that the output power degradation for all modules is around 1.96%/year, which is almost double compared with European countries. Electrical analysis of different PV technologies showed that multi-crystalline silicon technology installed in hot-and-dry climate is degrading (around 2.54%/year) faster, while thin-film technology (CdTe) has shown lowest degradation (average of 0.8%/year) compared with any other PV technology. Furthermore, infrared image analysis showed that the presence of hot cells in PV modules is also a significant contributing factor in PV degradation rates. Severity of interconnect breakage tests confirm the increase of the series resistance of PV modules, which further contributes to the reduction of short-circuit current and thus PV maximum output. power.

Probabilistic evaluation of solar photovoltaic systems using Bayesian networks: a discounted cash flow assessment

AbstractSolar photovoltaic (PV) technology is now a key contributor worldwide in the transition towards low‐carbon electricity systems. To date, PV commonly receives subsidies in order to accelerate adoption rates by increasing investor returns. However, many aleatory and epistemic uncertainties exist with regard to these potential returns. In order to manage these uncertainties, an innovative probabilistic approach using Bayesian networks has been applied to the techno‐economic analysis of domestic solar PV. Empirical datasets from over 600 domestic PV systems, together with national domestic electricity usage datasets, have been used to generate and calibrate prior probability distributions for PV yield and domestic electricity consumption, respectively, for typical urban housing stock. Subsequently, conditional dependencies of PV self‐consumption with regard to PV generation and household electricity consumption have been simulated via stochastic modelling using high temporal resolution demand and PV generation data. A Bayesian network model is subsequently applied to deliver posterior probability distributions of key parameters as part of a discounted cash flow analysis. The results illustrate the sensitivity of PV investment returns to parameters such as PV self‐consumption, PV degradation rates and geographical location and quantify inherent uncertainties when evaluating the impact of sector‐specific PV adoption upon economic indicators. The outcomes are discussed in terms of the value and impact of this new Bayesian approach in terms of supporting robust and rigorous policy and investment decision‐making, especially in post‐subsidy contexts globally. © 2016 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.

Compendium of photovoltaic degradation rates

AbstractPublished data on photovoltaic (PV) degradation measurements were aggregated and re‐examined. The subject has seen an increased interest in recent years resulting in more than 11 000 degradation rates in almost 200 studies from 40 different countries. As studies have grown in number and size, we found an impact from sampling bias attributable to size and accuracy. Because of the correlational nature of this study we examined the data in several ways to minimize this bias. We found median degradation for x‐Si technologies in the 0.5–0.6%/year range with the mean in the 0.8–0.9%/year range. Hetero‐interface technology (HIT) and microcrystalline silicon (µc‐Si) technologies, although not as plentiful, exhibit degradation around 1%/year and resemble thin‐film products more closely than x‐Si. Several studies showing low degradation for copper indium gallium selenide (CIGS) have emerged. Higher degradation for cadmium telluride (CdTe) has been reported, but these findings could reflect a convolution of less accurate studies and longer stabilization periods for some products. Significant deviations for beginning‐of‐life measurements with respect to nameplate rating have been documented over the last 35 years. Therefore, degradation rates that use nameplate rating as reference may be significantly impacted. Studies that used nameplate rating as reference but used solar simulators showed less variation than similar studies using outdoor measurements, even when accounting for different climates. This could be associated with confounding effects of measurement uncertainty and soiling that take place outdoors. Hotter climates and mounting configurations that lead to sustained higher temperatures may lead to higher degradation in some, but not all, products. Wear‐out non‐linearities for the worst performing modules have been documented in a few select studies that took multiple measurements of an ensemble of modules during the lifetime of the system. However, the majority of these modules exhibit a fairly linear decline. Modeling these non‐linearities, whether they occur at the beginning‐of‐life or end‐of‐life in the PV life cycle, has an important impact on the levelized cost of energy. Copyright © 2016 John Wiley & Sons, Ltd.

Photovoltaic Degradation Rates—an Analytical Review

ABSTRACTAs photovoltaic penetration of the power grid increases, accurate predictions of return on investment require accurate prediction of decreased power output over time. Degradation rates must be known in order to predict power delivery. This article reviews degradation rates of flat‐plate terrestrial modules and systems reported in published literature from field testing throughout the last 40 years. Nearly 2000 degradation rates, measured on individual modules or entire systems, have been assembled from the literature, showing a median value of 0·5%/year. The review consists of three parts: a brief historical outline, an analytical summary of degradation rates, and a detailed bibliography partitioned by technology. Copyright © 2011 John Wiley & Sons, Ltd.