Photovoltaic Power System Research Articles

Optimal allocation of solar photovoltaic distributed generation for performance enhancement of electrical distribution networks considering optimal volt‐var regulation under uncertainty and high load variation

AbstractThis article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt‐var regulation control of PV inverters compared to several valid and robust optimization algorithms.

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

Design of an Electric Vehicle Charging System Consisting of PV and Fuel Cell for Historical and Tourist Regions

One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity.

Stand-alone Photovoltaic System for a House in Misurata City

Direct conversion of solar irradiance into electricity can be used as electrical power source for home to meet its daily energy requirement. This paper presents the complete design of a stand-alone Photovoltaic system (SAPV) to supply electric power for a traditional house in city of Misrata-Libya according to its energy requirements. This paper outlines in detail the procedure for specifying each component of the standalone photovoltaic power system. This system can be installed on the roof and the south side of the cabin. The results show that the sizing of PV stand-alone system depends on the load data, the solar resource data and the investment cost of system components. To power these appliances, 79 strings of one with average daily energy of 13.9 kWh. In addition, 52 batteries in the battery bank with system battery capacity of 1300 Ah and voltage 48V, and one charge controller are required. Simulation results and analyses are presented to validate the proposed system configuration.

Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Hydrogen production to combat power surpluses in hybrid hydro–wind–photovoltaic power systems

The complementary operation of hydropower, photovoltaic, and wind power can promote the integration of renewable energy resources into the grid. However, the competition of power transmission channels among multiple energy raises surpluses significantly. This study equips a hydrogen production plant for the hydro–wind–PV hybrid system to utilize the power surplus. First, a multi-scale nested joint operation model that considers both long-term and short-term operation strategies is proposed to simulate the operation process of the hybrid system. Then, the potential surplus of the system is evaluated and used as the power source of the hydrogen production plant. Lastly, the optimal hydrogen production plant size is determined based on economic and technical analysis. A case study is performed with the Yalong River basin of China. The results show that (McElroy and Chen, 2017 (1)) The deployment of a 260 MW hydrogen production system has the potential to boost the annual power generation benefit of the hybrid system by 610 million CNY； (2) The fluctuation of hydropower station output is effectively reduced, subsequently mitigating operational risks. Especially during flood season, the power variation coefficient experiences a notable decrease of approximately 75%； Liu and Xu (2022) (3) The deployment of hydrogen production plants is more economically feasible than expanding power transmission channels, particularly when considering long-distance power transmission. Furthermore, given that hydrogen production plants frequently operate under fluctuating power conditions, the electrolyzers and compressors must exhibit prompt responsiveness to these power variations.

A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting

Under the goals of carbon neutrality and peak carbon emissions, photovoltaic (PV) power generation is widely valued for its clean and green characteristics. However, the uncertainty and randomness of PV power pose challenges to energy management. Therefore, this study proposed a novel bimodal feature fusion network-based deep learning model with an intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. First, a threshold-guided iNNE-based outlier detection and repair method is designed for preprocessing PV data. Second, a bimodal feature fusion network was proposed to extract global and local features from PV power sequences, and the environmental factors-based rime optimization algorithm with growth mutation strategy and humidity perception mechanism was devised to optimize model's hyperparameters. Additionally, a photovoltaic power interval prediction model with a volatility segmentation strategy was introduced. Finally, the effectiveness of the proposed model, algorithm, and strategies was validated using measured datasets. The results demonstrated that under various weather conditions, the proposed model achieved point prediction evaluation metrics with an R2 exceeding 98 % and a prediction interval evaluation metric with a Prediction Interval Coverage Probability of 85.07 %. The obtained outcomes contribute to providing a basis for decision-making in the scientific scheduling and management of PV power systems.

Determination of Solar Irradiance Benchmarks for Municipal Off-grid Photovoltaic Power System Development in Lagos State, Nigeria

The paper examined the solar irradiance benchmarks for off-grid photovoltaic power systems development in Ikeja, Lagos State, Nigeria as a power supply alleviation strategy in the State and strategic model for other sponsors in Lagos State and Nigeria. Solar irradiance analysis, an energy technology foresight analysis method for effective energy planning and evaluation, was used. The solar irradiance analysis determined three solar irradiance measurements – the NiMet pyranometer, the NASA satellite, and the Mechlouch and Brahim (2008) model. The study determined the model graph to be incongruous with the NiMet pyranometer and NASA satellite-derived graph plots while the NIMet and NASA satellite plots essentially overlapped. The study adjudged the model graph as being theoretical and inappropriate while the NiMet and NASA readings gave realistic plots which were deemed acceptable as suitable solar irradiation benchmarks. In conclusion the study established solar irradiance benchmarks of 15 MJ/m2/day (upper (maximin) value) and 20 MJ/m2/day (lower (minimax) value) for the photovoltaic power systems development in Ikeja, Lagos State, Nigeria.

Assessment of Power Quality Enhancement in a Grid-tied PV Network via ANN-based UPQC

Background: Microgrid is the recent decade terminology that surpasses the long-run issues associated with the public and utility grids. Among the renewable energy sources, solar PV units have gained greater importance owing to their huge potential availability and laidback operating characteristics on technological grounds. Conversely, it offers pollution-free electricity and perhaps the dependability is volatile in most situations. The literature study accumulates the foresaid setback and presents the fluctuation-less and controlled standard quality of power outputs. Objective: The aim of this particular research is to propose an assessment of Power Quality enhancement in a Grid-tied photovoltaic (PV) network via ANN-based UPQC. The novel idea behind this proposed approach is the UPQC component which deliberately regulates and controls the power system to achieve higher levels of power quality, ultimately meeting the recent IEEE standards. Method: This particular research enhances the performances of UPQC employed in the microgrid unit by replacing the traditional PI controller with a multi-layered feed-forward-type ANN controller for the current regulation of the series active filter. Additionally, a training algorithm for the ANN controller is built, trained and simulated via MATLAB/Simulink platform. The ANN-based UPQC is proposed to alleviate the power quality challenges like sag and swell in voltage, harmonic distortion, the time required for voltage compensation, and power factor. Therefore, UPQC is equipped to enrich the standard of power transfer at the point of common coupling inside the power frameworks, respectively. Result: Finally, the simulation results are presented to validate the operation of the grid-tied PV network via an ANN-based UPQC system. To show the enriched performance of the proposed topology, a comparative analysis is made with PI controller-based UPQC, and outcomes infer to be in agreement with the theoretical discussions. Also, the ANN-based proposed approach reduces the restoration time and THD as well under both sag and swell conditions, respectively. Conclusion: In this articulated work, a PV power system network with a DC-DC converter and three-phase inverter is employed for grid integration. The peak power extraction is ensured via a DC-DC converter with an incremental conductance algorithm. Both UPQCs are analysed and experimented via MATLAB/Simulink platform with inconstant nonlinear loads to investigate the indices mentioned above and corroborate the same within the operating regions.

Maximum power point tracking using unsupervised learning for photovoltaic power systems

ABSTRACT The Maximum Power Point Tracking (MPPT) technique in the solar energy field optimises the performance of solar panels in different atmospheric conditions and variable loads. In this study, we present a new method that uses unsupervised learning (K-means clustering) to identify the atmospheric clusters of solar irradiance and cell temperature (G, T) and delimit homogeneous atmospheric zones or clusters to reduce the search space of the optimal parameters ( V mp , I mp , and P mp ). The data collected for one year is segregated into 12 clusters; in every cluster, 04 regions are defined based on every cluster’s centroid ( G c , T c ). A local search of the reference voltage/Duty cycle per cluster region is initiated for every sensed (G, T). Variable atmospheric conditions and resistive loads are tested. The results show that the efficiency of the DC/DC converter is 97.5% with a settling time (4.013 ms/5.577 ms) compared to the Perturb and Observe (P&O) the conventional tracking method and the Particle Swarm Optimisation (PSO), both applied locally inside a cluster and a deviation of 2% from the global maximum.

A comparison of several maximum power point tracking algorithms for a photovoltaic power system

This paper presents a comparative study between traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for Photovoltaic (PV) powered DC Shunt Motors. Given the nonlinearity of PV systems, they require nonstandard approaches to harness their full potential. Each PV module has a unique maximum power point on its IV curve due to its nonlinear characteristic nature. Power electronic converters are utilized to enable operation at that point. There are many different algorithms described in the introduction, each with its have their own advantages and drawbacks. Recognizing the potential enhancement of PV system efficiency through effective Maximum Power Point (MPP) tracking, this paper evaluates five MPPT methods under varying DC loads. The five algorithms will be as follows: Incremental Conductance and Perturb and Observe as traditional algorithms. Fuzzy Logic Control, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference Systems as Intelligent Algorithms. While traditional algorithms generally produced acceptable results except for Perturb & Observe, intelligent algorithms performed well under rapidly changing solar radiation conditions. Due to inadequate data, intelligent algorithms relying on data training struggled to track the maximum power point when the temperature changed due to inadequate data used for the training. The analysis focuses on the time required by each method to reach peak power under different load conditions, solar irradiance, and temperature variations. The advantages and disadvantages of each MPPT with a shunt DC motor are detailed in the comparative study.

Scientific Mapping of Machine Learning Methods in Predicting Power Output of Solar Photovoltaic Power Systems

Objective: This study aimed to conduct a scientometric mapping of the scientific literature on prediction models in photovoltaic solar energy generation, with a special focus on grid-connected photovoltaic systems (GCPV), aiming to provide important insights for researchers, policymakers, and professionals interested in advancing the integration of photovoltaic solar energy into the current energy distribution system. Theoretical Framework: In this section, the main concepts and theories underpinning the research are presented, focusing on prediction models in photovoltaic solar energy generation, as well as grid-connected photovoltaic systems (GCPV). Method: The methodology adopted comprised a bibliometric approach, analyzing publications indexed in the Scopus and Web of Science databases over the last decade, using the Biblioshiny software from RStudio. Results and Discussion: The results revealed a significant growth in academic production, identifying key authors, leading research countries, and influential journals in the field. Central and emerging themes were also mapped, along with research gaps and opportunities in the field of photovoltaic solar energy. Research Implications: The practical and theoretical implications of this research include insights into how the results may influence the integration of photovoltaic solar energy into the energy distribution system, impacting areas such as scientific research, policy development, and professional practice. Originality/Value: This study contributes to the literature by offering a comprehensive mapping of research on prediction models in photovoltaic solar energy generation, highlighting gaps and opportunities to advance the field, as well as providing valuable insights for various stakeholders interested in this area.

Impact of a power ramp event on photovoltaic system power quality under different weather conditions and operating powers

Impact of a power ramp event on photovoltaic system power quality under different weather conditions and operating powers

Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling

In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 > 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite-derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.

Comparative Analysis of Hybrid Maximum Power Point Tracking Algorithms Using Voltage Scanning and Perturb and Observe Methods for Photovoltaic Systems under Partial Shading Conditions

Partial shading significantly affects the performance of photovoltaic (PV) power systems, rendering traditional maximum power point tracking (MPPT) methods ineffective. This study proposes a novel hybrid MPPT algorithm integrating voltage scanning and modified Perturb and Observe (P&O) techniques to overcome the limitations posed by partial shading. This algorithm has a simple structure and does not require panel information such as the number of panels or voltage due to its voltage scanning feature. To test the proposed algorithm, a grid-connected PV power system with a power of 252.6 kW was created in the MATLAB/Simulink environment. In this power system, six different PS conditions, containing quite challenging situations, were listed in three different scenarios and simulated. The proposed algorithm was compared with the voltage scanning and P&O and voltage scanning and variable-step P&O methods. It was observed that the proposed algorithm has lower power fluctuations compared to the other two traditional methods. Additionally, this algorithm managed to achieve higher efficiency than the other methods in some cases.

Study on the optimal layout of roof vents and rooftop photovoltaic of the industrial workshop

Roof vents are crucial for facilitating natural ventilation within industrial workshops. However, their substantial size and height can limit available roof space and create extensive shadow areas, thereby impeding the installation of photovoltaic (PV) systems on industrial roofs. This study quantitatively assesses the influence of roof vent positioning, morphology, and roof architecture on the natural ventilation efficiency of workshops with varying indoor heat sources. Subsequently, the study evaluates how different vent configurations affect the installable area for rooftop solar panels. Findings suggest that shifting from traditional roof ridge vents directly above a heat source to north-side vents inclined towards the roof can marginally decrease the ventilation flow rate by approximately 5 %, translating to a minor temperature rise of around 0.46 °C within the occupied zone. Nonetheless, the Predicted Mean Vote (PMV) thermal comfort indices remain consistent across scenarios. Notably, adopting north-side vents, either flush with the roof or inclined, can enhance the annual energy yield of the rooftop PV system by 15.49 % and 22.67 %, respectively, under equivalent solar irradiation. Thus, strategic adjustments to vent positioning, form, and roof design can substantially boost the photovoltaic system's power output without compromising the workshop's natural ventilation performance.

A new wide input voltage DC-DC converter for solar PV systems with hybrid MPPT controller

The present working conventional power generation systems utilization is reducing day by day because of their demerits are more functioning cost, high carbon dioxide emission, more complexity in handling, and required high installation area. So, the current power generation company focuses on Renewable Energy Sources (RES) which are wind, tidal, and solar. Here, the solar power network is utilized for supplying electricity to the electrical vehicle battery charging system. The Solar photovoltaic (PV) modules supply nonlinear power which is not useful for automotive systems. To maximize the supply power of the solar PV system, an Adaptive Step Genetic Algorithm Optimized (ASGAO) Radial Basis Functional Network (RBFN) is utilized for tracking the working point of the solar PV module thereby enhancing the operating efficiency of the overall system. The features of this proposed hybrid Maximum Power Point Tracking (MPPT) controller are quick system dynamic response, easy operation, quick convergence speed, more robustness, and high operating efficiency when equalized with the basic MPPT controllers. The major issue of solar PV modules is low supply voltage which is increased by introducing the wide input voltage DC-DC converter. The merits of this introduced converter are low-level voltage stress on diodes, good quality supply power, high voltage gain, plus low implementation cost. Here, the introduced converter along with the AGAO-RBFN controller is analyzed by selecting the MATLAB/Simulink environment. Also, the proposed converter is tested with the help of a programable DC source.

Voltage Error Estimation-Based Fault Diagnosis Method for Three- phase PWM-inverter in Grid-connected Photovoltaic Power System Without any Over-rating

Inverter is an essential component of a grid connected PV system. In fact, an open-switch fault in this power converter could result an important system malfunction and consequently leads to system disconnection. In this context, this paper focuses on the fault-tolerant control of PWM-inverter with redundancy leg for grid-connected PV system. In addition, a fast method of fault detection and compensation is used to maintain the continuous operation of the converter when there is a half-leg open-circuit fault (IGBT+ anti-parallel diode) on one of PWM-inverter legs. Therefore, it can detect this type of fault and compensate it in less than 50 μs by using a time criterion instead of voltage criterion. Also, the fuzzy logic technique is employed to control the active and reactive power injected into the network without exceeding the whole system power capacity limits. Note that, in this study, the active and reactive power references of the chosen operating point (the reactive-to-active power ratio: tgφ = Q/P = 0.19) are based on experimental tests. Simulation results show the feasibility and the effectiveness of the proposed fault diagnosis method in terms of fault detection and service continuity in presence of a half-leg open-circuit fault in PWM-inverter without any over-rating of the PV system components or instability.

Risk Assessment of Radial Distribution Systems using Modified Jelly Fish Search Algorithm to analyse the Performance Indices

One of the essential techniques for figuring out Power Distribution System performance is reliability evaluation. With time, the range of methods for assessing reliability has grown, and the distribution system's evolution has also become more intricate. The likelihood of a network failing grows with time once it begins to function, especially if it is used for an extended period. Reliability indices have been evaluated using different algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and various modified versions of algorithms. The Jelly Fish Search Algorithm has been used in various power system applications such as to determine the most cost-effective way to dispatch generating units' loads, integrate Distributed Generation (DG) units, track the maximum power of photovoltaic systems, and determine optimal power flow solutions, among other uses. The performance indices for the Radial Distribution System (RDS) have not been evaluated using JFSA.Variation of performance indices with respect to unavailability has not been discussed in the literature. Here an attempt is made to analyse the variation of the indices with the unavailability. The proposed algorithm is evaluated first on 3 load point radial distribution system and the computations are performed on the popular IEEE-RBTS Bus2 test system. Based on the output it is observed that for the both the systems the behaviour of the indices with respect to the unavailability is in similar fashion. The equations are derived for the performance indices using the Minitab. With the equations obtained the Performance indices values can be predicted for the given unavailability value. It is the better appropriate method for the assessing the performance indices of Radia Distribution System.

Study the optimization of offshore floating PV power systems and its application

In recent years, the application of solar photovoltaic (PV) has been developing rapidly with a large amount of PV power stations built and operating. However, the land resources for PV power plants are relatively limited. Offshore PV power plants are expected to become an alternative means to solve such problems. In this paper, the PVsyst and AQWA are used to comprehensively simulate the power generation performance of floating PV power stations and thin-film PV power stations under offshore conditions, and the factors affecting the performance of floating PV systems are studied. By establishing the nonlinear regression numerical models, the relationship between the factors is compared by path analysis. The results show that the swing angle has the greatest influence on the system performance. The above work provides certain guidelines for the development and construction of offshore PV systems.