Utility-scale Photovoltaic Systems Research Articles

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Accurate modelling of the bifacial gain potential of rooftop solar photovoltaic systems

Bifacial solar modules have emerged as a promising technology in utility-scale photovoltaic systems, experiencing significant growth and capturing a substantial market share worldwide, as reported by the International Technology Roadmap for Photovoltaic (ITRPV) 2023. Despite this progress, the potential of bifacial modules in rooftop applications remains largely unexplored. This paper aims to address this knowledge gap by conducting a comprehensive study utilizing Monte Carlo Ray Tracing techniques coupled with detailed electrical modelling. The primary objective of this study is to investigate the viability of implementing bifacial solar modules on rooftops by examining the potential energy yield gains. By conducting a detailed analysis on a representative rooftop in Canberra, Australia, real-world conditions, and variations are incorporated, providing a more accurate assessment of the energy yield gains achievable in such settings. The simulation results reveal that the implementation of bifacial solar modules on rooftops within Australia can result in energy yield gains of up to 22.6%. These findings demonstrate the considerable potential of bifacial technology in maximizing solar energy production in rooftop applications. The analysis shows significant implications of module and system design on the potential gain. For example, electrical optimisation of individual modules in a system accounted for 1.4% of the bifacial gain. The analysis considers full annual time-step simulation, typical mechanical mounting components, installation orientations and module characteristics, ensuring practical relevance and reliability of the results.

Effects of Extreme Weather Conditions on PV Systems

We are witnessing significant climatic changes and increasingly frequent extreme weather conditions affecting every part of the globe. In order to reduce and stop these unfavourable climate changes, there has been a shift to the use of renewables, and in this sense, a significant contribution of the photovoltaic (PV) power plant is planned. This paper analyses the safety, reliability, and resilience of PV systems to extreme weather conditions such as wind storms, hail, lightning, high temperatures, fire, and floods. In addition to using available information from the literature, temperature measurements were also carried out on the rooftop PV power plant in Slavonski Brod, as well as a numerical stress analysis at extreme wind speeds using Ansys software. The results of the analysis show that existing PV systems are very resilient to extreme weather conditions. Utility-scale PV systems can usually withstand wind speeds of up to 50 m/s without any problems, and only at higher speeds do local stresses occur in certain parts of the structure that are higher than permissible. Resistance to hail is also very high, and manufacturers guarantee resistance to hail up to 25 mm in size. At high air temperatures, the temperature of the panel frame can reach about 70 °C, the panel temperature up to 85 °C, and the temperature of the cable insulation over 60 °C, as measurements have shown. Such high temperatures lead to a drop in electricity production up to 30% but do not pose a fire hazard to the cables and the roof if the roof insulation is conducted correctly. Forest fires do not usually pose a direct threat to PV systems, but the smoke that spreads over a large area reduces the solar radiation reaching the PV panel. It can also cause an unfavourable “wiggle effect”. Lightning strikes to a PV panel are not common, although they are possible. With built-in safeguards, no major damage should occur. Flooding is always a possibility, but with properly designed drainage systems, the damage is minimal in most cases.

A GIS-based method for assessing the economics of utility-scale photovoltaic systems

Solar photovoltaic capacities have experienced remarkable gains worldwide. The accelerated deployment of photovoltaic (PV) systems has emphasized the need for methods and tools that can assist in planning and investment decisions of utility-scale photovoltaic systems to ensure a sustainable energy transition. This study bridges the gap between research and current solar PV project evaluation practices by proposing a geographic information system (GIS)-based approach for analyzing land eligibility and performing techno-economic assessments of utility-scale photovoltaic systems. To tackle the issue of country-specific cost elements, the model incorporates a levelized cost of electricity (LCOE) breakdown often used by governmental and intergovernmental organizations. The proposed GIS-based model can assist in mapping the distribution of eligible land for utility-scale solar systems while considering exclusion constraints, estimating PV capacity and generation potentials, as well as determining the average LCOE of utility-scale solar photovoltaic systems at a spatial resolution of 100 m. The GIS-based approach is demonstrated through the case study of Poland. The model estimates that 3.61% of the total area of Poland is suitable for the installation of utility-scale solar PV systems. Implementing PV installations in these areas would result in solar capacities ranging from 394.64 to 563.77 GW. Furthermore, the findings of the case study indicate that the LCOE would range from €0.043/kWh to €0.049/kWh, with a national average of €0.045/kWh. The proposed approach can be utilized to develop national and regional strategies focused on large-scale PV installations, facilitating the attainment of renewable energy goals. The study fills a significant gap in the literature as it provides a GIS-based tool for planning the sustainable development of utility-scale PV systems at the regional scale. In addition, it is the first to comprehensively assess the capacity and generation potential of utility-scale solar photovoltaics in Poland at the NUTS-2 level.

Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation

The utility-scale photovoltaic systems have been widely integrated for sustainable urban development. However, the varying photovoltaic conversion efficiency (PVCE) affected by dynamic urban thermal environment causes uncontrollable uncertainty in electricity generation, which challenges installed-capacity planning and load-balancing operations. To tackle this problem, this study develops a PVCE estimation model containing four hierarchical modules. First, photovoltaic surface temperatures (PVSTs) are retrieved from satellite imagery, and meteorological features are collected to represent the dynamic thermal environment. Second, the contribution of each feature to the PVST estimation is evaluated, referring to the Mean Decrease in Impurity, Permutation Importance, and SHapley Addictive exPlanations. Third, machine learning models using Support Vector Machine, Random Forest, and XGBoost are developed to establish robust regressions between PVSTs and the selected features, enabling an accurate estimation of PVST spatio-temporal heterogeneity. Finally, the spatio-temporally corresponding PVCEs are calculated, resulting in a refined estimation of annual electricity generation. The investigation of four floating PV systems in Singapore found that their PVCEs vary insignificantly throughout the year, which is probably because of its stable climate and the cooling effect of water. The proposed model is simple and effective, demonstrating its impact on PV potential estimation when the urban thermal effect becomes significant.

Univariate and multivariate short-term solar power forecasting of 25MWac Pasir Gudang utility-scale photovoltaic system using LSTM approach

The generation of solar photovoltaic (PV) systems in Malaysia has great potential due to the abundance of sunlight and high irradiation level. Malaysia’s unique location near the equator makes solar energy the most attractive option for future energy sources. However, the erratic weather leads to variability of power generation, especially during high feed-in of solar energy that will eventually affect grid system stability. Hereof, solar power forecasting is critical, especially in operating utility-scale photovoltaic systems or large-scale solar (LSS) plants. This paper presents an approach to forecasting solar power generation for 10-min to 180-min ahead based on univariate and multivariate using Long -Short-Term Memory (LSTM) technique. The model performances are evaluated based on a real dataset from the 25MWac Pasir Gudang LSS plant from July 2021 to May 2022. The result shows that LSTM with a univariate model outperformed the multivariate model for the short-term forecasting (10-min to 50-min ahead) by 2.09% of RMSE. However, multivariate outperformed univariate for the longer forecasting horizon at 180-min ahead by 34.87% of RMSE. The forecasting output for the multivariate model that only uses historical meteorological data is less reliable than the multivariate model that uses historical meteorological and AC power output data. This research finding is envisaged to provide benefits to the grid system operation, planning, maintenance, and scheduling, thereby improving the reliability of the LSS plant.

Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.

Techno-economic analysis of a utility-scale grid-tied solar photovoltaic system in Benin republic

About 60.0% of Benin's population currently lacks access to reliable electricity to perform their daily activities. The Benin Republic has abundant solar energy resource, which could be harnessed efficiently to increase its access rate to electricity and improve living standards. This study evaluates the techno-economic viability of installing a 10.0 MW utility-scale grid-tied solar photovoltaic (PV) system in seven cities located in Benin. The RETScreen software was used to perform technical, economic, and greenhouse gas emission analyses on the proposed system. Based on the assumptions in this study, the PV system produces, on average, about 13,222 MWh/yr of electricity available for grid export. This yields a capacity factor of 15.1% and a performance ratio of approximately 67.3%. Without revenues and capital subsidies, the project generates a levelized cost of energy (LCOE) ranging from 0.110 USD/kWh to 0.125 USD/kWh. Also, the PV project is attractive for investment at a feed-in tariff of above 0.10 USD/kWh. Using the utility-scale PV plant for electricity generation at the installation sites saves about 76.0% of carbon dioxide (CO2) emissions compared to the utility grid. The findings show that incentives and subsidies could lower the LCOE and increase solar PV investment in Benin. Investing in utility-scale PV systems could help Benin increase its electricity access rate and mitigate greenhouse gas emissions for sustainable development. The study aims to alert stakeholders, decision-makers, and investors toward developing the Benin utility-scale solar PV sector.

Correspondence: Bill of Materials Variation and Module Degradation in Utility-Scale PV Systems

Photovoltaic (PV) modules of the same make and model are often assembled with different bills of materials (BOMs). In this correspondence, we revisit two case studies of utility-scale silicon PV systems in which these differing BOMs were associated with faster-than-expected degradation. In one of the sites, we found that different metallization paste had been used for grid lines in some cells leading to loss of contact to the cell and severe series resistance degradation. We provide details on the observation of this mechanism at two additional sites not described in the original article. In a second case study, we found that two different types of cell had been used, and that they could be distinguished by their back contact. Cells with uniform back contacts suffered from light and elevated temperature induced degradation (LeTID), while those with local back contacts did not. We also briefly describe BOM variations observed at other sites to illustrate the extent of the challenge. Our results from both sites underscore that variations in BOM, even among modules of the same make and model can lead to reliability challenges.

Incorporating energy justice into utility-scale photovoltaic deployment: A policy framework

Utility-scale photovoltaic (PV) installations made up 77 GW (6%) of installed capacity in the United States, as of the end of 2021 [1,2]. This will grow to more than 500 GW by 2050 under a mid-case projection or more than 800 GW if solar costs decline more rapidly [3]. While utility-scale PV is projected to grow rapidly, to date, unlike energy efficiency or distributed PV, utility-scale PV has not been used to provide substantial financial benefits to underserved communities, either through ownership, financing of assets, or direct electricity bill reduction.We assess two mechanisms through which utility-scale PV could benefit underserved communities. We find that while a framework for direct electricity bill reduction can be meaningful to customers, this mechanism falls short of providing restorative justice via wealth creation for minority-owned businesses. In contrast, we find that a framework for procurement of utility-scale PV by public and private entities from PV projects that are financed, owned, and/or developed by minority-owned businesses can provide this restorative justice benefit, and thereby facilitate an equitable energy transition. We conclude with concrete recommendations for new policies and programs to ensure that the benefits of utility-scale PV systems are distributed to underserved communities.

Light- and Elevated-Temperature-Induced Degradation-Affected Silicon Cells From a Utility-Scale Photovoltaic System Characterized by Deep-Level Transient Spectroscopy

Photovoltaic modules from a utility-scale field experienced power loss by light- and elevated-temperature-induced degradation (LeTID). Samples from the affected monocrystalline silicon cells are cored and extracted from the module packaging and then laser-scribed to form 2-mm diameter isolated areas. Using deep-level transient spectroscopy, a majority-carrier, hole-trap defect with an activation energy of 0.42 eV is detected on degraded and regenerated samples. The LeTID-degraded sample, however, has a larger signal corresponding to a trap density of 1.1 &#x00D7; 10¹³ cm^&#x2212;3, which is about five times larger than the 2.1 &#x00D7; 10¹² cm^&#x2212;3 trap density of the regenerated sample. An increase in filling pulse time from 50 <i>&#x03BC;</i>s to 20 ms shows a slight decrease in activation energy from 0.42 to 0.36 eV suggesting that the defect level may consist of a band of energy states where shallower states continue to fill with long filling times. The capture rate of the defect is directly measured using an increasing series of filling pulsewidths in 15 to 125 ns range. This leads to a measured capture cross section of 5.1 &#x00D7; 10^&#x2212;17 cm², and using an approximate defect density of 10¹³ cm^&#x2212;3, the majority-carrier-hole lifetime related to this defect is approximately 100 <i>&#x03BC;</i>s when in the LeTID-degraded state.

Implementation of Utility-Scale PV System to Support Power System Reliability

Nowadays, the Iraqi power system requires additional power due to its growing population and demands. Utility-scale photovoltaic (PV) power plants are generally feasible in geographic areas exposed to greater than 3 kWh/m2 /day of solar irradiance. This study aims to analyze the possibility of expanding in use of PV systems in Iraq; thus, the main challenges facing the Iraqi electrical power system are discussed in this paper. Furthermore, the ability to implement a large-scale PV power plant in Iraq to increase the system's reliability is studied. The system advisor model (SAM) was used to achieve this analysis. As a result, based on the simulation results, the PV power plant is approved as a feasible solution in Iraq.

A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems With Power Reserves and Fast Frequency Response Capabilities

This paper presents a fast power-setpoint tracking algorithm to enable utility-scale photovoltaic (PV) systems to provide high quality grid services such as power reserves and fast frequency response. The algorithm unites maximum power-point estimation (MPPE) with flexible power-point tracking (FPPT) control to improve the performance of both algorithms, achieving fast and accurate PV power-setpoint tracking even under rapid solar irradiance changes. The MPPE is developed using a real-time, nonlinear curve-fitting approach based on the Levenberg-Marquardt algorithm. A modified adaptive FPPT based on the Perturb and Observe technique is developed for the power-setpoint tracking. By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations. Furthermore, to limit the maximum output power ripple, a new design is introduced for the steady-state voltage step size of the adaptive FPPT. The proposed algorithm is implemented on a testbed consisting of a 500 kVA three-phase, single-stage, utility-scale PV system on the OPAL-RT eMEGASIM platform. Results show that the proposed method outperforms the state-of-the-art.

MODELS, DETECTION METHODS, AND CHALLENGES IN DC ARC FAULT: A REVIEW

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such hazards is crucial in the early phases of the distribution. In this paper, a detailed review of modern approaches for the identification of DC arc faults in PV is presented. In addition, a thorough comparison is performed between various DC arc-fault models, characteristics, and approaches used for the identification of the faults.

Geographic potential of shotcrete photovoltaic racking: Direct and low-concentration cases

Relentless cost declines in solar photovoltaic (PV) modules have radically reduced system costs, but the result is that racking costs have become more important. In this study a new open source racking method using earth and shotcrete-based ground mounts is investigated in two geographic regions: (1) Boa Vista, Brazil, where PV rows have an optimized shallow tilt angle and can be close-packed and (2) Kingston, Canada, where racks can make use of low concentration in the required inter-row spacing. Models are developed for both locations with single, double and triple vertical stacked shotcrete-based ground-mounted PV racking systems. Using these models bills of materials and energy simulations are produced to provide a cost benefit analysis specifically for the shotcrete racks. The results show that for utility-scale PV systems near the equator, a shotcrete racking system could reduce racking costs by 18–22% from the least expensive conventional racking and as much as 47% reduction for more expensive commercial racking. Economics is less clear for shotcrete-based racking at higher latitudes that incorporate low-concentration reflectors as they can be both lower and higher costs than conventional racking, but would be expected to produce at least 18% more electricity per installed Watt. Overall this novel racking concept was found to be promising, particularly for desert regions where it could reduce soiling losses. The results of this study indicate a need for future work to fully evaluate the potential of shotcrete racking to reduce solar electricity costs at the utility scale.

Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

Photovoltaic (PV) system power supply is characteristically intermittent. Therefore, PV forecasting is crucial for decision makers responsible for electrical grid stability. With forecast models traditionally trained as macro-level solutions, where a single model emulates the entire PV system, there is uncertainty regarding the ability of these macro-level models to capture the low-level power output dynamics of large multi-megawatt PV systems. Instead, an aggregated inverter-level forecasting methodology is proposed to obtain an enhanced forecasting accuracy. These macro-level and inverter-level forecasting methodologies are implemented with state-of-the-art deep learning based Feedforward neural network, Long Short-Term Memory and Gated Recurrent Unit recurrent neural network models. Results are generated for a real-world scenario, with multi-step forecasts delivered 1–6 h ahead for a 75 MW rated PV system. To ensure the scalability of the proposed methodology, a unique inverter-clustering technique is presented, which reduces the effort of optimising multiple low-level forecast models. A heuristic process of systematic hyperparameter optimisation is also proposed, which serves to guide future forecasting practitioners towards unbiased model development. From the deterministic and probabilistic confidence interval evaluations, overall results demonstrate a marginal increase in forecasting accuracy from the proposed aggregated inverter-level forecasts. The best performing macro-level model obtained Mean Absolute Percentage Error (MAPE) values ranging between 1.42%–8.13% for all weather types and forecast horisons. In comparison, the equivalent inverter-level forecasts delivered MAPE values ranging from 1.27%–8.29%. Finally, it is concluded that deep learning based macro-level forecast models have a sufficient ability to capture low-level PV system behaviour.

Light and Elevated Temperature Induced Degradation (LeTID) in a Utility-Scale Photovoltaic System

We present a detailed case study of degradation in monocrystalline silicon photovoltaic modules operating in a utility-scale power plant over the course of approximately three years. We present the results of degradation analysis on arrays within the site, and find that five of the six arrays degraded faster than the best performing array, even though the arrays consist of modules of the same manufacturer and model. We also describe the results of extensive laboratory characterization of modules returned from the field, including module- and cell-level current-voltage characterization, luminescence imaging, and accelerated testing. The laboratory test results and the field performance are consistent with light and elevated temperature induced degradation (LeTID). Notably, we observe differences in back contact technology between affected and unaffected modules. This article also demonstrates a method to identify possible LeTID degradation in the field and confirm the result with laboratory testing of a small number of modules.

Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling

The short-term power output variability of solar photovoltaic (PV) systems caused by passing clouds is becoming a major concern for grid operators. As the penetration of utility-scale PV systems boosts, the rapid power fluctuations greatly challenge the grid transient stability. In this regard, the concept of power ramp-rate (RR) has been recently imposed to quantify the PV fluctuations, and a series of ramp regulations are implemented by utilities. However, due to the limitations in terms of high-resolution simulation of utility-scale PV systems and reproducing cloud shadow natures, current studies still show deficiencies in comprehensively investigating the RRs rising from both endogenous and exogenous factors during cloud shadow transitions.With the objective of producing high-resolution and high-accuracy emulations of utility-scale PV systems under passing clouds, thus providing a clearer understanding on the cloud-induced RRs, this paper first sets forth a partial shading emulator that is capable of efficiently mimicking the behaviors of an arbitrary partially shaded PV system, in accuracy of the PV module level. Then a fully customizable shadow model that can reproduce the natures of a real cloud shadow is introduced. Based on the developed emulation tools, the effects of two endogenous factors i.e., PV array arrangement and system orientation, and three exogenous factors i.e., shadow intensity, shadow velocity, and shadow size on RRs are explored for a range of utility-scale PV systems from 1 MW to 60 MW. Furthermore, in order to assess the RRs in reality, a total of 3747 cloud shadow transitions exploited from real measurements have been applied for emulations. The results reveal that the RRs caused by passing clouds are critical problems for system operations, and a larger system can suffer more from ramp violations, indicating that the advanced ramp control strategies should be essential for contemporary utility-scale PV systems.

An improved methodology for reactive power management in grid integrated solar PV system with maximum power point condition

The grid-integrated solar photovoltaic (PV) inverter is generally used to provide active power (P) support to the distribution grid operated at maximum power point (MPP) condition. Moreover, it should also be capable of providing reactive power support (Q) to the distribution grid. Therefore, an appropriate range of reactive power should be known at MPP condition to ensure satisfactory operation of the grid-integrated PV system (GIPV). In this work, an improved mathematical methodology is proposed to derive the P-Q capability curve of GIPV for managing the reactive power injection into the grid at MPP condition for various environmental conditions. By evaluating all the possible set of working point within the stable region of operation, this methodology is used to obtain reactive power limits of a utility-scale single-stage three-phase PV system connected to the IEEE 33 bus distribution grid. The considered PV system is simulated using software-in-the-loop (SIL) environment based on OPAL-RT OP4510 real-time digital simulator with coordinated closed-loop control of dc-link voltage controller, maximum power point tracking (MPPT) controller, and active and reactive power controller. To validate the effectiveness of the improved mathematical methodology, the reactive power injection to the grid is changed, and the corresponding active power injection to the grid are measured, which are compared with the P-Q values obtained from the improved mathematical methodology.

Machine Learning Photovoltaic String Analyzer.

Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to detect PV system faults, the machine learning models should provide accurate outputs. The aim of this work is to accurately predict the DC energy of six PV strings of a utility-scale PV system and to accurately detect PV string faults by benchmarking the results of four machine learning methodologies known to improve the accuracy of the machine learning models, such as the data mining methodology, machine learning technique benchmarking methodology, hybrid methodology, and the ensemble methodology. A new hybrid methodology is proposed in this work which combines the use of a fuzzy system and the use of a machine learning system containing five different trained machine learning models, such as the regression tree, artificial neural networks, multi-gene genetic programming, Gaussian process, and support vector machines for regression. The results showed that the hybrid methodology provided the most accurate machine learning predictions of the PV string DC energy, and consequently the PV string fault detection is successful.