Design Of Solar Energy Systems Research Articles

Analisis dan Desain Floating PV System di Beberapa Waduk Batam untuk Optimalisasi Bauran Energi Terbarukan

The climate crisis caused by the use of fossil fuels is prompting countries around the world to shift to renewable energy. Indonesia has substantial solar energy potential, but this must be balanced with land conservation efforts. PT PLN Batam, a subsidiary of PT PLN (Persero), shares the responsibility of meeting the national renewable energy mix target of 23% by 2025. Adding new power plants requires a large area, which is a significant challenge in the development of both fossil and renewable energy plants in Batam. The floating PV system is an alternative solution to this problem. This study aims to design a floating solar power generation system at Tembesi and Mukakuning Reservoirs, focusing on solar energy potential, energy production, and system design. The results indicate that solar irradiation at both locations reaches 1,650 kWh/m² with an average horizontal PSH of 4.52 hours/day and net PSH Modules of 4.32 hours/day. Energy production in Year-1 is 55.83 GWh with a performance ratio of 81.65%, resulting in annual energy production of 1,345 kWh/kWp. The floating PV system design at Tembesi and Mukakuning Reservoirs consists of 10 islands, each with 19 array boxes and 20 strings per array box, comprising 28 PV modules per string. A total of 106,400 PV modules with a capacity of 390 Wp each is utilized. The system employs 3,125 kVA PV inverters, 7,500 kVA MV transformers, and 35 MVA HV transformers to ensure high efficiency and reliability. The system design also considers environmental durability, including resistance to corrosion and water level fluctuations. Recommendations include optimizing the design based on seasonal variations and implementing effective monitoring and maintenance systems.

Shading impact on the electricity generated by a photovoltaic installation using “Solar Shadow-Mask”

Abstract Solar energy is an excellent source of renewable power, but designing photovoltaic (PV) systems can be challenging without proper knowledge of solar radiation. The amount of energy received at the installation site plays a crucial role in determining the number of panels required to meet the electrical demand. For a given electrical demand, higher levels of received energy imply a reduced number of panels required, and vice versa. Hence, having knowledge of this irradiance is of paramount importance in the design and sizing of solar energy systems. The primary objective of this article is to provide an accurate estimation of electricity production in a PV installation when it is affected by shading. To achieve this, we performed calculations of the irradiation (direct and diffuse) received by our installation using the “Hottel” method, which integrates relevant local site parameters for our study. Subsequently, we study the impact of shading by conducting shade mask measurements on our installation. This enables us to obtain an accurate estimation of the irradiance received by the PV panels. The measurements include surveys of the geometry of obstacles and shade measurements taken at various times of the day. Additionally, a practical study of shading effects will be conducted using “close shading masks.” This method was applied to a 1.44 kWp PV installation located at the Faculty of Science and Technology of Settat (Morocco), where the output energy of PV panels was calculated. Finally, the effect of shading on the PV installation was quantified.

Model-prediction of Efficiency of a Parabolic Trough Collector Using Data-Driven Soft Computing

The study highlights the need to use suitable modeling techniques to accurately predict the efficiency of parabolic trough collectors in light of their significance in renewable energy. An examination of prediction models using the Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT) algorithms for the efficiency of parabolic trough collectors provides insightful information about how well they work. In terms of prediction accuracy and precision, the Decision Tree model regularly performs better than its rivals throughout the training and testing phases. What sets it apart from SVR and LR models is its ability to identify minute relationships within the data. SVR performs better than LR, although it is not as exact or accurate as the DT model. Among the three models, Linear Regression has the lowest performance, underscoring its limitations in terms of capturing non-linear relationships. Given its exceptional performance, the Decision Tree model may prove to be a crucial instrument in encouraging the design and construction of solar energy systems, hence advancing the growth of sustainable development projects and renewable energy technology.

Comparative analysis of single and hybrid machine learning models for daily solar radiation

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually and in combination with the wavelet transform (WT). The assessment of these models is based on meteorological data spanning three decades (1981–2010) from the province of Kütahya in Türkiye. To address the inherent uncertainty in these data-driven models, the quantile regression method is employed for uncertainty analysis. Statistical metrics, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), mean prediction interval (MPI), and prediction interval coverage probability (PICP), are utilized to evaluate the effectiveness and uncertainties of the models. The SVR and KNN models exhibit comparable performances concerning both predictive accuracy and uncertainty levels. However, hybrid models, such as KNN-WT, RF-WT, and SVR-WT, display enhanced accuracy compared to individual ML models, as indicated by statistical performance criteria. Notably, the SVR-WT model, incorporating inputs such as sunshine duration, air temperature, wind speed, and relative humidity, outperforms other models in terms of RMSE (2.174 MJ/m2), MAE (1.721 MJ/m2), R2 (0.923), MPI (28.55), and PICP (0.80) for the testing dataset. In conclusion, the integration of WT significantly improves the performance of ML models, providing valuable insights for the design and operation of solar energy systems, where precise daily SR estimation is critical for optimal and cost-efficient operation.

Comparing Energy Harvesting Efficiency between Solar Panels: Tracking the Sun vs Ground-Mounted 40 WP Panels

This experiment aimed to evaluate the energy efficiency of a solar tracking panel compared to a ground-fixed solar panel. A 40 WP solar panel with a time-tracking mechanism was used to monitor the sun's movement from morning to evening, while a static 40 WP ground-fixed solar panel was also studied for comparison.The study also included the energy consumption of a 12 V DC motor responsible for orienting the tracking panel to face the sun. Throughout the day, the tracking panel's position was continuously adjusted to align with the sun's position, facilitated by a microcontroller and a gyroscope. The energy consumed by the motor during the tracking process was accounted for. The net energy output of the tracking panel was computed by subtracting the energy expended in tracking from the total energy collected. Following the experimentation, the collected data was analyzed to determine the energy efficiency of the tracking panel in contrast to the ground-fixed panel. The findings revealed that the tracking panel exhibited a 15 percent increase in energy efficiency compared to the ground-fixed panel. These results emphasize the potential benefits of solar tracking technology in optimizing energy capture from solar panels. The implications of these findings are valuable for advancing the design and implementation of solar energy systems, offering increased efficiency and sustainability.

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions.
 The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

Enhanced Solar Tracking System Using Internet of Things Technology

Solar energy has grown in popularity in recent years. Conventional solar panels cannot provide enough energy to meet consumer demand. Therefore, more solar panels are needed to meet consumer demand. The Internet of Things (IoT) Solar Tracker maximises solar energy collecting by connecting the physical and digital worlds. This study integrates IoT to improve solar tracker performance. Blynk uses the NodeMCU ESP8266 control unit to monitor voltage, current, and power usage. This research uses the Arduino IDE, a photoresistor sensor, and a current sensor, INA219. This work advances solar energy efficiency. By comparing solar panel electrical qualities in different environments, this research succeeded. To assess solar panel efficiency, 5 V and 12 V fans were used as loads. The results demonstrated that load configuration deliberately influences power and energy production. Solar panels produced greater power and energy under 12 V fan loads than 5 V loads. This observation assists solar energy system analysis and design. Second, the IoT solar tracker was tested for electricity harvesting. The solar tracker harvested more solar energy than a fixed orientation. After tracking the sun, it enhances voltage, current, power, and energy by 30%. Web dashboards and smartphone apps allow real-time IoT device monitoring and control. The interactive web dashboard displayed solar panel data. Track electrical properties and sunshine intensity. IoT devices influence solar tracker decisions and promote sustainable energy.Keywords: IoT solar tracker, Blynk application, NodeMCU ESP8266

Exploring Global Solar Radiation: Enhancing Ground-Level Solar Radiation Prediction using Hottel's Semi-Empirical Model and Sunshine Duration Analysis

The effective utilization of solar energy at a specific geographical locale is contingent upon the acquisition and assimilation of comprehensive and meticulous solar radiation data pertinent to that specific site. A profound understanding of these datasets constitutes a pivotal factor in the precision-driven design and dimensioning of solar energy systems. It ensues that the attainment of accurate system dimensioning is contingent upon the continual availability of spatially and temporally resolved measurements. The principal objective of this research endeavor is to expound upon the methodological approach employed in the computation of solar energy parameters, alongside the delineation of the pertinent dataset by extrapolating salient insights. Prior to the initiation of any optimization endeavor, a methodical scrutiny of the geospatial and temporal distribution of solar insolation stands as a preeminent prerequisite, indispensably contributing to the efficacious implementation of solar infrastructure. The assessment of solar energy generation potential within the examined region necessitates a meticulous investigation of the theoretical solar resource inherent to Khouribga. Through a meticulous computation regimen encompassing insolation and solar irradiance metrics, this investigation facilitates the discernment of the optimal incident angle for maximal energy absorption by solar photovoltaic cells.

Design and optimization of solar energy system with hydrogen energy storage and alkaline fuel cell

Design and optimization of solar energy system with hydrogen energy storage and alkaline fuel cell

Wind loads on a low-rise gable roof with and without solar panels and comparison to design standards

ABSTRACT This paper aims to understand how photovoltaic (PV) panels impact wind loads on low-rise buildings. The hypothesis posits that solar panels on a roof reduce wind-induced forces on components and cladding. To test this hypothesis, we experimentally investigated a 1:7.5 scale model in an open-jet wind facility, considering cases of bare roof and roofs with PV panels in three different configurations. The findings indicate that PV panels offer varying benefits based on the wind direction angle, generally reducing total wind forces on the primary structure. The addition of solar panels yields wind load reductions of 45–63%, depending on the configuration and details of the solar panel system, implying that buildings may not require additional reinforcement for PV panels. The findings have significant implications for enhancing the design and installation of residential solar energy systems, promising a more sustainable and secure future amid climate change and extreme weather challenges.

Optimization of a solar energy system integrating cooling, hot water, and power units in Australian cities: A climate-based analysis and cost-efficiency investigation

This study presents an investigation into the analysis and optimization of a solar energy system that integrates cooling, hot water, and power units. The system employs PV/T (photovoltaic/thermal) modules to generate electricity and provide thermal energy simultaneously. Using the response surface methodology (RSM), the system is subjected to multi-objective optimization to enhance its performance and decrease costs. The first optimization scenario focuses on maximizing net output power and minimizing the cost of the thermoelectric generator (TEG). Six choice factors are identified to improve the system's cost and exergy efficiency. Results indicate an optimal exergy efficiency of 19.704% and a favorable cost rate of 1.774 $/h for the proposed system. Economic analysis reveals that the most expensive subsystems are the proton exchange membrane (PEM) electrolyzer and organic Rankine cycle (ORC). Exergy analysis highlights the PV panels, absorption chiller, PEM electrolyzer, and ORC evaporator as components with significant exergy devastation. To assess the system's viability in Australian cities, it is analyzed across six climate zones. The city of Maitland emerges as the most optimal candidate for system implementation. The study demonstrates that the proposed approach performs best in cities with similar climates to Maitland. Considering the impact of environmental factors such as temperature and sunlight radiation is crucial for determining system costs, net output power, and exergy efficiency on a global scale. Overall, this research contributes to the understanding of solar energy systems by integrating cooling, hot water, and power units. It emphasizes the importance of climate-based analysis and optimization for efficient system performance. The findings provide valuable insights for the design and implementation of solar energy systems, facilitating their widespread adoption and contribution to sustainable energy solutions.

Comparison and interpretation of solarimetric station data (diffuse solar radiation, UVB radiation, temperature, and relative humidity) from January 2017 to November 2018 in Zacatecas

In this research work, the data from the solarimetric station in Zacatecas during the period from January 2017 to November 2018 are compared and interpreted. The main objective is to analyze the climate and solar radiation characteristics in the region and identify possible correlations between variables. The methodology involved data collection using the solarimetric station and data processing in Excel. Hourly, daily, and monthly averages were calculated for diffuse solar radiation, UVB radiation, temperature, and relative humidity. In addition, graphs were generated using Excel, and two R codes were developed: one to obtain correlations among the analyzed variables and another to visualize the UVB index. This study provides a detailed analysis of climate data and solar radiation patterns in Zacatecas. The obtained results are relevant for the design and implementation of solar energy systems in the region and in areas with similar climatic conditions. In summary, this study compares and interprets the data from the solarimetric station in Zacatecas, aiming to understand the climate and solar radiation characteristics. The conducted analyses contribute to the development of solar energy strategies in Zacatecas and similar regions. solar radiation, climate analysis, Zacatecas.

Interchromophoric Twist: Gateway to Long-Lived Excitons

Structure-property-function relationships play a key role in governing physical properties in molecules and materials in the broader sense. Particularly in organic light-harvesting materials, the sensitivity is magnified as the vibronic and electronic energy landscapes are highly sensitive to the type and extent of quantum chemical communication between molecules. In this article, we select a particular geometric feature- the interchromophoric twist and discuss its implication on several fundamental photophysical properties in connection with our lab’s research interests. We highlight two important processes, singlet fission (SF) and symmetry- breaking charge separation (SB-CS). We narrate how the interchromophoric rotation angle can be a crucial tool in achieving an appropriate electronic coupling in each of the cases and help us utilize these processes to achieve long- lived excitons (triplets and unbound electron-hole pairs) for the design and development of efficient artificial solar energy systems.

Comparative Study of Solar Radiation Models for the Estimation of Solar Radiation Using Short-Term Meteorological Data in Lawra, Ghana

Monthly average daily global solar radiation data are essential for the design and study of solar energy systems. The performance and accuracy of eleven models for the estimation of monthly average global solar radiation were compared in this study. Nineteen months (Nov 2020 – May 2022) ground measurement data consisting of monthly mean daily sunshine duration, relative humidity, minimum and maximum temperatures, and global solar radiation collected from the Lawra Solar Plant were used. The models were compared using statistical indices. According to the indices, most of the models were in reasonably good agreement with the measured data. Two model equations, however, were found to have the highest accuracy and can thus be used to estimate monthly average global solar radiation in Lawra and other places with similar climatic conditions where radiation data is unavailable.

Optimal design and operation of solar energy system with heat storage for agricultural greenhouse heating

A significant challenge of agricultural greenhouses is their high energy demand which is mainly satisfied by fossil fuels resulting in climate change impacts. In this paper, a joint design-operation linear optimization framework for a solar energy system with heat storage is developed to fulfill the agricultural greenhouse heating load. The energy system consists of solar collector, backup boiler, and short-long term heat storages. The developed framework is applied to reach minimum-cost solution. Then, the effects of emission reduction policies, greenhouse cultivation scheduling, natural gas price, and investment cost scenarios are investigated. Furthermore, a multi-objective optimization is performed in terms of minimizing CO2 emissions and total annual cost using epsilon-constraint method. The optimal energy system due to the minimum-cost solution includes a 1065 m2 solar collector and a 1265 kW boiler in combination with 967 kWh and 25 MWh short-term and long-term heat storages, respectively. The 30 % carbon reduction policy results in a 70.5 % increase in solar collector area. The selected optimal solution of the Pareto front, which is the closest solution to the ideal point, has 35.3 % more annual cost and 89.5 % less CO2 emissions compared to the minimum-cost solution.

A Solar Energy System Design for Green Hydrogen Production in South-Western Nigeria, Lagos State, Using HOMER & ASPEN

A Solar Energy System Design for Green Hydrogen Production in South-Western Nigeria, Lagos State, Using HOMER & ASPEN

Performance analysis of a stand-alone integrated solar hydrogen energy system for zero energy buildings

This study analyzes the optimal sizing design of a stand-alone solar hydrogen hybrid energy system for a house in Afyon, Turkey. The house is not connected to the grid, and the proposed hybrid system meets all its energy demands; therefore, it is considered a zero-energy building. The designed system guarantees uninterrupted and reliable power throughout the year. Since the reliability of the power supply is crucial for the house, optimal sizing of the components, photovoltaic (PV) panels, electrolyzer, storage tank, and fuel cell stack is critical. Determining the sufficient number of PV panels, suitable electrolyzer model and size, number of fuel cell stacks, and the minimum storage tank volume to use in the proposed system can guarantee an uninterrupted energy supply to the house. In this study, a stand-alone hybrid energy system is proposed. The system consists of PV panels, a proton exchange membrane (PEM) electrolyzer, a storage tank, and a PEM fuel cell stack. It can meet the continuous energy demand of the house is sized by using 10 min of averaged solar irradiation and temperature data of the site and consumption data of the house. Present results show that the size of each component in a solar hydrogen hybrid energy system in terms of power depends on the size of each other components to meet the efficiency requirement of the whole system. Choosing the nominal electrolyzer power is critical in such energy systems.

Particle swarm optimization algorithm with Gaussian exponential model to predict daily and monthly global solar radiation in Northeast China.

Reliable global solar radiation (Rs) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, Rs measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate Rs, particle swarm optimization (PSO) algorithm integrating Gaussian exponential model (GEM) was proposed for estimating daily and monthly global Rs in Northeast China. The PSO-GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997 to 2016 from four stations in Northeast China. The results showed that in different stations, the PSO-GEM with full climatic data as inputs showed the highest accuracy to estimate daily Rs with RMSE, RRMSE, MAE, R2, and Ens values of 1.045-1.719MJm-2d-1, 7.6-12.7%, 0.801-1.283MJm-2d-1, 0.953-0.981, and 0.946-0.977, respectively. The PSO-GEM showed the highest accuracy to estimate monthly Rs with RMSE, RRMSE, MAE, R2, and Ens values of 0.197-0.575MJm-2d-1, 1.5-7.0%, 0.137-0.499MJm-2d-1, 0.999-1, and 0.992-0.999, respectively. Overall, the PSO-GEM had the highest accuracy under different inputs and is recommended for modeling daily and monthly Rs in Northeast China.

Design, Greenhouse Emissions, and Environmental Payback of a Photovoltaic Solar Energy System

This study aims to design a 16.4 MW photovoltaic solar system located in the Brazilian Northeast and quantify the associated greenhouse gas emissions and environmental payback. The energy system was designed to minimize the Levelized Cost of Energy. The greenhouse gas emissions were quantified with the Life Cycle Assessment methodology, expressing the environmental impact in terms of generated energy (kg CO2-eq/kWh) and following ISO 14040 and 14044. The environmental payback considered the Brazilian electricity mix and degradation of the panels. The results indicated a system capable of producing 521,443 MWh in 25 years, with an emission factor of 0.044 kg CO2-eq/kWh and environmental payback of five years and eight months. The emission factor is at least ten times lower than thermoelectric natural gas power plants. The solar panels were the main contributors to the greenhouse gas emissions, representing 90.59% of overall emissions. It is concluded that photovoltaic energy systems are crucial in the search for emissions mitigation, even in a country that presents a predominantly renewable electricity matrix, with demonstrated environmental benefits.

Design and Optimization of a Grid-Connected Solar Energy System: Study in Iraq

Hybrid energy systems (HESs) consisting of both conventional and renewable energy sources can help to drastically reduce fossil fuel utilization and greenhouse gas emissions. The optimal design of HESs requires a suitable control strategy to realize the design, technical, economic, and environmental objectives. The aim of this study is to investigate the optimum design of a grid-connected PV/battery HES that can address the load requirements of a residential house in Iraq. The MATLAB Link in the HOMER software was used to develop a new dispatch strategy that predicts the upcoming solar production and electricity demand. A comparison of the modified strategy with the default strategies, including load following and cycle charging in HOMER, is carried out by considering the techno-economic and environmental perspectives. According to optimization studies, the modified strategy results in the best performance with the least net present cost (USD 33,747), unmet load (87 kWh/year), grid purchases (6188 kWh/year), and CO2 emission (3913 kg/year). Finally, the sensitivity analysis was performed on various critical parameters, which are found to affect the optimum results on different scales. Taking into consideration the recent advocacy efforts aimed at achieving the sustainable development targets, the models proposed in this paper can be used for a similar system design and operation planning that allow a shift to more efficient dispatch strategies of HESs.