Solar Irradiance Fluctuations Research Articles

The Influence of Solar Irradiance and Temperature on The Pll-Synchronized Inverter Within Grid-Connected PV Systems

This study assesses the influence of solar irradiance and temperature on αβPLL synchronized inverters by investigating their intricate relationship within grid-connected PV systems. The intermittent nature of the DC side of these systems can significantly impact their performance. However, the extent of this impact remains uncertain when employing the stationary reference frame PLL (αβPLL) algorithm for synchronization with the utility grid. Employing simulation in MATLAB 2020a and a Model Based Design approach in the Simscape toolbox of SIMULINK, we conducted this investigation through two test scenarios: one with constant solar irradiance and varying temperature, and the other with varying solar irradiance and constant temperature. Our results indicate that fluctuations in solar irradiance and temperature have minimal effects on the output parameters of grid-connected PV systems when synchronized with the stationary reference frame PLL (αβPLL) algorithm. This conclusion is supported by the total harmonic distortion values, which all fall within the specified limits of IEEE Standard 929- 2000. These findings underscore the potential of αßPLL synchronization as a promising technique, capable of effectively synchronizing grid inverters even in the presence of DC input distortion. This proficiency in grid integration holds promise for enhancing the integration of renewable energy sources into the grid, thereby bolstering power availability for household distribution.

Implementation of an adaptive control approach in a single‐phase grid‐tied solar photovoltaic system for power quality improvement

SummaryThis work focuses on a novel single‐phase complex band‐pass filter‐based frequency‐locked loop (1ϕ‐CBF‐FLL) control for a grid‐tied solar photovoltaic array (SPVA)‐based energy generating system. Among the novel characteristics of this 1ϕ‐CBF‐FLL control algorithm are as follows: (1) It offers a quick convergence rate; (2) it has the ability to mitigate harmonics and DC offset; and (3) it has simplicity in execution. A voltage source converter (VSC), boost converter, 1‐ϕ grid, ripple filter, and nonlinear loads are all part of the system. Feeding 1‐ϕ nonlinear loads and the grid with active power from the solar array is managed by the VSC. Furthermore, grid current quality is enhanced by the use of VSC. Between the solar array and DC‐link capacitor, there is a boost converter that uses the perturb and observe (P&O) technique to track the maximum power from the SPVA. The primary aim of the proposed system is to remove power quality (PQ) issues brought on by nonlinear and imbalanced loads and to supply sinusoidal grid currents during solar irradiance fluctuations. Additionally, a comparison between 1ϕ‐CBF‐FLL control and other control algorithms is presented. The effectiveness of this 1ϕ‐CBF‐FLL control is examined under varying nonlinear loads and in various environmental settings. Test results are used to confirm the simulated results of the proposed system. According to the IEEE‐519 standard, the recommended control may achieve a source current of total harmonic distortion (THD) of 4.02%, which is lower than 5%, a source voltage of THD of 3.21%, and a load current of THD of 24.54%.

Spectral analysis of solar-irradiance fluctuations

Spectral analysis of solar-irradiance fluctuations

Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

Electric vehicles (EVs) are now considered to be a cutting-edge form of transportation. However, the electrical grid infrastructure is not sufficiently developed to meet the rising charging demand for electric vehicles. Therefore, it is impossible to rely entirely on grid-generated electricity. This article discusses a control approach for extremely rapid charging of EV batteries driven by a hybrid DC microgrid, consisting of isolated, Photovoltaic (PV), Wind Turbine, Fuel Cell and Energy Storage Systems. An adaptive sliding mode controller is designed to provide the appropriate power for charging and discharging EVs and energy storage units (ESUs) under a variety of power generation and demand scenarios. The proposed adaptive sliding mode controller operates in a decentralized manner to maintain power distribution amongst the microgrid DC-link EVs and the ESUs. In order to validate the effectiveness of the sliding mode controller in battery charging applications, a comprehensive comparison between fuzzy logic and adaptive sliding mode control is conducted. The proposed controller operates exceptionally well in the presence of unpredictable operating point variations such as rapid changes in EV charging load, fluctuations in solar irradiation, unexpected wind conditions, and so on. Finally, the proposed controller is verified by both simulation and experimental studies.

Hybrid Renewable Energy Microgrids: A Genetic Algorithm Approach to System Design

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.

Shunt Active Parallel Filter, Grid Photovoltaic System

This paper introduces a study focused on managing a photovoltaic system connected to the electrical grid. The primary components of this system include solar arrays linked via a DC bus to an inverter situated on the grid side. Fluctuations in solar irradiance and temperature are swift, prompting the integration of maximum power point tracking (MPPT) within the inverter’s control mechanism. The energy produced by the photovoltaic system is fed into the grid. This transfer is achieved through a proficient DC/AC conversion process, wherein the MPPT is integrated into the inverter’s operation to regulate the levels of active and reactive power injected into the grid. The paper also delves into the employment of the Space Vector Modulation (SVM) control technique for the DC-AC inverter. It covers the implementation of a Shunt Active Power Filter (SAPF) with a three-phase four-wire configuration, consisting of four legs and adopting a split capacitor topology. Furthermore, the paper includes an exploration of the instantaneous power theory and the utilization of hysteresis block control for the SAPF. The findings of this study are demonstrated and analyzed using Matlab/Simulink software.

Smart Design of Wireless Data Transmission Employing Solar-Powered IOT Devices

This study emphasizes the expanding need for solar energy extraction over the past few decades and focuses on the crucial role that solar photovoltaics (PV) play in the renewable energy environment. The surge in demand has driven attention toward enhancing monitoring technologies to maximize their efficiency. The paper introduces a novel wireless platform aimed at augmenting the monitoring of solar PV systems. This innovative approach enables wireless data transmission from a diverse array of sensors and nodes, addressing various challenges, including data volume management, potential signal interference, long-distance data transfer requirements, and data security concerns. The research findings underline the necessity of a minimum 14 Ah battery capacity to accommodate seasonal fluctuations in solar irradiation. A crucial aspect of the study lies in the implementation of an adaptive power system, which significantly alleviates the demand for batteries when the state of charge falls below 30%. This adaptive system not only ensures cost-effective data transmission rates but also offers the potential to maintain system functionality with smaller battery and solar panel configurations. Additionally, it enhances reliability under varying light conditions, mitigating the risk of system failure.

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

Multi-site solar irradiance forecasting based on adaptive spatiotemporal graph convolutional network

Accurate solar irradiance forecasting with fine spatiotemporal correlations is essential for photovoltaic power generation. However, current spatiotemporal methods are unable to extract spatial features appropriately, leading to rough prediction results. Consequently, a deep graph neural network is proposed in this paper for multi-site solar irradiance forecasting. Self-adaptive adjacency matrices were developed to better capture the intrinsic influence between sites under different climate types at various time steps. Based on the adaptive graph convolution and long short-term memory network (AGCLSTM), the spatiotemporal features of each site were extracted to obtain prediction results for all the sites in the research area simultaneously. In the experiments conducted on the datasets of three climate types, the average root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of the proposed model are observed to be in a range of 47.519–54.854 W/m2, 18.968–24.426 W/m2, and 0.9737–0.9889, respectively. Compared to the benchmark models, the average RMSE and MAE reduce by 1.31%–10.86% and 3.75%–18.57%, respectively. Moreover, the proposed method proves to be effective for scenes of solar irradiance fluctuations with the help of self-adaptive adjacency matrices, which enhances its generalization ability and prediction accuracy for one-hour-ahead solar irradiance forecasting.

A novel solar-driven Organic Rankine Cycle system based on the two-stage solar thermal collection and accumulation

Solar thermal power has attracted much attention because it is instrumental to solar energy storage and power grid peak shaving. Among the various solar thermal power technologies, the Organic Rankine Cycle stands out as a prevalent choice for low and intermediate-temperature (80 – 200 °C) solar thermal power generation applications. However, it is required of the concentrating solar collectors for medium temperature supply and its performance is greatly affected by the off-design operation owing to variable solar irradiance. In this paper, a novel solar-driven Organic Rankine Cycle system that consists of a two-stage solar thermal collection and accumulation design is proposed to solve the above issues. Two-stage non-concentrating solar plants are adopted to harvest global solar irradiance and regulate system operation. Two-stage energy accumulators can not only mitigate the influence of the solar irradiance fluctuation on the Organic Rankine Cycle off-design running but also enhance the temperature drop of thermal energy storage. Through the experimental test and numerical simulation, the results indicate that the influence of solar irradiance on the Organic Rankine Cycle steady operation has been weakened (reducing power output fluctuation range by approximately 70%), and the overall system efficiency has also been improved by 43.85%. Consequently, the solar Organic Rankine Cycle system proposed in this paper exhibits superior thermal performance compared to the conventional systems and is conducive to the advancement of the non-concentrating solar thermal power system.

Maloperation prevention for overcurrent protection in photovoltaic integration system under weather intermittency

Non-fault situations in a photovoltaic (PV) integration system, such as solar irradiance fluctuations and intermittency, may be detected as faults by overcurrent-based protection and lead to an unnecessary network outage. Therefore, this paper proposes a maloperation prevention strategy for overcurrent protection based on multiple criteria to accurately distinguish between fault and non-fault states in the PV integration system and prevent the transient overcurrent from causing breaker trips. A fault diagnosis algorithm is proposed for determining the fault and disturbance states by analyzing current changes and their integration over a time delay. The results obtained from both PSCAD/EMTDC simulation and hardware-in-the-loop testing demonstrate the effectiveness of the proposed method in identifying network faults and disturbances, thereby showcasing its ability to provide reliable protection with robustness against weather intermittency. Compared with existing methods, the proposed method demonstrates superior performance in response time, applicability and implementation. These characteristics offer assurances for the dependable and efficient operation of PV integration systems.

Solar irradiance prediction in the tropics using a weather forecasting model

Prediction of photovoltaic (PV) system generations is a powerful tool for managing the electric grids with multiple PV systems for reducing the instability of their electricity supply. For the prediction, solar irradiance, which is directly related to PV generation is forecasting using a weather forecasting model in this study. The target country, Thailand, is in the tropical zone. In the tropics, cumulus and cumulonimbus appear frequently, and their behavior makes weather forecasting difficult. The correlations of the forecasted irradiances with the observations are about 0.8 or more in the intra-day, next-day, and 2-day ahead forecastings. It shows that the validity of solar irradiance forecasting in the tropics for the prediction of PV output. On cloudy days, the tendency of the fluctuation of solar irradiance is reproduced well in the forecasting, but a phase of the fluctuation is shifted in the forecasting.

Characteristics and performance investigation of solar AES system with novel flat-plate collector-evaporator integrated unit capable of salinity wastewater thermal self-storage

Fluctuations and variations in the solar irradiation intensity, both regular and irregular, seriously damage the performance and effectiveness of conventional solar evaporation systems for salinity wastewater recovery. Furthermore, a novel flat-plate collector-evaporator integrated unit with thermal self-storage of salinity wastewater is designed and implemented in the solar air evaporating separation (AES) system. Comprehensive operating characteristics and performance investigations are conducted through the construction of the experimental system and the establishment of the multi-process coupling model. Results of experimental and simulation investigations indicate that this function of wastewater itself as heat-storage medium in the unit actually helps to counteract the reduced system performance and energy efficiency caused by fluctuations in solar irradiation, especially under overcast weathers, when average EE, GOR and ηsys of this system can still be maintained at 3.83 kg/kWh, 2.02, 59.78 %. What's more, this system supports effective 24-hour operation mode under variable environmental conditions, while maintaining superior wastewater recovery performance energy efficiency. Under variable conditions, including night time and cloudy weather, the average GOR, ηsys and ηex,sys,daily for 24-hour operation can be up to 4.49, 62.2 % and 3.95 %, while the average OPEX is only 0.0089$/kg and the OVEX for 15-year service lifetime is only 0.0142 $/kg.

Reduced order 1 + 3D numerical model for evaluating the performance of solar borehole thermal energy storage systems

This study presents a computationally efficient numerical model for solving the fluid flow and heat transfer phenomena in co-axial boreholes heat exchangers arranged on a rectangular grid (N × N) drill hole pattern. Such systems of boreholes are an integral component of a Solar-Borehole Thermal Energy Storage System for seasonal thermal storage application. The numerical model couples a one-dimensional fluid flow and convective heat transfer model in the heat exchanger with a three-dimensional model of conductive heat transfer in the strata. In addition to solving mass and heat transfer in borehole systems, it integrates a solar thermal collector system and building dynamic thermal load to design a Solar- Borehole Thermal Energy Storage system for a 182-unit residential apartment building. Simulation also considers and evaluates the conductive, convective, and radiative thermal losses of the system. The model is validated using experimental data from a coaxial borehole heat exchanger and compared to the results of a commercial finite volume solver for multiple co-axial boreholes. The computational cost of developed numerical model is reduced by a factor of 194 compared to a commercial finite volume solver. Solar- Borehole Thermal Energy Storage system simulation is performed for a period of five years considering hourly fluctuations in solar irradiance and building dynamic thermal energy demand. Parametric study is performed on depth and spacing of boreholes, and mass flowrate in the solar thermal collectors. It is found that a system of 100 boreholes, each 100 m deep, linked to 674 solar thermal collectors can provide the heating for the building with a total thermal demand of 5600 GJ per year.

Prediction Interval Estimation and Deterministic Forecasting Model Using Ground-Based Sky Image

Due to the explosive penetration of photovoltaic (PV) systems in the power grid, accurate and well-informed solar PV power forecasting has become exceptionally crucial, while accurate irradiance prediction is the basis of PV power forecasting. Yet most research on PV power/irradiance forecasting focus on deterministic forecasting, whereas it is difficult to express the credibility of the forecasting results, especially in the scene of instantaneous fluctuation of irradiance and photovoltaic power caused by cloud movement. Therefore, in order to accurately estimate and track the rapid fluctuations of solar irradiance, this paper takes the ground-based sky image information into consideration and obtains the probability-level prediction results along with the deterministic point forecasting, proposes a comprehensive evaluation function constructed with the average coverage and the average width as the fitness function to assist in the optimization of the deterministic forecasting results, and then constructs a three-output model, naming it DenLUBE. In addition, a framework for irradiance variation pattern prediction is proposed to provide pattern information for the forecasting time period to further improve the performance of the ultra-short-term forecasting model. The results show that the proposed model can achieve high quality and strongly robust deterministic forecasting results while producing tight prediction intervals independent of weather conditions. In addition, the performance of our model is greatly enhanced compared to the traditional approach of taking the middle position of the interval as the deterministic forecasting result, as well as other benchmark models.

Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

AbstractClouds cast shadows on the surface and locally enhance solar irradiance by absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. Typical spatiotemporal scales and driving mechanisms of this intra‐day irradiance variability are not well known, hence even 1 day ahead forecasts of variability are inaccurate. Here, we use long‐term, high‐frequency solar irradiance observations combined with satellite imagery, numerical simulations, and conceptual modeling to show how irradiance variability is linked to the cloud size distribution. Cloud shadow sizes are distributed according to a power law over multiple orders of magnitude, deviating only from the cloud size distribution due to cloud edge transparency at scales below ≈750 m. Locally cloud‐enhanced irradiance occurs as frequently as shadows, and is similarly driven mostly by boundary layer clouds, but distributed over a smaller range of scales. We reconcile studies of solar irradiance variability with those on clouds, which brings fundamental understanding to what drives irradiance variability. Our findings have implications not only for weather and climate modeling, but also for solar energy and photosynthesis by vegetation, where detailed knowledge of surface solar irradiance is essential.

The optimized power flow control system for the photovoltaic DC microgrid

The power flow control systems play a significant role in DC microgrids with photovoltaic inputs to supply the load with continuous power. The output power of the photovoltaic modules could experience a decline due to fluctuations in solar irradiation and temperature, which necessitates the use of batteries and the utility grid to reduce the negative effects of undesirable variations. However, an efficient control strategy is necessary to ensure an uninterrupted energy supply to the load units. This paper proposes an improved control of energy flow based on a State-of-Charge battery power estimation technique using the Coulomb counting method. By accurately estimating the available power from the batteries using the State-of-Charge technique, the microgrid is able to determine to assess if it requires to switch to the grid when the power output from photovoltaic modules is insufficient to meet the load demand. The proposed method also eliminates the need for DC bus voltage level-based approaches to charge or discharge the batteries with the advantages of the significant reduction in DC bus voltage variations. The simulation results of the proposed approach show that it provides satisfactory control performance to meet the load demand.

Transient Stability and Active Protection of Power Systems With Grid-Forming PV Power Plants

Photovoltaic (PV) power plants with grid-forming technology must withstand severe disturbances and remain operational. To address this challenge, this paper sets forth a grid-forming strategy for PV solar power plants so that they can ride through power system faults. This capability is accomplished by leveraging two-axis proportional-integral regulators with anti-windup functionality. This paper also demonstrates that fluctuations of solar irradiance can cause significant dc-link voltage variations and loss of synchronism of grid-forming PV plants. Hence, we develop an active dc-link protection method which depends on estimation of solar irradiance. The contributions of this paper are demonstrated via positive-sequence simulations of modified versions of the WSCC 9- and IEEE 39-bus grids.

Statistical analysis of cloud layers and solar irradiations for all seasons in Toyohashi city, Japan

Several solar irradiation forecasting methods are being developed toward economically efficient solar power generation. Among these, the method that utilizes all sky imagers to estimate the direction of cloud movement above certain observation points is able to forecast fluctuations in solar irradiation with localized temporal resolution (∼30 min) through observation of shadows cast by clouds. The one-layered cloud is usually assumed in forecasting the direction of cloud movement. However, in reality, multilayered clouds may move in different directions in the sky. While such multilayered clouds may have effect on forecast accuracy, the extent to which multilayered clouds affect the solar irradiation and the generated solar power has never been reported. Therefore, the purposes of this study are (1) to give the dataset to know the effect of multilayered clouds and (2) to decide whether the multilayered clouds have to be considered in forecasting of solar irradiation. We analyzed the number of cloud layers and their effect on the solar irradiation, the generated solar power, and the clearness index through statistical analysis for all seasons in Toyohashi city, Japan. The dataset used to analysis includes data with 4381 h. The analytical results show that multilayered clouds were observed for ∼40% of the daytime duration even in winter. In addition, the results show that multilayered clouds were observed even when the solar irradiation, the generated solar power, and the clearness index were high, which suggests that the effect of multilayered clouds must be considered. In addition, it will be shown that the maximum number of cloud layers to be considered is 3 from statistical analysis.

Charging station control strategy considering dynamic behaviour of electric vehicles with variable state of charge regulation for energy management of autonomous micro-grid

The electric vehicles (EVs) connected to the charging stations (CSs), as a part of autonomous micro-grid (MG), introduce additional fluctuations due to their dynamic behaviour owing to variable state of charge (SOC) and the type of EV. This paper puts forth the charging station control strategy (CSCS) for proper energy management (EM) and voltage regulation (VR) in an autonomous MG subjected to dynamic behaviour of EVs, fluctuations of solar irradiance, wind speed, and load. The EVs parked in the CSs either charge or discharge through two-way power exchange between the autonomous MG system and the CSs, depending on the generation and load demand distinctions. As a result, EVs that enter and depart the CSs may have varying battery voltage ratings and upper SOC limits that differ. The simulations are executed using the MATLAB® software, and the efficiency of the proposed CSCS is assessed in two different modes: excess power mode (EPM) and deficient power mode (DPM). For a realistic proposal analysis, the results are also checked using a hardware-in the-loop (HIL) real-time OPAL-RT simulator (OP5700). By comparing and contrasting the results obtained with MATLAB® and the OPAL-RT simulator, the proposed control strategy is validated.