Solar Photovoltaic Facilities Research Articles

Modeling Photovoltaic Facilities via Fuzzy Sets and Linguistic Petri Nets

This paper introduces a formal procedure based on linguistic Petri nets and fuzzy sets in order to model photovoltaic facilities and provide different alerts with respect to a correct performance. Furthermore, this procedure has been illustrated in a real case. Specifically, it is tested on six solar photovoltaic facilities (stations) owned by the company Grupo Energético de Puerto Real (GEN). The energy generated by each station has been compared through Dynamic Time Warping (DTW) and analyzed by a designed Petri net, which considers fuzzy sets and Natural Language for a better interpretation and understanding for the (nonexpert) user. The results show that the proposed procedure offers excellent results, bigger profits for the company and that it can be scalable and exportable to other facilities.

A multi‐objective low‐voltage ride‐through control strategy for three‐phase grid‐interfaced solar power plant during symmetrical and asymmetrical faults†

SummaryThis paper presents a low‐voltage ride‐through capability (LVRT) solution for the photovoltaic (PV) grid‐connected inverters compliant with the Indian grid code. The LVRT strategy ensures the uninterrupted connection of solar energy to the grid, safeguarding against system shutdown in the event of various grid faults. During this time, the inverter supplies reactive power to the utility grid, effectively utilizing converter capacity and aiding the restoration of the voltage profile at the point of common coupling (PCC). The system used in this study involves a two‐stage grid‐interfaced solar PV setup with a boost converter that tracks the maximum power point (MPP) and an inverter that maintains the DC‐link voltage. A modified dynamic braking chopper control (MDBCC) has also been implemented to minimize the DC‐link voltage oscillations during asymmetrical faults while simultaneously injecting the sinusoidal current into the grid. To improve grid synchronization in the presence of unbalanced abnormalities, a modified dual second‐order generalized integrator phase‐locked loop (DSOGI‐PLL) has been employed to track the grid voltage angle precisely. Furthermore, a new control system has been developed in conjunction with the LVRT approach. The suggested system is simulated in MATLAB/Simulink to validate the efficacy of the LVRT control method during symmetrical and asymmetrical faults using a 100‐kW two‐stage grid‐interfaced solar PV facility. Finally, for experimental validation, a real‐time test bench (RTS) OPAL‐RT 4510 is used to implement the setup with its control, and the practical results are compared with the simulation outcomes.

Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction

Due to its significant solar energy generation, solar PV power facilities have recently been utilized. Although PV power stations are highly preferable, the state’s fundamental drawback is that its output current qualities are unpredictable. Consequently, to ensure a balance and complete functioning, it would be crucial to build systems that allow accurate future projections of solar PV production in the short or medium term. Research postulates a strategy for using deep learning to estimate the short-term electricity generated by solar photovoltaic facilities. This study offers a novel method for predicting photovoltaic systems output power utilizing a Hybrid Deep Neural Network framework, making significant advancements in the field of deep learning applications to transmission system prediction issues. CNN and LSTM are combined in the postulated HDNN paradigm. Traditional deep learning techniques are employed in the initial stage. The effectiveness assessments of these techniques are instead presented in greater detail after they have been trained using firefly optimization techniques. The method with the highest reliability is chosen out of all the techniques used in previous research. Deep learning and power efficiency create a combination that appears to have a successful future, predominantlyin improving sustainable management and the digitalization of the electrical sector.

The Use of Power Gyrator Structures as Energy Processing Cells in Photovoltaic Solar Facilities

This paper will provide a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows increasing the performance of the whole installation. Thus, the design, simulation and implementation of a G-type power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

Capacitorless DC-DC Regulator as a Candidate Topology for Photovoltaic Solar Facilities

Linear-assisted DC/DC converters (or linear-switching hybrid DC/DC converters) consist of a voltage linear regulator (classic NPN or nMOS topologies and LDO) connected in parallel with a switching DC/DC converter. They are a good candidate for energy processing in photovoltaic solar facilities. In order to control these hybrid structures, different strategies exist, allowing fixing the switching frequency as a function of some parameters of the linear regulator. This article compares two control strategies that, although can be applied to the same circuital structure of linear-assisted converter, are sensibly different. The first one, reported in previous literature, cancels completely the average current through the linear regulator in steady state to achieve a reduction of the losses. Thus the efficiency of the whole system increases and almost equals the one of the standalone switching converter. The proposed approach, in spite of a slightly increment of linear regulator’s losses, reduces the output ripple due to the crossover distortion of linear regulator output stage.

The Inclusion of Power Gyrator Topologies as Energy Processing Cells in Photovoltaic Solar Conversion

This paper will provide a classification of high efficiency switching power-gyrator structures and their use as cells for energy processing in photovoltaic solar facilities. Having into account the properties of these topologies presented in the article, their inclusion in solar facilities allows increasing the performance of the whole installation. Therefore, the design, simulation and implementation of a Gtype power gyrator are carried out throughout the text. In addition, in order to obtain the maximum power from the photovoltaic solar panel, a maximum power point tracking (MPPT) is mandatory in the energy processing path. Therefore, the practical implementation carried out includes a control loop of the power gyrator in order to track the aforementioned maximum power point of the photovoltaic solar panel.

Creation of an Internet of Things (IoT) system for the live and remote monitoring of solar photovoltaic facilities

Reliable and widely accepted, renewable energy sources stand as the optimal substitute for fossil fuels in meeting our growing energy demands. Specifically, solar energy can be harnessed into electrical power through solar cells. Many solar installations are situated in remote locations like rooftops, mountains, and deserts. Effective monitoring of these solar photovoltaic systems is crucial to maximize their performance. Monitoring solutions come in various configurations. The simplest approach involves offline data collection, entailing the placement of a data collection device on the installation which then stores data locally for a specific timeframe. However, this conventional monitoring method falls short in providing real-time data. In contrast, leveraging Internet of Things (IoT) technology to oversee solar photovoltaic power generation offers a substantial performance boost. This project aims to develop an IoT-powered system for real-time remote monitoring of solar photovoltaic installations. The collected data is stored in the IoT cloud, accessible through an application via an active internet connection from anywhere worldwide. This grants immediate insights into the installation’s status, facilitating maintenance and prompt fault detection.

An Action‐Oriented Approach to Make the Most of the Wind and Solar Power Complementarity

AbstractSolar and wind power are called to play a main role in the transition toward decarbonized electricity systems. However, their integration in the energy mix is highly compromised due to the intermittency of their production caused by weather and climate variability. To face the challenge, here we present research about actionable strategies for wind and solar photovoltaic facilities deployment that exploit their complementarity in order to minimize the volatility of their combined production while guaranteeing a certain supply. The developed methodology has been implemented in an open‐access step‐wise model called CLIMAX. It first identifies regions with homogeneous temporal variability of the resources, and then determines the optimal shares of each technology over such regions. In the simplistic application performed here, we customize the model to narrow the monthly deviations of the total wind‐plus‐solar electricity production from a given curve (here, the mean annual cycle of the total production) across five European domains. For the current shares of both technologies, the results show that an optimal siting of the power units would reduce the standard deviation of the monthly anomalies of the total wind‐plus‐solar power generation by up to 20% without loss in the mean capacity factor as compared to a baseline scenario with an evenly spatial distribution of the installations. This result further improves (up to 60% in specific regions) if the total shares of each technology are also optimized, thus encouraging the use of CLIMAX for practical guidance of next‐generation renewable energy scenarios.

Selecting optimal power consumption patterns in the Republic of Altai local grid system based on renewable and alternative power sources

The authors develop a system of optimal planning of power consumption in the Republic of Altai local grid system based on a large proportion of renewable and alternative generating power sources. The studies were carried out using the linear programming method for power consumption patterns used by generating consumers. Power consumption charts by seasons and hourly retrospective rows of weather data by wind current velocity and solar insolation over 2021 were used as source data. Wind turbines, solar photovoltaic facilities, hydro-power plants, and energy accumulators were considered as local power consumption facilities to calculate electricity generation. The article provides calculation results for electricity generation in winter, since this is when a higher power deficiency is observed. It was shown that power consumers in the region under study can independently install additional generating power sources in the form of wind turbines, solar photovoltaic facilities, hydro-power plants, and energy accumulators due to electricity deficiency. Their combined generation mitigates the unpredictability of power generation by renewable sources. The authors propose a method that allows generating consumers to minimize their material and financial expenses and reduce the carbon footprint. The significance of the study consists in the substantiation of a hybrid power supply system with a high proportion of renewable and alternative power sources, which is implemented in the Republic of Altai and can be reproduced in other local energy systems with similar weather conditions.

Photovoltaic solar farms in California: can we have renewable electricity and our species, too?

Photovoltaic solar power generating facilities are proliferating rapidly in California and elsewhere. While this trend is welcomed for many reasons (e.g., reducing greenhouse gas emissions), these facilities also can have profound environmental impacts, particularly to local species populations. These impacts become more significant when species of conservation concern are affected. In the San Joaquin Desert region in central California, a number of conservation measures have been routinely implemented on solar facilities, and these measures have facilitated continued use of the facilities by a number of species of conservation concern. Some of the more significant measures include permeable security fences, vegetation management, movement corridors, avoiding critical features such as dens and burrows, and vehicle speed limits. Detailed studies have been conducted on San Joaquin kit foxes (Vulpes macrotis mutica) using solar facilities in the San Joaquin Desert. Demographic and ecological attributes of foxes are similar between foxes using the facilities and foxes on nearby reference sites, and values for foxes on solar sites are within the ranges of values for foxes reported from sites within core population areas. Facilitated by the conservation measures, kit foxes are using at least six facilities in the San Joaquin Desert as are a variety of other species of conservation concern. This successful model also potentially could be adapted to other ecosystems and applied to facilities in regions outside of the San Joaquin Desert, such as the Mojave Desert. Determining whether species in other regions can use photovoltaic solar facilities and identifying the most efficacious conservation measures will require time and testing, and these efforts would benefit from collaborative efforts among landowners, solar developers, natural resources agencies, researchers, and others. The San Joaquin Desert facilities and a recent demonstration facility in the Mojave Desert provide strong evidence that solar facilities can be constructed and operated in a manner that also accommodates continued use of the facilities by some species of conservation concern.

Aquatic Habitat Bird Occurrences at Photovoltaic Solar Energy Development in Southern California, USA

The development of photovoltaic (PV) utility-scale solar energy (USSE) in the desert Southwest has the potential to negatively affect birds through collision mortality. Based on early patterns in fatality monitoring data, the lake effect hypothesis (LEH) was developed and suggested that birds misinterpret PV solar panels for water. As the LEH was only recently defined and inference beyond bird mortality is limited, our research objective was to examine the species composition, abundance, and distribution of live and dead aquatic habitat birds at five PV solar facilities and paired reference areas in southern California. Further, we collected data from a small regional lake as an indicator of the potential aquatic habitat bird community that could occur at our study sites. Using an ordination analysis, we found the lake grouped away from the other study sites. Although the bird community (live and dead) at the solar facilities contained aquatic habitat species, Chao’s diversity was higher, and standardized use was more than an order of magnitude higher at the lake. Finally, we did not observe aquatic habitat bird fatalities in the desert/scrub and grassland reference areas. Thus, the idea of a “lake effect” in which aquatic habitat birds perceive a PV USSE facility as a waterbody and are broadly attracted is likely a nuanced process as a PV solar facility is unlikely to provide a signal of a lake to all aquatic habitat birds at all times.

Solar farm voltage anomaly detection using high-resolution [formula omitted]PMU data-driven unsupervised machine learning

The usual means of solar farm condition monitoring are limited by the typically poor quality and low-resolution data collected. A micro-synchrophasor measurement unit has been adapted and integrated with a power quality monitor to provide the high-resolution, high-precision, synchronised time-series data required by analysts to significantly improve solar farm performance and to better understand their impact on distribution grid behaviour. Improved renewable energy generation at large solar photovoltaic facilities can be realised by processing the enormous amounts of high-quality data using machine learning methods for automatic fault detection, situational awareness, event forecasting, operational tuning, and planning condition-based maintenance. The limited availability of existent data knowledge in this sector and legacy performance issues steered our exploration of machine learning based approaches to the unsupervised direction. A novel application of the Clustering Large Applications (CLARA) algorithm was employed to categorise events from the large datasets collected. CLARA has been adapted to recognise solar site specific behaviour patterns, abnormal voltage dip and spike events using the multiple data streams collected at two utility-scale solar power generation sites in England. Fourteen days of empirical field data (seven consecutive summer days plus seven consecutive winter days) enabled this analytical research and development approach. Altogether, ∼725 million voltage measurement data points were investigated, and automatic voltage anomaly detection demonstrated.

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

Integrating social considerations in multicriteria decision analysis for utility-scale solar photovoltaic siting

Solar photovoltaic facilities are cropping up with ever-increasing size and frequency. Often, these utility-scale projects incite opposition from local communities – once branded a “Not in my backyard” reaction. Here we conduct a cross-disciplinary review covering two interrelated topics: 1) solar siting analyses utilizing a geographical information system – multicriteria decision analysis (GIS-MCDA) approach and 2) social science research discussing public responses to solar photovoltaic development. We follow this with a discussion on how to improve GIS-MCDA by more explicitly including aspects of the people and the place under study. Past research leans primarily upon technical and economic criteria directly related to the solar facility itself, largely ignoring social considerations, which are important for public acceptability and fairness. Incorporating siting criteria reflecting the interests of local people and how they interact with their immediate environment would not only improve the process for developers, but it could also be adapted to mitigate inequalities pervasive in the historical siting of energy facilities.

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Wildfire smoke and other particulate matter can substantially inhibit solar photovoltaic (PV) generation production. While solar PV facilities may not be located in areas with a high fire risk, smoke from wildfires can travel hundreds of kilometers impacting a large number of facilities. This paper proposes a geospatial wildfire PV capacity model to quantify the anticipated temporal reduction in PV capacity due to wildfire smoke. A case study using data for two time periods from the 2020 California wildfires and real utility scale solar generation data evidences the model's high accuracy. Results argue that wildfire smoke can cause significant temporal solar generation capacity reductions over wide geographic regions. Application of the proposed model to inform power system resiliency planning is demonstrated for two use cases: generation scheduling and siting. With meteorological service providers beginning to release smoke forecasts, our geospatial wildfire PV capacity model enables balancing authorities to make use of this information to proactively schedule generation to compensate for reductions in PV capacity. The trained model also produces geospatial derate maps that can enable generation developers to consider historical capacity derates due to smoke when making siting or planning decisions.

태양광 설비투자에 대한 제도적 유인방안 연구: 감가상각법의 경제적 효과 분석

The value of tangible assets depreciates over their useful life and this depreciation should be adequately reflected in any tax or financial reports. However, the method used to calculate depreciation can impact the financial performance of solar projects due to the time value of money. Korean tax law stipulates only one method for calculating the depreciation of solar photovoltaic facilities: the straight-line method. Conversely, USA’s tax law accepts other depreciation methods as solar incentives, including the modified accelerated cost recovery system (MACRS) and Bonus depreciation method. This paper compares different depreciation methods in the financial analysis of a 10 MW solar system to determine their effect on the financial results. When depreciation was calculated utilizing the MACRS and Bonus depreciation method, the internal rate of return (IRR) was 10.9% and 16.4% higher, respectively, than when the Korean straight-line depreciation method was used. Additionally, the increased IRR resulting from the use of the two US methods resulted in a 20.5% and 27.4% higher net present value, respectively. This shows that changing the depreciation calculation method can redistribute the tax amount during the project period, thereby increasing the discounted cash flow of the solar project. In addition to increasing profitability, USA’s depreciation methods alleviate the uncertainty of solar projects and provide more flexibility in project financing than the Korean method. These results strongly suggest that Korean tax law could greatly benefit from adopting USA’s depreciation methods as an effective incentive scheme.

Photons vs. firewood: female (dis)empowerment by solar power in India

Renewable energy transitions are accelerating in the Global South. Yet many large-scale renewable energy infrastructures are developed on public lands with unknown impacts on commons access and usage. A prime example of this is the Gujarat Solar Park (GSP) in India, which is one of the world’s largest solar photovoltaic facilities. The GSP is situated on 2,669 acres of previously common property, which has historically been used by female pastoralists for firewood collection. In this paper, we examine the following research questions: How do gender and caste power shape natural resource access in this region?; Does the Gujarat Solar Park exacerbate already gendered social-economic-political asymmetries? Our study utilizes a feminist political ecology framework to analyze the social dimensions of the GSP, drawing on recent work in this vein that uses a postcolonial and intersectional approach to examine the production of social difference through the spatial processes and political economy of solar energy generation. We find that the enclosure of public ‘wastelands’ to develop the Gujarat Solar Park has dispossessed resource-dependent women of access to firewood and grazing lands. This spatial dislocation is reinforcing asymmetrical social power relations at the village scale. Intersectional subject-positions are (re)produced vis-à-vis the exclusion of access to firewood in the land enclosed for the solar park. Affected women embody this dispossession through inter- and intra-village emotional geographies that cut across caste, class and gender boundaries.

Photovoltaic Generation Impact Analysis in Low Voltage Distribution Grids

Due to a greater social and environmental awareness of citizens, advantageous regulations and a favourable economic return on investment, the presence of photovoltaic (PV) installations in distribution grids is increasing. In the future, not only a significant increase in photovoltaic generation is expected, but also in other of the so-called distributed energy resources (DER), such as wind generation, storage, electric vehicle charging points or manageable demands. Despite the benefits posed by these technologies, an uncontrolled spread could create important challenges for the power system, such as increase of energy losses or voltages out-of-limits along the grid, for example. These issues are expected to be more pronounced in low voltage (LV) distribution networks. This article has two main objectives: proposing a method to calculate the LV distributed photovoltaic generation hosting capacity (HC) that minimizes system losses and evaluating different management techniques for solar PV inverters and their effect on the hosting capacity. The HC calculation is based on a mixture of deterministic methods using time series data and statistical ones: using real smart meters data from customers and generating different combinations of solar PV facilities placements and power to evaluate its effect on the grid operation.

Trends in performance factors of large photovoltaic solar plants

Abstract This short communication discusses the two parameters recently emerged as a Key Performance Indicator of solar energy facilities, the mean capacity factor over a year, and the standard deviation of the capacity factor computed with high-frequency sampling. Both parameters impact directly and indirectly on the Levelised Cost Of Electricity (LCOE), permitting to quantify the energy production by the specific facility, and the attribution of the grid energy storage costs to the specific facility. The annual average capacity factors of the latest, largest, 53 photovoltaic (PV) solar energy facilities in the US, vary between 10% and 36%, with a mean value of 27% and a standard deviation of 5%. There are large differences also in between plants located in areas of a similar solar resource. Inference of long term performance degradation or O&M costs is difficult. We know from Australia that solar PV facilities work with high-frequency capacity factors’ standard deviations larger than the mean, for coefficients of variability above unity, and significant variability also at the grid level. This variability necessitates of energy storage. High-frequency data to assess the standard deviation of the individual US facility contribution to the different grids, as well as the energy storage needed for every grid, is unavailable. Construction cost data are less reliable than energy production data and mostly missing. Based on data for 15 plants, completed between 2014 and 2017, the construction cost dramatically varies between different facilities. The specific cost varies between 1,719 [US$/kW] and 7,143 [US$/kW], an average of 3,983 [US$/kW]. Considering the actual generating power vs. the nominal generating power, the specific cost varies between 6,374 and 22,806, an average 14,006 [US$/kW]. Hence, an accurate prediction of the LCOE of large PV facilities in the US is presently difficult.

A summary of bird mortality at photovoltaic utility scale solar facilities in the Southwestern U.S.

Recent trends in renewable energy development in the United States (U.S.) show that new installed capacity of utility-scale solar energy has exceeded 30% of total installed capacity of all sources per year since 2013. Photovoltaic solar energy provides benefits in that no emissions are produced; however, there are potential impacts from photovoltaic solar development on birds that include habitat loss and potential for collision mortality. Only 2 papers in the peer-reviewed literature present fatality information from fatality monitoring studies at a photovoltaic utility-scale solar energy facility; however, more data exists in unpublished reports. To provide a more comprehensive overview of bird mortality patterns, we synthesized results from fatality monitoring studies at 10 photovoltaic solar facilities across 13 site-years in California and Nevada. We found variability in the distribution of avian orders and species among and within Bird Conservation Regions, and found that water-obligate birds, which rely on water for take-off and landing, occurred at 90% (9/10) of site-years in the Sonoran and Mojave Deserts Bird Conservation Region. We found that a cause of mortality could not be determined for approximately 61% of intact carcasses, and that approximately 54% of all carcasses were feather spots, introducing uncertainty into the interpretation of the fatality estimates. The average annual fatality estimate we calculated for photovoltaic solar (high-end estimate of 2.49 birds per megawatt per year) is lower than that reported by another study (9.9 birds per megawatt per year) that included one photovoltaic facility. Our results provide a summary of fatalities in bird conservation regions where the facilities are located, but expanding our conclusions to new regions is limited by the location of facilities with fatality monitoring data.