Solar Irradiance Variations Research Articles

Maximum Demand Reduction (MDRed) modelling using MATLAB Stateflow®

Based on renewable energy case studies, the MDRed (Maximum Demand Reduction) Model is created as an optimization apparatus for the solar PV-battery system. In this paper, MATLAB modeling toolbox is used for simulation of the solar PV-BESS design and verification of the MDRed modeling via variations in MD limit and solar PV load. Based on MDRed model, the maximum demand supervisory control algorithm is developed via MATLAB Stateflow® for the battery management system (BMS) to monitor and regulate the load to maintain the net load consumption below MD limit with the solar PV system. In summary, the simulation with the master MD controller shows the effectiveness of the proposed controller on the BMS to cater to MD reductions during solar irradiance and load pattern variations.

Control a Photovoltaic/Wind Turbine/Diesel generator with storage battery

Wind/Photovoltaic energy systems are among the most used configurations in multi-sources energy systems because their reliability, robustness and power sources complementarily. For heavy loads, it is more convenient to add backup systems (diesel generator) for an enhanced power availability. In order to protect power sources and increase their lifespan, several power management strategies are such as fuzzy logic control, artificial neural networks and metaheuristic algorithms are proposed and used in literature. This paper presents a power supervisory control (PMC) based on fuzzy logic control dedicated for photovoltaic/wind turbine/ diesel generator/battery systems. The application of the aforementioned technique makes it possible to predict how the hybrid system various operating processes would change in response to solar irradiation and wind speed variations. The study was implemented under Matlab/Simulink. To obtain more realistic results, measured weather values have been incorporated in the simulation. The application is made in a coastal town which has a good potential values of solar irradiance and wind speeds. The obtained results show that the load have been supplied while the batteries were protected due to the PMC.

Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach

Cloud dynamics are the main factor influencing the intermittent variability of short-term solar irradiance, and therefore affect the solar farm output. Sky images have been widely used for short-term solar irradiance prediction with encouraging results due to the spatial information they contain. At present, there is little discussion on the most promising deep learning methods to integrate images with quantitative measures of solar irradiation. To address this gap, we optimise the current mainstream framework using gate architecture and propose a new transformer-based framework in an attempt to achieve better prediction results. It was found that compared to the classical CNN model based on late feature-level fusion, the transformer framework model based on early feature-level prediction improves the balanced accuracy of ramp events by 9.43% and 3.91% on the 2-min and 6-min scales, respectively. However, based on the results, it can be concluded that for the single picture-digital bimodal model, the spatial information validity of a single picture is difficult to achieve beyond 10 min. This work has the potential to contribute to the interpretability and iterability of deep learning models based on sky images.

Analisis Pengaruh Suhu Dan Partial Shading Terhadap Output Daya PV 100-WP Untuk Sistem Hidroponik

This experiment discusses the effect of the variability of solar radiation due to partial shading and surface temperature of the PV Panel on the output power of 100 Wp solar panels. This study begins by determining the load of the system, i.e. DC pump in the nutrient circulation system of the DFT hydroponic system. From there, we can observe the change in solar panel power output and PV surface temperature change due to partial shade. This survey method actually measures the temperature and intensity of solar radiation and measures the power output of a solar panel, and the tools used are thermal sensors used to measure the surface temperature of solar panels. Solar power meter. to measure the solar irradiance and use the multimeter to measure the voltage and current, 10A capacity SCC battery, 100Wp capacity solar panel and 65Ah battery. The test will be conducted for 5 days, starting from 09:00 to 15:00 every day. This result shows that temperature increase affects the output power decrease when the solar panel is in normal condition (not half shaded condition). On the other hand, when partial shade conditions increase, the temperature does not affect the decrease in solar panel output power. The greater the intensity of partial shade produced by the solar panel, the lower the output power. However, for short periods of partial shade, the output power produced tends to be more stable than under normal conditions.

Modelling and numerical simulations of eco-friendly double absorber solar cell “Spiro-OmeTAD/CIGS/MASnI3/CdS/ZnO” and its PV-module

A broad study for design of higher efficiency modules based on low-cost, non-toxic Au/Spiro-OmeTAD/CIGS/MASnI3/CdS/ZnO/FTO tandem configuration solar cells has been conducted using solar cell capacitance simulator package “SCAPS-1D”. For the cause of highly efficient device performance, the selection of suitable layer materials such as “ZnO” as electron transport layer “ETL”, “CIGS” and “MASnI3→CH3NH3SnI3” as absorber, “Spiro-OmeTAD” as hole transport layer “HTL”, and CdS as active buffer layer material have been developed. The structural layout of the recommended composition was enhanced by variation in thickness of the specified layers, different back contact layers and temperature. The power conversion efficiency “PCE” was found to be 36.38%, along with other electrical parameters such as short-circuit current density “Jsc” of 48.28 mA/cm2, open-circuit voltage “Voc” of 1.38 V, and fill factor of 54.24% for optimized layer thickness of 0.084 μm for “HTL”, 0.051 μm for ETL”, 0.028 μm for buffer layer, 2.10 μm and 1.51 μm for CIGS and CH3NH3SnI3 absorber layers respectively. The overall device performance in perspective of charge carrier generation and recombination was investigated in depth. The improvement of diffusion length has increased the charge carrier's generation around 3.69 μm and the recombination rate was found at a lower value of 2.2 μm which overall enhance the device performance. At standard conditions, the formation of supplementary electron-hole pairs by varying the absorber layers thickness shows the maximum extent for Jt, FF% and PCE% during optimized investigation. A module of dimensions (2.2 × 1.1) m consisting of 72 cells in series was configured in PVsyst software to get the panel performance. Input parameters obtained through solar capacitance simulator were incorporated to evaluate module performance and output power by variation in solar irradiation and temperature. Yearly performance ratio for home location has been explored and reported.

Adaptive Coordination of Relays in AC Microgrid Considering Operational and Topological Changes

Conventional protection schemes are vulnerable to varying changes in a microgrid with different types of distributed generators (DGs), which alter the fault current levels, leading to miscoordination of directional over-current relays (DOCRs). Due to limited storage in numeric relays, having one setting for each network scenario in the relays is infeasible. Also, schemes with one setting considering grid-connected mode (GCM) and islanded mode (IM) of operation with multiple scenarios may be infeasible for a microgrid with inverter-interfaced DGs due to low short-circuit level in IM. Thus, this article proposes an adaptive trip characteristics-based relay coordination scheme for a microgrid with different DGs, where two trip characteristics are assigned to each DOCR, one each for GCM and IM. Also, a novel framework for selecting the suitable trip characteristic is proposed. Hardware-in-loop simulations using Numerical Relay Development Environment for a meshed CIGRE microgrid, modified IEEE 34-bus distribution system, and comparison with previous works validate the proposed method's performance for various scenarios, such as the outage of a DG or line, variations in solar irradiation and wind speed, and network reconfiguration for different fault types during GCM and IM.

Surface Solar Extremes in the Most Irradiated Region on Earth, Altiplano

Abstract Satellites have consistently pointed to the Altiplano of the Atacama Desert as the place on Earth where the world’s highest surface irradiance occurs. This region, near the Tropic of Capricorn, is characterized by its high elevation, prevalent cloudless conditions, and relatively low concentrations of ozone, aerosols, and precipitable water. Aimed at studying the variability of the surface solar irradiance and detecting atmospheric composition changes in the Altiplano, an atmospheric observatory was set up in 2016 at the northwestern border of the Chajnantor Plateau (5,148 m MSL, 22.95°S, 67.78°W, Chile). Here, we report on the first 5 years of measurements at this observatory that establish the Altiplano as the region that receives the highest-known irradiation on Earth and illuminate the unique features of surface solar extremes at high-altitude locations. We found that the global horizontal shortwave (SW) irradiance on the plateau is on average 308 W m−2 (equivalent to an annual irradiation of 2.7 MWh m−2 yr−1, the highest worldwide). We also found that forward scattering by broken clouds often leads to intense bursts of SW irradiance; a record of 2,177 W m−2 was measured, equivalent to the extraterrestrial SW irradiance expected at approximately 0.79 astronomical units (AU) from the Sun. These cloud-driven surface solar extremes occur on the Chajnantor Plateau at a frequency, intensity, and duration not previously seen anywhere in the world, making the site an ideal location for studying the response of photovoltaic (PV) power plants to periods of enhanced SW variability.

Symbiotic Zooxanthellae Drive the δ13C Changes of Tridacna gigas Shell in the Southern South China Sea

AbstractThe carbon isotope (δ13C) in marine biogenic carbonate is a valuable tool for comprehending the marine carbon cycle. However, the absence of monthly resolved δ13C records before the instrumental era (since 1850) hinders us to fully understand the global carbon cycle. In this study, we present a δ13C record from a modern Tridacna gigas shell (δ13Cshell) collected from the southern South China Sea (SCS) with an average resolution of ∼10 days. The δ13C record illustrates seasonal and interannual cycles which are superimposed by long‐term decreasing trend. We investigate the environmental controls influencing the δ13Cshell on different time scales. We suggest that more positive δ13Cshell values observed during the juvenile stage (first 8–10 years), which correspond to a high growth rate, can be attributed to 13C enrichment of the internal dissolved inorganic carbon pool. This resulted from the exponential increase of symbiotic zooxanthellae, and preferential 12C sequestration by the accelerated buildup of productive tissues. On the interannual time scale, changes in δ13Cshell were likely affected by both internal (vital effect) and external processes (sea water δ13C), as well as being disturbed by the super El Niño‐La Niña event. Seasonal changes of δ13Cshell were mainly dominated by the intensity of photosynthesis and respiration of symbiotic zooxanthellae, which depend on the seasonal solar irradiance variations in association with precipitation and the South Asian summer monsoon. Our study sheds light on the controlling factors of δ13Cshell variations on seasonal timescale and the long‐term trend, providing insight into the carbon cycle over geological history.

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics

Abstract. Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7462362 (Mol et al., 2022); the reader is referred to the “Code and data availability” section of this paper for the complete list.

The optimal configuration of distributed generators for CCHP micro-grid based on double-layer operation strategy and improved NSGA-III algorithm

The micro-grid with combined cooling, heating, and power supply system (CCHP) is a complex integration that assembles various distributed generators (DGs). The motivation of this paper is to select a reasonable configuration of DGs for a CCHP-MG by means of uncertainties models, improved operation strategy and algorithm. The DGs in a CCHP-MG are composed of wind turbine (WT), photovoltaic (PV), battery energy storage (BES) system, biomass gasification (BG), Stirling engine, heat exchanger, absorption chiller, electric heater and cooler. In terms of uncertainties models, the uncertainties of variations in wind speed, solar irradiance, temperature, and load demand are considered with 8760 h of a whole year. The paper proposes a multi-objective optimization model for the CCHP-MG system under three objectives associated with the minimized system cost, greenhouse gas (GHG) emissions and power waste. In this study, the double-layer operation strategy is raised for replacing the conventional objective of loss of energy supply probability (LESP) in the CCHP-MG system. Based on the non-dominated sorting genetic III (NSGA-III) algorithm, methods of 1/8 unit sphere and angular distance are proposed for an improved NSGA-III algorithm (INSGA-III) to increase computational efficiency and avoid local convergence. Finally, the multiple analysis and comparisons demonstrate that the cooperation of the double-layer operation strategy and INSGA-III has better performance with superiorities of economy and reliability. The results in the case study show that the system cost, GHG emission, and energy waste of the selected CCHP-MG system respectively reached 85.23, 72446.7, and 7690.46. The study confirms that the improved operation strategy, INSGA-III algorithm, and multiple analysis framework can be extended to design CCHP-MG system in other places.

Computer Programming to Estimate the Global Daily and Hourly solar Radiation of any location around the Globe

Solar data collection and radiation analysis are basic to study several solar energy-related types of research. But, most researchers get difficulty while collecting such valuable data. In most developing countries and rural areas of the world, it is a bottleneck situation to get the ground-level solar radiation data. The intended purpose of this research is to develop a code that can estimate the daily and hourly variation of global solar radiation for any location around the globe. A python script is developed to make users flexible to use for any climatic region of the world. As an illustration, the developed code is implemented for three regions namely, Algeria, Pakistan, and Nigeria that researchers developed a correlation for each climatic region. The output shows that it is possible to use a computer program for any climatic region that researchers or any users want to find the estimated global daily and hourly solar radiation data.

Neural-network modeling of solar radiation and temperature variability due to climate change in Ibadan Metropolis

his research focused on studying the variability of solar radiation and temperature under climate change in the Ibadan metropolis. In the study, spatial distribution, temporal variations, annual distribution, estimation and prediction of the solar radiation, minimum and maximum temperature data in the Ibadan Metropolis was collected. A Long Short-Term Memory Neural Network (LSTM-NN) model was developed for the prediction using the time-series data obtained. An ARIMA model was further developed to compare and validate the LSTM-NN model. The performance of the prediction models were determined using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The RMSE values for the minimum, maximum temperature and solar radiation predictions were 1.543, 1.290, 1.967, and 1.611, 1.309, 2.106 for the LSTM-NN and ARIMA models respectively, while the MAPE values for the minimum, maximum temperature and solar radiation predictions were 3.603, 4.351, 8.859, and 3.840, 4.480, 9.502 for the LSTM-NN and ARIMA models respectively. The LSTM-NN model had a better prediction performance in all categories with lower error when compared with ARIMA. From the prediction, it was observed that there will be a reduction in the maximum temperature, minimum temperature and solar radiation values when compared to obtained data. The observed minimum temperature ranged from 22.9032-23.2032(0C), while the predicted minimum temperature ranged from 19.9260- 19.977(0C) also the observed maximum temperature ranged from 32.87096-33.7064(0C), while the predicted maximum temperature ranged from 29.5159-29.5529(0C), the observed solar radiation ranged from 19.203-19.722 (W/m2 ), while the predicted solar radiation ranged from 14.123-14.115 (W/m2 ). The year with the highest solar radiation which constitutes the useful energy is 2024 with an average value of 14.1395 W/m2

The relationship between interannual variability of winter surface solar radiation over the Southeast Asian low-latitude highlands and the circumglobal teleconnection

Using ERA5 reanalysis data, this study reveals the relationship between variations of winter surface solar radiation (SSR) over the Southeast Asian low-latitude highlands (SEALLH) and the circumglobal teleconnection (CGT). The results indicate that the winter SSR shows significant interannual variability with a monopole pattern over the SEALLH. The winter CGT significantly modifies the interannual variability of the simultaneous SSR over the SEALLH. When the CGT is in its positive phase, a wave train appears over the central-eastern Mediterranean, Arabian Peninsula, Indian Peninsula, and East Asia with anomalous “northerly, southerly, northerly, southerly” nondivergent meridional wind. The associated southerly wind anomalies around the SEALLH favor greater transport of water vapor from the Bay of Bengal that converges over the SEALLH. The consequent increase of cloud cover in the lower–middle troposphere and aerosol optical depth (AOD) reduce the winter SSR over the SEALLH. When the CGT is in its negative phase, the anomalous northerly wind with anomalous divergence increases the winter SSR over the SEALLH by decreasing both cloud cover in the lower–middle troposphere and the AOD.

A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting

The development of solar energy is crucial to combat the global climate change and fossil energy crisis. However, the inherent uncertainty of solar power prevents its large-scale integration into power grids. Although various sky-image-derived modeling methods exist to forecast the variations of solar irradiance, few focus on fully utilizing the coupling correlations between sky images and historical data to improve the forecasting performance. Therefore, a novel multimodal-learning framework is proposed for forecasting global horizontal irradiance (GHI) in the ultra-short-term. First, the historical and empirically estimated clear-sky GHI are encoded by Informer. Then, the ground-based sky images are transformed into optical flow maps, which can be handled by Vision Transformer. Subsequently, a cross-modality attention method is proposed to explore the coupling correlations between the two modalities. Last, a generative decoder is used to implement multi-step forecasting. The experimental results show that the proposed method achieves a normalized root mean square error (NRMSE) of 4.28% in 10-min-ahead forecasting. Several state-of-the-art methods are also used for comparisons. The experimental results show that the proposed method outperforms the benchmark methods and exhibits higher accuracy and robustness in ultra-short-term GHI forecasting.

Graphical decomposition model to estimate hourly global solar radiation considering weather stochasticity

Hourly solar radiation data are of great concern for solar PV, solar thermal and building energy assessment. Unfortunately, due to economic as well as technical limitations, most studies of solar radiation as well as actual measurements are daily values. Hence, daily solar radiation decomposition model is quite significant. Furthermore, graphical representation is a simple and direct way to analyze and interpret meteorological data. Consequently, a graphical solar radiation decomposition model based on the solar radiation daily pattern is proposed. It is achieved by clustering, regression, classification and identification of meteorological data (including solar radiation). The developed model is nested into the prediction system and allows reliable prediction of hourly global solar radiation. The proposed model improves 8.3% in accuracy compared to the commonly used model with an R2 of 0.91. Meanwhile, in existing studies, solar radiation daily patterns are mostly discriminated by clearness index. This is an average state but cannot describe the temporal variation of solar radiation. In our work, it is found that weather, cloud cover, and sunshine duration have a good temporal consistency on the solar radiation daily patterns. The biased distribution of solar radiation can be explained by the time series of these characterizing factors. Based on these new advances, sixteen different types of solar radiation have been identified, and the frequency of occurrence of each type has been established and presented.

Reference evapotranspiration estimate with missing climatic data and multiple linear regression models.

The reference evapotranspiration (ETo) is considered one of the primary variables for water resource management, irrigation practices, agricultural and hydro-meteorological studies, and modeling different hydrological processes. Therefore, an accurate prediction of ETo is essential. A large number of empirical methods have been developed by numerous scientists and specialists worldwide to estimate ETo from different climatic variables. The FAO56 Penman-Monteith (PM) is the most accepted and accurate model to estimate ETo in various environments and climatic conditions. However, the FAO56-PM method requires radiation, air temperature, air humidity, and wind speed data. In this study in Adana Plain, which has a Mediterranean climate for the summer growing season, using 22-year daily climatic data, the performance of the FAO56-PM method was evaluated with different combinations of climatic variables when climatic data were missing. Additionally, the performances of Hargreaves-Samani (HS) and HS (A&G) equations were assessed, and multiple linear regression models (MLR) were developed using different combinations of climatic variables. The FAO56-PM method could accurately estimate daily ETo when wind speed (U) and relative humidity (RH) data were unavailable, using the procedures suggested by FAO56 Paper (RMSEs were smaller than 0.4 mm d-1, and percent relative errors (REs) were smaller than 9%). Hargreaves-Samani (A&G) and HS equations could not estimate daily ETo accurately according to the statistical indices (RMSEs = 0.772-0.957 mm d-1; REs (%) = 18.2-22.6; R2 = 0.604-0.686, respectively). On the other hand, MLR models' performance varied according to a combination of different climatic variables. According to t-stat and p values of independent variables for MLR models, solar radiation (Rs) and sunshine hours (n) variables had more effect on estimating ETo than other variables. Therefore, the models that used Rs and n data estimated daily ETo more accurately than the others. RMSE values of the models that used Rs were between 0.288 to 0.529 mm d-1; RE(%) values were between 6.2%-11.5% in the validation process. RMSE values of the models that used n were between 0.457 to 0.750 mm d-1; RE(%) values were between 9.9%-16.3% in the validation process. The models based only on air temperature had the worst performance (RMSE = 1.117 mm d-1; RE(%) = 24.2; R2 = 0.423).

Optimization of an isolated photovoltaic water pumping system with technical–economic criteria in a water users association

AbstractWith proper management, the modernization of irrigation systems makes it possible to improve the efficiency of application and use of water at the cost of an increase in pumping needs and, therefore, an increment of the energy consumed. The recent drastic price increase for energy put the viability of many farms at risk. In this context, using photovoltaic solar energy to power pumping stations has become an increasingly attractive alternative and a cheap and reliable option. The dimensioning of pumping systems powered by photovoltaic solar energy must be done considering the variability of solar radiation to take advantage of the available photovoltaic energy, especially during periods of less irradiation. By investigating a particular case, this paper studies the effect of increasing the number of pumps in parallel while maintaining the total power, as well as the relationship between the installed photovoltaic capacity and the power of the pumping system, to meet pumping requirements throughout the year. The pumped volume increased as the number of pumps installed in parallel increased for the same photovoltaic power generator. Although this increment has a limit, beyond which no greater significant rise in volume is achieved, installation costs increase. In addition, for the same pumping power installed, the required photovoltaic generator power decreases as the number of pumps in parallel increases. In the case studied, a 27% increase in the annual pumped volume was achieved by incrementing the number of pumps in parallel from one to five, thus leading to a 44.1% reduction in the size of the photovoltaic generator and a 13.3% reduction in the cost of installation compared with a system with only one pump. The procedure used to determine the most appropriate number of pumps to install in parallel when pumping water between two tanks, which minimizes the photovoltaic generator's size while guaranteeing pumping requirements, is easily generalizable for sizing isolated photovoltaic water pumping systems.

Thermal energy storage applications in solar water heaters: An updated review

Fossil fuel usage for heating applications must be reduced considering the issues related to the environment and the restriction of their resources. In this regard, attention is devoted to renewable energy sources to supply the energy requirements of different sectors. In the building sector, solar energy is harnessed for heating and cooling. Solar energy is applicable both directly and indirectly for heating using different technologies. The intermittent nature of solar energy obliges the use of storage units to make the solar systems applicable at night hours or during periods the low solar intensity. Various thermal energy storage materials have been utilized in different kinds of solar heaters to stabilize their performance, improve their reliability, and avoid issues related to variations in solar radiation. In this article, studies on the usage of thermal energy storage units in solar water heaters are reviewed and their key results are reflected. As one of the main conclusions of the reviewed works, it can be denoted that several factors such as the operation condition and characteristics of the storage unit are effective on the function of the systems combined with the thermal storage component. Aside from an increment in the operating hours of solar heaters, usage of storage units can boost both energy and exergy efficiencies. Furthermore, the study denotes that the power saving rate is influenced by the abundance of solar energy resources. In addition, it could be denoted that the performance of the systems is improvable by employing some ideas, such as the application of nanotechnology in storage materials.