Solar Installations Research Articles

Integrating Technological Environmental Design and Energy Interventions in the Residential Building Stock: The Pilot Case of the Small Island Procida

The next decade will see severe environmental and technological risks, pushing our adaptive capacity to its limits. The EPBD Case Green directive, to counter this phenomenon, emphasizes accelerating building renovations, reducing GHG emissions and energy consumption, and promoting renewable energy installations. Additionally, it calls for deadlines to phase out fossil fuels and mandates solar system installations. This research provides a comprehensive perspective on the opportunities for and challenges of incorporating renewable energy into the built environment. It focuses on the 2961 residential buildings on Procida, a small island located south of Italy, to efficiently utilize energy resources and lay the groundwork for sustainability. Beginning with an analysis of the territorial, urban, historical–conservation, structural, and geological context, in addition to environmental assessments, the research develops a classification and archetypalization system using in-house software. This system aggregates data on the island’s residential buildings, analyzes their current state, and formulates various intervention scenarios. These scenarios demonstrate how integrating technological–environmental design interventions, such as upgrading the building envelope and enhancing bioclimatic behavior, with energy retrofitting measures, such as replacing mechanical systems and installing solar panels, can improve the overall performance of the existing building stock and achieve energy self-sufficiency.

Rapid assessment of solar PV micro-system energy generation in Poland based on freely pvlib-python library

Abstract Poland has experienced a remarkable growth in renewable energy adoption, notably in photovoltaic (PV) solar systems. The majority of installations are prosumer PV micro-installations, exceeding predicted capacity targets outlined in the Energy Policy of Poland until 2040. Despite the significant growth in installed PV capacity, there is still a lack of comprehensive research focusing on fast assessment of energy generation capacity for solar PV micro-systems. This study aims to address this gap by providing a comprehensive analysis of energy production potential across different configurations and locations in Poland. Using geocoding techniques, solar irradiation data from PVGIS database and pvlib-python library, a methodology was developed to rapidly estimate energy generation from 1 kWp solar PV systems . Results reveal spatial disparities in energy yield from solar PV micro installations in Poland, influenced by factors such as geographical location and panel orientation and inclination. Recognizing that the presented energy indicators provide valuable initial parameters for determining solar PV system power output, this data can serve as a critical reference point for stakeholders, assisting them in estimating potential energy generation capacities in different regions of Poland.

Transient modeling of stratified thermal storage tanks: Comparison of 1D models and the Advanced Flowrate Distribution method

Thermal energy storage (TES) is one of the key technologies for enabling a higher deployment of renewable energy. In this context, the present study analyzes the modeling strategies of one of the most common TES systems: stratified thermal storage tanks. These systems are essential to many solar thermal installations and heat pumps, among other clean energy technologies. Three different one-dimensional tank models are compared by their computing speed and resilience to long time steps. Two of the models analyzed are numerical, one being explicit and the other one implicit, and the other is analytical. The models are validated against data from experiments carried out considering small-scale stratified tanks, showing that their performance can be improved by using the Advanced Flowrate Distribution (AFD) method. The results show that the analytical model maintains its accuracy with longer time steps and is robust against divergence. Conversely, the numerical models show equivalent performance for short time steps, while the computation time is reduced. Although the AFD method shows promising results by achieving an improvement of 43% in terms of Dynamic Time Warping, its parameter optimization must be generalized for different tank designs, flow rates, and temperatures.

Field measurements of wind load effects in a photovoltaic single-axis tracker mounting rail

The demand for solar photovoltaic power installations has resulted in a highly competitive industry. Equipment suppliers are under pressure to reduce design margins for projects to be feasible. Single-axis trackers are widely used to increase yield and reduce the levelised cost of energy compared to fixed-tilt installations. Load coefficients on tracker arrays are usually quantified using model-scale studies conducted in atmospheric boundary layer wind tunnels. These dynamically sensitive structures have been known to suffer wind damage despite the use of modern design methods. Ongoing research into the support structure design is deemed important to ensure long-term reliability. The current study presents full-scale field data acquired over 109 days on an experimental one-in-portrait single-axis tracker facility located in the Western Cape, South Africa. The purpose of these measurements was to quantify wind load effects in two instrumented PV module mounting rails. Frequency domain data showed modal participation of both the torsional and bending modes of the torque tube. The effect of the bending mode was more pronounced for the rail furthest from a pile. Load coefficients calculated using experimental data were generally lower than those presented in ASCE 7-22. Fluctuating stresses at critical locations led to negligible fatigue damage.

IoT-Enabled energy conservation in residential Buildings: Machine learning models for analyzing annual solar power consumption

This study investigates enhancing the self-consumption of solar panels by integrating thermal and electrical energy storage using lithium-ion batteries in a duplex building over a year. Machine learning algorithms are applied to understand energy usage patterns. An IoT-connected model is introduced to reduce electricity expenses by boosting consumption with solar energy and extending solar panel lifespan through optimization. The model includes lithium-ion batteries for energy storage and a thermal cooling system to regulate panel temperatures. Data collected from sensors monitoring occupancy, temperature, humidity, energy, and thermostats are sent to a cloud-based platform via IoT. Results show peak monthly energy conservation measures of 41.6 kW and 28 kW during summer for the two residences. Winter witnesses a reduction of 13.9 % and 7 % in energy consumption, while autumn sees an 18 % decrease for one residence and stability for the other. Real-time IoT monitoring aids model training, with the Gaussian Probability model emerging as the most accurate. Optimization analysis underscores the thermal cooling system’s efficacy in improving solar panel performance and reducing electricity costs. Integration of machine learning and IoT curtails energy consumption to 22 kWh and 15.7 kWh for the respective residences, offering inhabitants free energy from renewable sources and ensuring swift recovery of solar panel installation costs.

Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs

This research explores the application of digital twin technologies to progress the United Nations’ Sustainable Development Goals (SDGs) within the water-energy-food-environment (WEFE) nexus management in urban refugee areas. The study in Irbid Camp utilizes a detailed 3D Revit model combined with real-time data and community insights processed through advanced machine learning algorithms. An examination of 450 qualitative interviews indicates an 80% knowledge level of water conservation practices among the community but only 35% satisfaction with the current management of resources. Predictive analytics forecast a 25% increase in water scarcity and an 18% surge in energy demand within the next ten years, prompting the deployment of sustainable solutions such as solar energy installations and enhanced rainwater collection systems. By simulating resource allocation and environmental impacts, the digital twin framework helps in planning urban development in line with SDGs 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). This investigation highlights the capacity of digital twin technology to improve resource management, increase community resilience, and support sustainable urban growth, suggesting its wider implementation in comparable environments.

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

A novel development of hybrid maximum power point tracking controller for solar pv systems with wide voltage gain DC-DC converter

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink.

Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Short-Term forecasting of floating photovoltaic power generation using machine learning models

Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations.

Analysing the prospects of grid-connected green hydrogen production in predominantly fossil-based countries – A case study of South Africa

Importing substantial amount of green hydrogen from countries like South Africa, which have abundant solar and wind potentials to replace fossil fuels, has attracted interest in developed regions. This study analyses South African strategies for improving and decarbonizing the power sector while also producing hydrogen for export. These strategies include the Integrated Resource Plan, the Transmission Development Plan, Just Energy Transition and Hydrogen Society Roadmap for grid connected hydrogen production in 2030. Results based on an hourly resolution optimisation in Plexos indicate that annual grid-connected hydrogen production of 500 kt can lead to a 20–25% increase in the cost of electricity in scenarios with lower renewable energy penetration due to South African emission constraints by 2030. While the price of electricity is still in acceptable range, and the price of hydrogen can be competitive on the international market (2–3 USD/kgH2 for production), the emission factor of this hydrogen is higher than the one of grey hydrogen, ranging from 13 to 24 kgCO2/kgh2. When attempting to reach emission factors based on EU directives, the three policy roadmaps become unfeasible and free capacity expansion results in significant sixteen-fold increase of wind and seven-fold increase in solar installations compared to 2023 levels by 2030 in South Africa.

Development assessment of regional rooftop photovoltaics based on remote sensing and deep learning

Assessing the development of rooftop photovoltaic (PV) plays a positive role in promoting the deployment of solar installations. In response to the problem that previous studies did not consider the PV already installed on rooftops and thus had a low level of refinement, this study proposes a dual-branch framework based on remote sensing imagery and deep learning to effectively monitor the current status and estimate the future potential of regional rooftop PV installations. Based on the dual-branch framework, a semantic segmentation network called MANet is designed that integrated multi-attention modules for more accurate extraction of roofs and PV panels from remote sensing imagery. Finally, the methodology is validated through an application in Taihuyuan Town, Hangzhou City. Model comparisons show that MANet achieves the best accuracy in both roof and PV panel extraction, with IoU scores of 88.17% and 91.58%, respectively. The difference between the existing PV installed capacity and the roof area extracted by the framework and the corresponding statistical data is 8.1% and 11.7%, respectively. The framework calculates the existing rooftop PV installed capacity to be 16,173.74 kW, and estimates the PV installed capacity potential of the remaining rooftops to be 403 MW.

Solar thermal hot water system in hospitals: Robust design methodology considering uncertainties

The energy yield of solar thermal installations is influenced by local climatic conditions, heating demand, and the efficiency of the system. These factors are affected by uncertainties arising from natural variations and a lack of knowledge. Designers typically address these uncertainties by oversizing the collector area. However, this practice usually leads to high costs, low efficiency, and short service lifetimes of the system. This paper proposes a robust sizing methodology for the solar thermal system used for service hot water preparation in a case-study hospital with a daily average hot water consumption of 8.69 m3. This novel approach incorporates uncertainties through probabilistic simulations, assessing the system’s reliability in achieving a 70% solar fraction design goal. The simulations reveal a mere 22% confidence in the initial deterministic design concerning the solar target and emphasize the importance of thermal insulation, temperature control, and collector cleaning as key factors to reduce performance variability. Although the final revised design outcome requires a 15% increase in the collector area compared to the initial scenario and stricter specifications for control components, pipe insulation, and valve maintenance, it achieves a 90% reliability. In addition, annual carbon emissions and levelized costs of hot water production can be reduced by 18% and 13%, respectively.

A Model Validatory Approach in Determining Solar Panel Tilting Angles and Orientations at the Brikama Environment of The Gambia

Solar energy demand in The Gambia has got a boost in recent times owning to it cleaner nature and affordability compared to other renewables and by this many people are shifting towards solar PV installation. To optimize the design of any PV system for maximum performance, adequate knowledge of the orientation and tilt angles of the solar panel against the sun radiation received by the solar panel is vital. This paper determines the solar optimal tilt angle and orientation of solar panels in the Brikama environment of The Gambia. This was achieved by comparing a mathematical model results with experimental data collected using a PV module (Model AP-120) manufactured by AstroPower, Inc., Deleware, USA, and single axis tracker technology at the same location and months of the year 2022. The experimental tilt angles obtained range from 29.85-30.14 with an average angle of 29.99 while the mathematical model approach obtained the tilt angle to be 30. By comparing all the values recorded during the experiment, the peak value of power generated by the solar panel was recorded at an angle of 30due South and this proves our techniques in determining the tilted angles with our applied model. The monthly tilt angles of the solar panel, which were averaged from the total months of the year and the average tilt angle in the Brikama environment is 15.5and the solar module should face south during installation for optimization. These results are vital in maximizing solar energy collection from photovoltaic systems.

Comprehensive study on the efficiency of vertical bifacial photovoltaic systems: a UK case study

This paper presents the first comprehensive study of a groundbreaking Vertically Mounted Bifacial Photovoltaic (VBPV) system, marking a significant innovation in solar energy technology. The VBPV system, characterized by its vertical orientation and the use of high-efficiency Heterojunction cells, introduces a novel concept diverging from traditional solar panel installations. Our empirical research, conducted over a full year at the University of York, UK, offers an inaugural assessment of this pioneering technology. The study reveals that the VBPV system significantly outperforms both a vertically mounted monofacial PV (VMPV) system and a conventional tilted monofacial PV (TMPV) system in energy output. Key findings include a daily power output increase of 7.12% and 10.12% over the VMPV system and an impressive 26.91% and 22.88% enhancement over the TMPV system during early morning and late afternoon hours, respectively. Seasonal analysis shows average power gains of 11.42% in spring, 8.13% in summer, 10.94% in autumn, and 12.45% in winter compared to the VMPV system. Against the TMPV system, these gains are even more substantial, peaking at 24.52% in winter. These results underscore the VBPV system's exceptional efficiency in harnessing solar energy across varied environmental conditions, establishing it as a promising and sustainable solution in solar energy technology.

Solar Energy Received on Flat-Plate Collectors Fixed on 2-Axis Trackers: Effect of Ground Albedo and Clouds

This study investigates the performance of isotropic and anisotropic diffuse models to estimate the total solar energy received on flat-plate collectors fixed on dual-axis trackers. These estimations are applied at twelve sites selected in both hemispheres with different terrain and environmental conditions. The diffuse (or transposition) models used in this study are the isotropic Liu-Jordan (L&J), Koronakis (KOR), Badescu (BAD), and Tian (TIA), and the anisotropic Hay (HAY), Reindl (REI), Klucher (KLU), Skartveit and Olseth (S&O), and Steven and Unsworth (S&U). These models were chosen because of their simplicity in the calculations and minimum number of input values. The results show that a single transposition model is not efficient for all sites; therefore, the most appropriate models are selected for each site under all, clear, intermediate, and overcast conditions in skies. On the other hand, an increase in the ground albedo in the vicinity of the solar installation can increase the annual inclined solar availability on a two-axis tracker by at least 9% on average. Further, a linear dependence of the annual inclined solar energy on the variation of the ground albedo was found. Also, a linear relationship exists between the annual diffuse-fraction and cloud-modification factor values at the 12 sites.

5G as Communication Platform for Solar Tower Plants

Wiring of heliostat fields for solar tower plants is a cost factor that becomes more important as the overall cost target is decreasing. Wireless heliostats with radio communication and autarchic energy supply have therefore been proposed in the past. But none of the communication solutions investigated so far could realistically scale to commercial size plants with tens of thousands of heliostats. Moreover, the digitalization of CSP plants with numerous mobile and stationary sensor systems requires a suitable data communication, too. The new generation of mobile radio communication (5G) is capable of handling the heterogenous communication profile portfolio comprising large numbers of units with low data rates – like heliostats - and few units with very large data volume – like e.g. drone-based camera systems. The communication requirements of a typical solar tower installation are assessed in this work and a data traffic model is created for the most relevant communication channels. The various existing 5G implementations are assessed to find the most suitable solution. Different operator models for 5G are considered and their applicability in CSP target countries is discussed. A simulation test case is presented that models the radio communication traffic of a heliostat field control during a cloud passage. Finally, the experimental 5G campus network is introduced that is currently installed at the Solar Tower Jülich research plant and will be operated in the upcoming months to demonstrate the technical feasibility of 5G radio for communication in solar fields.

Performance evaluation of coloured filters on PV panels in an outdoor environment

AbstractThis study aims to investigate the effects of colour filters and tilt angles on the electrical output characteristics of solar photovoltaic modules. The experimental set‐up allowed for the evaluation of various combinations of colour filters and tilt angles by using a realistic simulation of solar panel installations. The various light spectrum wavelengths and energy levels were taken into account, keeping in mind any potential impacts on the solar cells' conversion efficiency. The results showed that compared to other filter combinations, the use of red colour filters with a 10% blockage rate and an optimal tilt angle led to a noticeable increase in electrical output. This result emphasises the value of colour filtering and tilt angle selection in raising solar photovoltaic systems' overall performance. When compared to the other filters, the red, orange, and yellow filters stand out due to their outstanding efficiency of around 84.98%, 84.08%, and 83.69%. However, the teal, green, and blue filters exhibit relatively low levels of efficiency of about 69.62%, 67.06%, and 58.52%, respectively. The distinct reaction shown by purple and pink filters, when subjected to tilt angle of 30 degrees, offers a potentially productive direction of enhancing the efficiency of PV panel.

Predictive Modeling of Solar PV Panel Operating Temperature over Water Bodies: Comparative Performance Analysis with Ground-Mounted Installations

Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction.