Special Issue "Solar Radiation Forecasting and Photovoltaic Systems Performance Modeling"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 23 April 2021.

Special Issue Editor

Dr. Jesús Polo
Website
Guest Editor
Photovoltaic Solar Energy Unity (Renewable Energy Division) CIEMAT, 28040 Madrid, Spain
Interests: solar radiation; atmospheric physics; solar systems modeling; radiative transfer; remote sensing; solar power plants performance
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to announce this Special Issue of the Energies journal on Solar Radiation Forecasting and Photovoltaic Systems Performance Modeling and to encourage you to contribute to this topic. Reliable meteorological and climatic data are playing a main role as penetration of PV increases. The developments and the interest in solar irradiance forecasting have grown significantly in the last ten years. Different methodologies are being proposed for forecasting solar radiation time series as well as probabilistic forecasting, for different forecasting horizons (short-term or nowcasting to 1–2 days ahead forecasting) and for several applications. The selection of the forecasting technique or modeling approach depends on the forecast horizon and so do the required input data to the model. The link between solar irradiance forecasting and PV modeling is clear, since solar forecast for managing the grid, including storage, is needed for large penetration of PV. The modeling approaches for PV plants can be grouped as parametric and nonparametric modeling. The former makes use of physical equations to estimate energy conversion and the latter is conceived as a black box. This Special Issue is aimed at covering contributions for both methodologies for solar irradiance forecasting, including parametric and probabilistic forecasting, and methods for modeling the performance of PV systems including long-term performance and models for PV power prediction based on irradiance and temperature forecast.     

Dr. Jesús Polo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • solar irradiance forecasting
  • PV power forecasting
  • parametric and nonparametric PV models
  • total sky cameras
  • solar forecasting based on satellite imagery
  • NWPM solar forecasting
  • grid integration
  • PV system modeling
  • probabilistic forecasting as solar resource

Published Papers (9 papers)

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Research

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Open AccessArticle
A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning
Energies 2020, 13(24), 6712; https://doi.org/10.3390/en13246712 - 19 Dec 2020
Abstract
A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn [...] Read more.
A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features. Full article
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Open AccessArticle
Economic Effect of Dust Particles on Photovoltaic Plant Production
Energies 2020, 13(23), 6376; https://doi.org/10.3390/en13236376 - 02 Dec 2020
Abstract
The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation [...] Read more.
The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation is extreme. In this work, the effect of dust soiling is examined on the electricity generation of an experimental photovoltaic pilot plant, installed at the Solar Energy Research Center (CIESOL) at the University of Almería. An average reduction of 5% of the power of a photovoltaic plant due to dust contamination has been obtained, this data being used to simulate the economic effect in plants of 9 kWp and 1 and 50 MWp. The economic losses have been calculated, and are capable of being higher than 150,000 €/year in industrial plants of 50 MWp. A cleaning strategy has also been presented, which represents a substantial economic outlay over the years of plant operation. Full article
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Open AccessArticle
Fog Forecast Using WRF Model Output for Solar Energy Applications
Energies 2020, 13(22), 6140; https://doi.org/10.3390/en13226140 - 23 Nov 2020
Abstract
The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact [...] Read more.
The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface. Full article
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Open AccessArticle
AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System
Energies 2020, 13(17), 4373; https://doi.org/10.3390/en13174373 - 24 Aug 2020
Abstract
With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict [...] Read more.
With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method. Full article
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Open AccessArticle
Calculating the Energy Yield of Si-Based Solar Cells for Belgium and Vietnam Regions at Arbitrary Tilt and Orientation under Actual Weather Conditions
Energies 2020, 13(12), 3180; https://doi.org/10.3390/en13123180 - 19 Jun 2020
Abstract
Predicting actual energy harvesting of a photovoltaic (PV) installation as per site-specific conditions is essential, from the customer’s point of view, to choose suitable PV technologies as well as orientations, since most PVs usually have been designed and evaluated under standard illumination. Hence, [...] Read more.
Predicting actual energy harvesting of a photovoltaic (PV) installation as per site-specific conditions is essential, from the customer’s point of view, to choose suitable PV technologies as well as orientations, since most PVs usually have been designed and evaluated under standard illumination. Hence, the tendency lives in the PV community to evaluate the performance on the energy yield and not purely on the efficiency. The major drawback is that weather conditions play an important role, and recording solar spectra in different orientations is an expensive and time-consuming business. We, therefore, present a model to calculate the daily, monthly and annual energy yield of Si-based PV installations included in commercial panels as well as tandem solar cells. This methodology will be used to evaluate the benefit of potential new technologies for domestic and building integrated applications. The first advantage of such a numerical model is that the orientation of solar panels and their properties can be easily varied without extra experiments. The second advantage is that this method can be transferred to other locations since it is based on a minimum of input parameters. In this paper, the energy yield of PV installations for different regions in Belgium and Vietnam will be calculated. Full article
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Open AccessArticle
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information
Energies 2020, 13(4), 901; https://doi.org/10.3390/en13040901 - 18 Feb 2020
Abstract
Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate [...] Read more.
Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions. Full article
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Open AccessArticle
A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms
Energies 2020, 13(2), 435; https://doi.org/10.3390/en13020435 - 16 Jan 2020
Cited by 3
Abstract
Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the [...] Read more.
Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models’ generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months’ solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models. Full article
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Open AccessArticle
Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
Energies 2020, 13(1), 147; https://doi.org/10.3390/en13010147 - 27 Dec 2019
Cited by 10
Abstract
Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. [...] Read more.
Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency. Full article
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Review

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Open AccessReview
Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions
Energies 2020, 13(12), 3256; https://doi.org/10.3390/en13123256 - 23 Jun 2020
Cited by 6
Abstract
Significant growth in solar photovoltaic (PV) installation has been observed during the last decade in standalone and grid-connected power generation systems. However, the PV system has a non-linear output characteristic because of weather intermittency, which tends to a substantial loss in overall system [...] Read more.
Significant growth in solar photovoltaic (PV) installation has been observed during the last decade in standalone and grid-connected power generation systems. However, the PV system has a non-linear output characteristic because of weather intermittency, which tends to a substantial loss in overall system output. Thus, to optimize the output of the PV system, maximum power point tracking (MPPT) techniques are used to track the global maximum power point (GMPP) and extract the maximum power from the PV system under different weather conditions with better precision. Since MPPT is an essential part of the PV system, to date, many MPPT methods have been developed by various researchers, each with unique features. A Google Scholar survey of the last five years (2015–2020) was performed to investigate the number of review articles published. It was found that overall, seventy-one review articles were published on different MPPT techniques; out of those, only four were on non-uniform solar irradiance, and seven review articles included shading conditions. Unfortunately, very few attempts were made in this regard. Therefore, a comprehensive review paper on this topic is needed, in which almost all the well-known MPPT techniques should be encapsulated in one document. This article focuses on online and soft-computing MPPT algorithm classifications under non-uniform irradiance conditions along with their mathematical expression, operating principles, and block diagram/flow charts. It will provide a direction for future research and development in the field of maximum power point tracking optimization. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: Design of a Low-Cost Multiplexer for the Study of the Soiling Impact on the PV Panels Performance

    Authors: G. López, J. Sarmiento, J. Alonso-Montesinos, A. Marzo, J. Polo, N. Martín-Chivelet, F.J. Batlles, J. Barbero and N. Vela

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