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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 24439

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Prof. Dr. Guojiang Xiong

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Guest Editor

College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Interests: renewable energy; power system; economic dispatch; parameter identification; photovoltaic module; optimization; artificial intelligence; swarm intelligence; neural network; fault diagnosis
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of “Forecasting, Modeling, and Optimization of Photovoltaic Systems”. In the face of climate change, many countries and regions have made active efforts to restructure their energy system. For example, China set up a “30·60” plan, pledging to achieve the peak of carbon dioxide emissions by 2030 and carbon neutrality by 2060. In this context, solar energy, in particular, the technology of photovoltaic modules and systems, has become a tool to achieve carbon reduction.

Forecasting, modeling, and optimization are the fundamental problems of photovoltaic systems. In recent years, there have been many emerging technologies based on artificial intelligence, deep learning, and evolutionary computation to solve these problems. Moreover, these technologies are also interesting topics for engineering researchers. This Special Issue will deal with novel forecasting, modeling, and optimization techniques for photovoltaic systems. Topics of interest for publication include, but are not limited to, the following:

Grid integration of photovoltaic systems;
Forecasting methods and tools for photovoltaic systems;
Modeling methods and tools for photovoltaic modules and systems;
Technologies for photovoltaic maximum power point tracking;
Fault diagnosis of photovoltaic modules and systems;
Parameter identification of photovoltaic modules;
Performance evaluation of photovoltaic modules and systems.

Prof. Dr. Guojiang Xiong
Guest Editor

Manuscript Submission Information

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Keywords

renewable energy
solar energy
solar cell
grid-connected PV system
photovoltaic model
parameter extraction
power forecasting
fault diagnosis
artificial intelligence
deep learning
evolutionary computation
swarm intelligence
meta-heuristic algorithms

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Published Papers (12 papers)

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Research

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32 pages, 9428 KiB

Open AccessArticle

Short-Term Solar Irradiance Prediction with a Hybrid Ensemble Model Using EUMETSAT Satellite Images

by Jayesh Thaker, Robert Höller and Mufaddal Kapasi

Energies 2024, 17(2), 329; https://doi.org/10.3390/en17020329 - 9 Jan 2024

Cited by 4 | Viewed by 1522

Abstract

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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18 pages, 6502 KiB

Open AccessArticle

Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning

by Alen Jakoplić, Dubravko Franković, Juraj Havelka and Hrvoje Bulat

Energies 2023, 16(14), 5428; https://doi.org/10.3390/en16145428 - 17 Jul 2023

Cited by 9 | Viewed by 1811

Abstract

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model’s predictions and actual future production values, demonstrating the model’s efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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17 pages, 3645 KiB

Open AccessArticle

Synergy between Photovoltaic Panels and Green Roofs

by Fernando Alonso-Marroquin and Ghulam Qadir

Energies 2023, 16(13), 5184; https://doi.org/10.3390/en16135184 - 5 Jul 2023

Cited by 7 | Viewed by 2862

Abstract

To reduce the impact of climate change in the form of low-carbon developments, innovations in sustainable building strategies are imperative. In this regard, the performance of a double-roof house consisting of a photovoltaic panel roof (PV) and green roof (GR) was compared to traditional solar-roof buildings. The synergy between both the PV and GR systems was analysed by numerical simulations and physical modelling across the four seasons. The performance of the systems was assessed on three dimensions: indoor thermal comfort, photovoltaic temperature, and energy yield. The synergy of photovoltaic roofs with green roofs kept the indoor environment 6% more comfortable than solar roofs. The synergy also reduced the photovoltaic temperature by up to 8 °C, extending the PV life span and increasing the energy yield by 18%. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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24 pages, 12119 KiB

Open AccessArticle

Impact of Domain Nesting on High-Resolution Forecasts of Solar Conditions in Central and Eastern Europe

by Michał Mierzwiak and Krzysztof Kroszczyński

Energies 2023, 16(13), 4969; https://doi.org/10.3390/en16134969 - 26 Jun 2023

Cited by 3 | Viewed by 1278

Abstract

The article presents a study on the impact of the domain nesting method on the results of simulated solar conditions using the mesoscale Weather Research and Forecasting model. The analysis included 8 consecutive days (July 2022), which were characterized by cloudless conditions, as well as complex situations related to the passing of a cold front. The study covered a region located in Central and Eastern Europe—the southern area of eastern Germany. The results of the model simulations using the adopted domain configurations (with spatial resolutions of 9, 3, and 1 km; 3 and 1 km; and 5 and 1 km) were compared to data from ground measurements from Deutscher Wetterdienst (DWD) stations. The effect of the duration of the triggered prediction on the quality of the output data was also investigated, and for this purpose, short-term predictions covering 24 and 48 h, respectively, were selected. Research revealed the advantages of one combination of domains—3 and 1 km—over the others and showed that the results of simulations with different duration lengths were characterized by consistent results. Research supports the demand for high-quality forecasts of solar conditions, which are extremely important in the process of managing energy systems. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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16 pages, 3477 KiB

Open AccessArticle

Fault Detection and Power Loss Assessment for Rooftop Photovoltaics Installed in a University Campus, by Use of UAV-Based Infrared Thermography

by Kyoik Choi and Jangwon Suh

Energies 2023, 16(11), 4513; https://doi.org/10.3390/en16114513 - 4 Jun 2023

Cited by 3 | Viewed by 2092

Abstract

In contrast to commercial photovoltaic (PV) power plants, PV systems at universities are not actively monitored for PV module failures, which can result in a loss of power generation. In this study, we used thermal imaging with drones to detect rooftop PV module failures at a university campus before comparing reductions in power generation according to the percentage of module failures in each building. Toward this aim, we adjusted the four factors affecting the power generation of the four buildings to have the same values (capacities, degradations due to aging, and the tilts and orientation angles of the PV systems) and calibrated the actual monthly power generation accordingly. Consequently, we detected three types of faults, namely open short-circuits, hot spots, and potential-induced degradation. Furthermore, we found that the higher the percentage of defective modules, the lower the power generation. In particular, the annual power generation of the building with the highest percentage of defective modules (12%) was reduced by approximately 25,042 kWh (32%) compared to the building with the lowest percentage of defective modules (4%). The results of this study can contribute to improving awareness of the importance of detecting and maintaining defective PV modules on university campuses and provide a useful basis for securing the sustainability of green campuses. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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20 pages, 11920 KiB

Open AccessFeature PaperArticle

3D Solar Irradiance Model for Non-Uniform Shading Environments Using Shading (Aperture) Matrix Enhanced by Local Coordinate System

by Kenji Araki, Yasuyuki Ota, Akira Nagaoka and Kensuke Nishioka

Energies 2023, 16(11), 4414; https://doi.org/10.3390/en16114414 - 30 May 2023

Cited by 10 | Viewed by 3336

Abstract

Building-integrated photovoltaics (BIPVs) and vehicle-integrated photovoltaics (VIPVs) receive solar irradiance through non-uniform shading objects. Standard scalar calculations cannot accurately determine the solar irradiance of BIPV and VIPV systems. This study proposes a matrix model using an aperture matrix to accurately calculate the horizontal and vertical planes affected by non-uniform shading objects. This can be extended to the solar irradiance on a VIPV by applying a local coordinate system. The 3D model is validated by a simultaneous measurement of five orientations (roof and four sides, front, left, tail, and right) of solar irradiance on a car body. An accumulated logistic function can approximate the shading probability. Furthermore, the combined use of the 3D solar irradiance model is effective in assessing the energy performance of solar electric vehicles in various zones, including buildings, residential areas, and open spaces. Unlike standard solar energy systems, the energy yield of a VIPV is affected by the shading environment. This, in turn, is affected mainly by the location of vehicle travel or parking in the city rather than by the climate zones of the city. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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21 pages, 5906 KiB

Open AccessArticle

Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified Gray Wolf Optimization Algorithm

by Kuo-Hua Huang, Kuei-Hsiang Chao, Ying-Piao Kuo and Hong-Han Chen

Energies 2023, 16(11), 4329; https://doi.org/10.3390/en16114329 - 25 May 2023

Cited by 9 | Viewed by 1084

Abstract

In this study, a modified gray wolf optimization algorithm (GWOA) was proposed to facilitate the maximum power point tracking (MPPT) of photovoltaic module arrays (PMAs). To increase the voltage conversion ratio and achieve a voltage boost through reduced duty cycles, a high-voltage step-up converter with a coupled inductor was used to replace the conventional energy storage inductor. To achieve global MPPT, the iteration parameters of the proposed GWOA were adjusted according to the slope of the PMA power–voltage (P–V) curve. According to the simulation results, the modified GWOA is more effective in MPPT than the perturbation and observation algorithm and conventional GWOA when multiple peaks appear in the P–V curve of a shaded PMA. In addition, the modified GWOA exhibits an improved tracking speed response and steady-state response. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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11 pages, 1516 KiB

Open AccessArticle

Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania

by Sergiu-Mihai Hategan, Nicoleta Stefu and Marius Paulescu

Energies 2023, 16(11), 4290; https://doi.org/10.3390/en16114290 - 24 May 2023

Cited by 5 | Viewed by 1452

Abstract

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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12 pages, 3789 KiB

Open AccessArticle

Performance Monitoring Algorithm for Detection of Encapsulation Failures and Cell Corrosion in PV Modules

by Easter Joseph, Pradeep Menon Vijaya Kumar, Balbir Singh Mahinder Singh and Dennis Ling Chuan Ching

Energies 2023, 16(8), 3391; https://doi.org/10.3390/en16083391 - 12 Apr 2023

Cited by 2 | Viewed by 1392

Abstract

This research work aims to develop a fault detection and performance monitoring system for a photovoltaic (PV) system that can detect and communicate errors to the user. The proposed system uses real-time data from various sensors to identify performance problems and faults in the PV system, particularly for encapsulation failure and module corrosion. The system incorporates a user interface that operates on a micro-computer utilizing Python software to show the detected errors from the PV miniature scale system. Fault detection is achieved by comparing the One-diode model with a controlled state retrieved through field testing. A database is generated by the system based on acceptable training data and it serves as a reference point for detecting faults. The user is notified of any deviations based on the threshold value from the training data as an indication of an error by the system. The system offers real-time monitoring, easy-to-understand error messages, and remote access capability, making it an efficient and effective tool for both users and maintenance personnel to manage and maintain the PV system. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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21 pages, 4426 KiB

Open AccessArticle

Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution

by Derong Lv, Guojiang Xiong, Xiaofan Fu, Yang Wu, Sheng Xu and Hao Chen

Energies 2022, 15(24), 9489; https://doi.org/10.3390/en15249489 - 14 Dec 2022

Cited by 7 | Viewed by 1461

Abstract

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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25 pages, 6570 KiB

Open AccessFeature PaperArticle

A Variable-Weather-Parameter MPPT Method Based on Equation Solution for Photovoltaic System with DC Bus

by Shaowu Li, Kunyi Chen, Qin Li and Qing Ai

Energies 2022, 15(18), 6671; https://doi.org/10.3390/en15186671 - 13 Sep 2022

Cited by 3 | Viewed by 1313

Abstract

The control signals of the variable-weather-parameter (VWP) methods need to be calculated by the real-time measured data of the irradiance and temperature (I&T) sensors, which leads to the high hardware cost of the sensors. To solve this problem, the PV system with a DC bus is selected as the research subject and a novel maximum power point tracking (MPPT) method is proposed. It is named the VWP MPPT method based on the equation solution (ES-VWP method). Its control signal is directly calculated by the solution of an established equation set rather than data measured by the I&T sensors. This equation set consists of two integrated mathematical equations, which represent two different operating points of the PV system. Meanwhile, when the bus voltage is varying or unknown, a calculation method that can estimate the real-time value of the DC bus voltage is proposed. In addition, an implementation method corresponding to the ES-VWP method is also designed. Finally, some simulation experiments are carried out to verify the availability and feasibility of the ES-VWP method. Meanwhile, some simulation experiments show that the error of the equation solution is less than 0.0001. In addition, some simulation experiments illustrate that the MPPT settling times of the ES-VWP method are always less than one-tenth of the P&O method (or one-sixth of the FLC method). Compared with the existing VWP methods, it can be implemented without the use of I&T sensors or external I&T data. Meanwhile, compared with other existing MPPT methods, its better MPPT rapidity originating from the advantage of the VWP methods is inherited. This work is the first attempt to design a novel MPPT method by obtaining the real-time equation solutions of

V_{o c}

and

I_{s c}

. Meanwhile, this work is also the first attempt to solve the real-time equation of

V_{b u s}

by the solved

V_{o c}

and

I_{s c}

. In addition, this work is also the first attempt to design an implementation method for establishing an equation set by sampling two operating points of a PV system at the same time. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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Review

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18 pages, 2494 KiB

Open AccessReview

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

by Zixia Yuan, Guojiang Xiong and Xiaofan Fu

Energies 2022, 15(22), 8693; https://doi.org/10.3390/en15228693 - 19 Nov 2022

Cited by 21 | Viewed by 3298

Abstract

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis. Full article

(This article belongs to the Special Issue Forecasting, Modeling, and Optimization of Photovoltaic Systems)

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