Utility-scale Photovoltaic Plants Research Articles

The role of flexible demand to enhance the integration of utility-scale photovoltaic plants in future energy scenarios: An Italian case study

Photovoltaic (PV) will have a prominent role in the future energy mix to achieve the energy and climate targets. However, its variability requires that its exploitation must be improved to minimize its integration costs. The objective of this research is to provide a methodology applicable to any energy system modelling tool for assessing the benefits of flexible demand as an option to better manage the PV variability and to reduce its integration costs. Two strategies are used to shift the flexible demand within three different scenarios based on the Italian energy system and its planned evolution to the year 2030. The effects of flexibility are investigated through an expansion capacity optimization of battery energy storage systems (BESS) and powerlines transport capacities in three different scenarios. Results suggest that flexible demand allows to reduce the BESS capacity of around 2 %–6 %, and powerlines capacity in the range of 2%–10 %, thus the resulting VRES avoided integration costs varies from 758 M€ to 8611 M€. However, it has negligible effects in boosting RES penetration and lowering the CO2 emissions. The estimated DSM remuneration opens up interesting possibilities for applications in the context of energy communities and electricity market aggregators.

Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method

It is, now, universally accepted that modern solar resource assessment should proceed with acquiring satellite-derived irradiance; the underlying problem is one of retrieval. The Advanced Geostationary Radiation Imager (AGRI), which is one of the main payloads of China’s latest-generation geostationary satellite series, namely, Fengyun-4 (FY-4), offers such an opportunity, albeit its full potential remains largely unexplored. On this account, a region-adapted semi-empirical model based on Heliosat-2 is herein developed to estimate global horizontal irradiance (GHI) at the native resolution of FY-4 satellite (0.5 km, 15 min), using AGRI reflectance measurement at 640 nm. The adaptation of Heliosat-2 parameters to China’s climatology and geography is based on a research-grade ground-based radiometry network with 38 stations evenly distributed across the country. Model validation is conducted against ground-based observations made at 34 unseen stations, over the course of one year (2018). As compared to the 5-km Himawari-8 radiation product, the developed product not only has a 10-fold refinement in resolution, which can clearly depict those small-scale variations in solar resources that are not observable from the Himawari-8 product, but also attains a substantially lower bias (1.4% versus 7.6% under clear skies and –0.3% versus 9.5% under cloudy skies). This ultra-high-resolution product is of vital importance to solar resource assessment on a scale consistent with the size of typical utility-scale photovoltaic plants. The product is also of great value for agricultural and ecological studies, which often depends upon high-quality solar radiation at a high spatio-temporal resolution.

Analysis of Photovoltaic Plants with Battery Energy Storage Systems (PV-BESS) for Monthly Constant Power Operation

Photovoltaic generation is one of the key technologies in the production of electricity from renewable sources. However, the intermittent nature of solar radiation poses a challenge to effectively integrate this renewable resource into the electrical power system. The price reduction of battery storage systems in the coming years presents an opportunity for their practical combination with utility-scale photovoltaic plants. The integration of properly sized photovoltaic and battery energy storage systems (PV-BESS) for the delivery of constant power not only guarantees high energy availability, but also enables a possible increase in the number of PV installations and the PV penetration. A massive data analysis with long-term simulations is carried out and indicators of energy unavailability of the combined system are identified to assess the reliability of power production. The proposed indicators allow to determine the appropriate sizing of the battery energy storage system for a utility-scale photovoltaic plant in a planning stage, as well as suggest the recommended operating points made for each month through a set of graphs and indicators. The presence of an inflection zone has been observed, beyond which any increase in storage does not generate significant reductions in the unavailability of energy. This critical zone is considered the sweet spot for the size of the storage, beyond which it is not sensible to increase its size. Identifying the critical point is crucial to determining the optimal storage size. The system is capable of providing reliable supply of constant power in monthly periods while ensuring capacity credit levels above 95%, which increases the penetration of this renewable resource. Despite the fact that the study focuses exclusively on the analysis from an energy perspective, it is important to consider the constraints associated to real storage systems and limit their oversizing.

A PV variability tolerant generic multifunctional control strategy for battery energy storage systems in solar PV plants

This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant.

The impact of photovoltaic power plants on surface energy budget based on an ecohydrological model

Solar photovoltaic (PV) generation has become the major type of solar energy utilization as the world's energy demand grows. However, the layout of large-scale public PV plants changes the original underlying conditions and has an impact on the local surface energy budget and distribution. Based on the ecohydrological model T&C, taking the fixed utility-scale PV plants as the typical object proposes a PV module, and the T&C-PV model is developed to simulate the impact of PV plants on the regional surface energy budget. Compared with the original T&C model, a substantially improvement of the simulation of energy flux in the PV plant paved underlying surface has been achieved in the T&C-PV model. This model is applied to PV power plants in Xinjiang Province, China, and the results show that the sensible heat flux increased by 12% and the latent heat flux decreased by 25%, with the percentage of transpiration decreasing from 90.1% to 74.5% and the percentage of evaporation increasing from 9.9% to 25.5% after the PV power plants are set up.

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.

A Cost–Benefit Analysis for Utility-Scale Agrivoltaic Implementation in Italy

Utility-scale photovoltaic plants can take up areas as wide as several tens of hectares, often occupying spaces normally used for other purposes. This “land competition” issue might become particularly relevant for agriculture since, similarly to the production of photovoltaic electricity, farming uses the sun as a primary energy source. Thus, there is increasing interest in investigating agrivoltaic plants that allow the coexistence of agricultural activity and the production of electricity from photovoltaics. Such solutions are more complex and expensive than standard ground-mounted photovoltaic plants, so it is questionable whether the economic revenues produced by the agrivoltaic choice and resulting from both the cropland activity and electricity production can compensate for the high costs involved. The problem is further complicated by the fact that both crop revenues and photoelectricity costs depend, in general, on the geographical location. In this study, a cost/benefit methodology was developed to investigate the conditions under which the installation of an agrivoltaic utility plant can be economically advantageous compared with a standard ground-mounted photovoltaic plant. The analysis relies on the evaluation of both the extra cost related to the agrivoltaic choice and the performance benefit related to the crop revenues. By fixing the capacity of PV utility plants to be installed in all Italian regions, results were validated, considering crops such as wheat, corn, soybean, potato, and sunflower that make use of wide areas. It was determined that the higher infrastructural costs of agrivoltaic plants seriously hamper their installation, even for high-revenue croplands, unless suitable supporting policies in the form of public subsidies are conceived. In this context, it would be useful to evaluate whether such financial aids conceived to support agrivoltaic implementation in productive agricultural areas could be better used to support agrivoltaic installations in croplands at risk of abandonment or even already abandoned croplands, recovering otherwise unproductive agricultural lands.

Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants

Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance.

Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively.

Coordination of Utility-scale PV Plant and Wind Power Plant in Interarea-Oscillation Damping

This paper proposes a coordinated control scheme of a utility-scale photovoltaic (PV) power plant and a wind power plant (WPP) to support voltage stability and provide fast recovery after the grid faults in a power system with high penetration renewable energy sources (RES). <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The operation of PV and wind plants are time-shifted by approximately twelve hours. PV generates during the daytime, and wind plants usually produce peak power at night. Seasonally, the PV plants generate peak power on summer days, and wind plants produce maximum power during winter nights. These characteristics present an opportunity to use both types of renewables to complement each other in producing power and transforming energy, and also in providing active damping to safeguard the power system. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">To achieve these goals, two separate power oscillation damping (POD) control loops have been included for active and reactive power modulation in both PV and WPP. In both PV and WPP, an active power damping function without any power curtailment and a reactive power damping function modulating the reactive current (<i>I</i>_Q) on the voltage control loop have been included. This paper uses the nearest synchronous generator's local bus angle and speed as feedback for each of these loops. The performance of the proposed coordinated scheme based on the time of the day has been verified in a two-area system using the PSCAD simulator. The effectiveness of the control schemes has been investigated during the different timings of the day by observing the efficiency and the response time of the post-fault oscillations mitigation and the voltage restoration.

The characteristics and parameterizations of the surface albedo of a utility-scale photovoltaic plant in the Gobi Desert

The characteristics and parameterizations of the surface albedo of a utility-scale photovoltaic plant in the Gobi Desert

A coupled WRF-PV mesoscale model simulating the near-surface climate of utility-scale photovoltaic plants

To simulate the local or regional climate of utility-scale photovoltaic (PV) plants, a new PV-associated energy balance model was developed for the Weather Research and Forecasting Model (WRF). The resultant tool can be used to improve our understanding of the interaction between utility-scale PV plants and land-surface processes. The effects of PV panels were represented by transferring the solar radiation into electricity and heat flux and imposing an extra drag force on the mean flow in the model domain. The proposed PV model is more advanced than previous models in the following ways: (1) The model is universal based on its analytical expression of the sensible heat of PV panels and the influence of the physical shielding of the panels on the upward longwave radiation. (2) The photoelectric conversion efficiency (ε) in the model is temperature dependent and restrained between seasons. (3) The model includes the soil and latent heat flux parameterizations under observation constraints in a novel way. (4) The model bases the dynamic roughness length (Z0) on the eddy covariance flux observation. This new PV model was then incorporated with the WRF mesoscale meteorological model, and the model evaluation in two utility-scale PV plants demonstrated acceptable performance of the new WRF-PV mesoscale model in simulating the max/mean daily surface temperature, downward shortwave radiation, and wind speed with correlation coefficients of 0.61–0.99, and the hourly sensible/latent heat fluxes, with correlation coefficients of 0.59–0.93 during different seasons. The consistency of the performance of the model across these near-surface meteorological variables confirms the versatility and reliability of the new WRF-PV model as a suitable tool for simulating the climate of utility-scale PV plants.

Detecting Permanently Activated Bypass Diodes in Utility-Scale PV Plant Monitoring Data

Data-driven operation and maintenance (O&M) schemes are needed to secure efficient operation through the full lifetime of a photovoltaic (PV) power plant. In this study, we expand the capabilities of an analytics platform for data-driven O&M by proposing a method for the detection of permanently activated bypass diodes from PV plant monitoring data. The new method is based on a step detection algorithm developed and employed on historical power monitoring data from a 75-MWp dc PV plant. The results are correlated with active bypass diodes detected by an infrared thermography (IRT) scan performed after 5 years of operation. A stepwise drop in normalized yield is detected for 54.6% of string sets with one active bypass diode. The same is only detected in 1% of string sets with no detected IRT signatures. By replacing modules affected by IRT signatures, the detected power loss is recovered, a strong validation of the method. The novelty of the approach is fault detection of small losses with no extra sensor requirements and that through precise detection of the onset of active diode faults, the historical fault rates can be determined with high time resolution.

Hierarchical Control of Utility-Scale Solar PV Plants for Mitigation of Generation Variability and Ancillary Service Provision

This paper presents a hierarchical control system to mitigate the variability of solar photovoltaic (PV) power plant and provide ancillary services to the electric grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a small fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up production from the overall power plant, for a service such as regulation reserve. This control system is practical for continuously changing cloud cover conditions in partially cloudy days. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The proposed control algorithm is subsequently compared with the alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.

Unleashing the Full Power of Perovskite/Silicon Tandem Modules with Solar Trackers

Perovskite/silicon tandem photovoltaics is a promising technology to exceed the performance limit of single-junction solar cells. For utility-scale photovoltaic plants, trends and forecasts indicate that bifacial modules mounted on solar trackers will increasingly dominate the market in the next 20 years. In line with this roadmap, we investigate the outdoor performance of perovskite/silicon tandem solar cells mounted on a horizontal single-axis solar tracker in an environment with elevated solar insolation and albedo. We experimentally demonstrate in such conditions that a bifacial monolithic tandem solar cell installed on a tracker can generate 55% more power than an equivalent tandem mounted on a fixed rack. On the basis of our results, we anticipate a significant economic advantage by coupling bifacial monolithic perovskite/silicon tandem technology with solar trackers.

Sensitivity Analysis Based Identification of Key Parameters in the Dynamic Model of a Utility-Scale Solar PV Plant

Grid modernization and innovations in clean energy are leading to a rapid increase in the penetration of inverter-based resources (IBRs) like wind power and solar photovoltaic (PV) plants in the bulk power system (BPS). To study the implications on BPS dynamics and mitigate potential risks to grid reliability, the second-generation generic renewable energy system model has been developed earlier. However, this model has three modules with 86 user-defined parameters and eight control flags to facilitate the utility-scale solar PV plant operation in different control modes. It presents a major challenge to the transmission system planning engineers. There is a critical need to identify the key parameters of the dynamic model that are highly sensitive to grid events causing large-scale voltage and frequency disturbances. In this work, a sensitivity analysis based on modified Sobol’ method is proposed for identification of the key parameters in the utility-scale solar PV plant model. The total Sobol’ sensitivity index captures even the higher-order effects of the variations in the parameters. Validation of the identified key parameters was carried out on a modified IEEE 9-bus system and 200-bus synthetic grid using PSS®E for plant-level control and local coordinated control operation of the solar PV plant. The reduced set of parameters identified in this work offer help to utility grid planning teams while conducting dynamic studies to meet the interconnection requirements.

Land Requirements for Utility-Scale PV: An Empirical Update on Power and Energy Density

The rapid deployment of large numbers of utility-scale photovoltaic (PV) plants in the United States, combined with heightened expectations of future deployment, has raised concerns about land requirements and associated land-use impacts. Yet our understanding of the land requirements of utility-scale PV plants is outdated and depends in large part on a study published nearly a decade ago, while the utility-scale sector was still young. We provide updated estimates of utility-scale PVs power and energy densities based on empirical analysis of more than 90% of all utility-scale PV plants built in the United States through 2019. We use ArcGIS to draw polygons around satellite imagery of each plant within our sample and to calculate the area occupied by each polygon. When combined with plant metadata, these polygon areas allow us to calculate power (MW/acre) and energy (MWh/acre) density for each plant in the sample, and to analyze density trends over time, by fixed-tilt versus tracking plants, and by plant latitude and site irradiance. We find that the median power density increased by 52% for fixed-tilt plants and 43% for tracking plants from 2011 to 2019, while the median energy density increased by 33% for fixed-tilt and 25% for tracking plants over the same period. Those relying on the earlier benchmarks published nearly a decade ago are, thus, significantly overstating the land requirements of utility-scale PV.

Evaluation of extreme weather impacts on utility-scale photovoltaic plant performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. This analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems. These insights can inform planning activities, especially as renewable energy continues to expand into new geographic and climatic regions around the world.

Energy analysis of utility-scale PV plant in the rain-dominated tropical monsoon climates

To limit the adverse impact of fossil fuel-generated power, energy generation from solar photovoltaic (PV) power is gaining importance. A lot of utility-scale PV power plants are being installed in tropical regions owing to the increased sunshine hours especially during the summer season. The influence of rain on the performance of PV power plants during monsoon seasons in a tropical climate is not studied in detail. This paper analyses the operational performance of a 2 MWp photovoltaic plant commissioned at the Kuzhalmannam site, Palakkad district, Kerala State, South India. The methodology includes the analysis of standard performance metrics of the plant utilizing two-year measured data and comparison with other climates. The PV plant's average performance ratio (PR) is 73.39, with an average of 15.41% capacity utilization factor (CUF) over the study period. The most deficit generation for the PV plant is observed in the months from June to August, which are part of the Southwest monsoon season. It is observed that the monsoon seasons prevailing in the region have a more substantial influence leading to a 35% reduction in energy generation from the annual average generation. Further, a comparison with other climates revealed that the specific yield obtained in rain-dominated monsoon tropical climates is lesser than dry and temperate climates.

Observed impacts of utility-scale photovoltaic plant on local air temperature and energy partitioning in the barren areas

Rapid progress of solar photovoltaic (PV) technology has caused growing interest in understanding interactions between large scale PV plants and near-surface atmosphere. However few attempts have been made to quantify the impact of PV modules on surface radiative forcing and energy partitioning process. Here, this issue is explored experimentally and analytically in the two adjacent sites located at the PV plant and the natural barren field respectively in Wujiaqu in Xinjiang of China. The results showed that the physical effect of PV panels is not symmetrical in the whole day. During the daytime, compared with the reference site, net shortwave radiative forcing increases 8%, a warming effect on the integrated underlying surface (0.1 K) and a cooling effect (∼-2.6 K) on the ground surface were found in the PV plant. 9.2% of the net radiation (NR) was converted into electric energy (PE), sensible heat flux (H) increased by 30.6% hence resulted in the convection heating effects of 0.64 K and 0.32 K on the near-surface air temperature at the height of 2 m and 10 m respectively, while latent heat flux (LE) and ground surface heat flux (GS) decreased by 49% and 3% respectively related to the reference site. At night, PV panels produce a cooling effect of −0.2 K and −2.3 K on the ground and integrated underlying surface respectively, and less GS is released in the PV plant which contribute to the cooling effects of −0.24 K and −0.08 K on the air temperature at the height of 2 m and 10 m respectively related to the reference site. In the whole day, in the PV plant, H increased by 27.6%, LE and GS decreased by 47.4% and 6.7% respectively, air temperature increased by 0.16 K and 0.1 K at 2 m and 10 m respectively.