Solar Energy Production Research Articles

A review of PV solar energy system operations and applications in Dhofar Oman

<abstract> <p>Energy is seen as one of the most determinant factors for a nation's economic development. The Sun is an incredible source of inexhaustible energy. The efficiency of the conversion and application of Photovoltaic (PV) systems is related to the PV module's electricity generation and the location's solar potentials. Thus, the solar parameters of a region are important for feasibility studies on the application of solar energy. Although solar energy is available everywhere in the world, countries closest to the equator receive the greatest solar radiation and have the highest potential for solar energy production and application. Dhofar in Salalah-Oman is one of the cities in Oman with high temperatures all year round. The city has been reported to exhibit a maximum solar flux of about 1360 w/m<sup>2</sup> and a maximum accumulative solar flux of about 12,586,630 W/m<sup>2</sup> in March. These interesting solar potentials motivated the call for investment in solar energy in the region as an alternative to other non-renewable energy sources such as fossil fuel-powered generators. As a consequence, several authors have reported on the application of different solar energy in the different cities in Oman, especially in remote areas and various results reported. Therefore, the present review highlighted the achievements reported on the availability of solar energy sources in different cities in Oman and the potential of solar energy as an alternative energy source in Dhofar. The paper has also reviewed different PV techniques and operating conditions with emphasis on the advanced control strategies used to enhance the efficiency and performance of the PV energy system. Applications of standalone and hybrid energy systems for in-house or remote power generation and consumption in Dhofar were discussed. It also focused on the relevance of global radiation data for the optimal application of PV systems in Dhofar. The future potential for the full application of solar systems in the region was mentioned and future work was recommended.</p> </abstract>

Modelling of Solar Power Production in Dry and Rainy Seasons Using Some Selected Meteorological Parameters

In this paper, we deployed the multiple linear regression method in developing a solar power output model for solar energy production, where the meteorological parameters are the independent variables. We fitted the model and found that the meteorological variables considered accounted for 94.88% and 99.61% of the power output in both dry and rainy seasons. We observed from the work that the solar panel performs well in all seasons but slightly better in the rainy seasons. This could be attributed to the washing away of dust particles from solar panels by the rain and higher operating temperature different from the specified manufactured temperature of 25°C. We observed that other factors such as the cloud slightly affect the optimal performance of the system. Panels inclined at an angle of 5° (Tilt) and facing south azimuth performs optimally, periodic washing of the surface of solar panels enhances optimal performance.

Solar energy potential in Freiburg, Graz, Maribor, Banja Luka, Nis, and Athens

This paper presents a comparative analysis of solar energy potential for six different cities, in six different countries in Europe: Freiburg (Germany), Graz (Austria), Maribor (Slovenia), Banja Luka (Bosnia and Herzegovina), Nis (Serbia), and Athens (Greece). Data processed in this work are accessed from Photovoltaic Geographical Information System (PVGIS). Photovoltaic technology is crystalline silicon, and installed peak photovoltaic power is 5 kWp. The aim of the work is to find out whether there are statistically significant differences among the cities in relation to monthly energy production in regard to different types of photovoltaic system (fixed - free standing, fixed - building integrated, inclined, and two axis solar power plants). The work is based on four hypotheses. The estimation of solar energy production in different regions is very important for determination of potential regions suitable for generation of renewable and sustainable energy.

A New Integrated Approach for Determination of Turkey’s Solar Energy Production Strategies: The Fuzzy Expanded SWOT

Because of Turkey's high solar energy potential due to its geographical location and certain advantages of the solar energy, the solar energy is one of the most important among renewable energy types which are invested in the country. Therefore, it aimed to determine of the strategies related to Turkey's solar energy production in this study. For this, a new integrated method called the Fuzzy Expanded SWOT, consisting of the Fuzzy AHP and the Expanded SWOT methods, has been proposed. The proposed method eliminates many disadvantages of the traditional SWOT analysis. First of all, factors and sub-factors related to the solar energy were determined by the Expanded SWOT analysis. Subsequently, the local weights of the factors and sub-factors were determined by the Fuzzy AHP method. The sub-factors which are insignificant according to the weights found, were eliminated and the global weights of the remaining sub-factors were found. Finally, sub-factors were prioritized according to their global weights and strategies were determined based on the Expanded SWOT matrix, taking these priorities into consideration. As a result of the analyzes, it was observed that the strategies which should be dwelled on related to solar energy production in the Turkey, are ones which concentrate on R&D studies in public institutions, private sector and universities.

Design of a Sustainable City in Iraq Using SAM Program to Calculate Renewable Energy

Urban design and human activity are main causes of environmental pollution and climate change, such as reliance on energy production plants, factories and transportation that depend on fossil fuels, in addition to transformation of most green areas into residential areas. This paper aims to establish a sustainable city with an environment friendly urban design that contributes to exploitation of natural resources in the generation of electric power, use of green means of transport and thus the reduction of pollution. Anah site was chosen as the best site for designing a sustainable city, with its natural resources, air quality and moderate climate. The total area of ​​the city is 9990187m2 divided into seven main parts, which include residential area, green areas, agricultural areas, public areas, wind farm, industrial area, and solar collecting area. SAM software has also been used in the city's renewable energy accounts. It was found that the annual output of the wind farm consisting of 15 turbines is 71080608 kWh and the power factor ratio is 36.1%. While the annual production of solar energy for each house was equal to 12527 kWh and the power factor was 23.8%.

The use of artificial neural networks and big data infrastructure for predictive analytics in solar energy

Abstract Renewable energy appears to be the solution to both the continuously growing energy demand, and pollution from fossil-based fuels. Recent advances in big data means that crucial areas of the energy supply chain are of interest to the use of advanced analytics. Solar energy is one of the most important renewable energy sources; however, it is stochastic, leading to production volatility and making it difficult to dispatch. The European Commission provides the legal framework and guidelines for increasing the adoption of renewable technologies in the European Union (EU). Meanwhile, the research community must provide solutions for increasing the predictability of solar energy: successful integration depends on how well solar energy production is predicted. Working under the Cross-Industry Standard Process for Data Mining, using real word operational data, this research focuses on providing a foundation of the analytics capabilities needed for reducing, or even removing, the disadvantages of solar energy, demonstrating that a world-class predicative tool can be obtained. Using weather and production data from photovoltaic cells installed in Romania, as a case study, coupled with the powerful artificial neural networks (ANN) architecture, results in a benchmark prediction performance. Currently, there is no research addressing photovoltaic energy production prediction by integrating the impact of artificial intelligence and big data.

Understanding mechanisms of snow removal from photovoltaic modules

Snow accumulation on photovoltaic (PV) modules causes major economic losses by reducing/preventing solar energy production. To develop mitigation strategies for snow accumulation issues on the PV modules, it is crucial to understand various snow removal mechanisms from them. Here, natural snow accumulation and its removal from the PV modules were investigated over two years using a surface-mountable sensor that measures liquid water content (LWC) at the interface of snow and module’s surface real-time. It was found that the snow removal from the PV modules occurs under four distinct categories: melting, shedding (fast sliding), prolonged melting, and melting followed by shedding. During snow melting, the average LWC of snow and the module’s surface temperature increased gradually (13.45%/hr and 0.81 °C/hr), while during snow shedding the average LWC and surface temperature sharply increased (14.4%/hr and 1.46 °C/hr). During prolonged melting, the average LWC of snow slowly increased during the day (4 average LWC slopes were 5.88, 3.22, 6.18, and 7.92%/hr), and the module’s surface temperature reached 0 °C for a very short period of time, then decreased back to freezing temperatures at night. Lastly, the final snow removal mechanism is melting followed by shedding, during which the average LWC of snow increased gradually indication of snow melting (LWC of 11.25%/hr), followed by a very sharp increase in LWC with an average rate of ∼83.02%/hr, after which snow shedding occurred. These understanding provide means to develop a combined passive and active mitigation strategy for efficient snow removal from the photovoltaic modules.

Photovoltaic-grid control method based on self-gain system compensation

Abstract The presence of decentralized production in modern electrical systems precludes traditional automatic generation control (AGC) strategies. Renewable energy production is highly dependent on the environment; for example, photovoltaic (PV) system, but its power generation is very unstable, which makes power grids containing PV systems face greater challenges. In this paper, we propose a PV-grid control method based on self-gain system compensation to reduce the negative effects of large fluctuations and uncertainties in solar energy production. First, the performance characteristics of solar modules are discussed and a multi-region model with a system with high penetration of solar systems is built. The AGC power is then displayed with different power variations according to different maximum power point tracking methods to analyze the uncertainty and variation of the PV output power and its effects on the AGC. We have also developed and implemented a compensation unit to eliminate the negative effects of PV power on AGC. Finally, the proposed method was demonstrated using the load from the example step as a disturbance variable to obtain a dynamic model of a dual-zone switched grid AGC with two PV systems based on a DMPC-distributed model. As a result of the simulation, the proposed method in this paper can guarantee the excellent properties of the dynamic behavior of the AGC system with large variations and high uncertainty of the PV system, and the validity of the proposed method have also been confirmed.

O SETOR ENERGÉTICO NO BRASIL: UM DEBATE SOBRE A POTENCIALIDADE DAS FONTES RENOVÁVEIS NO CONTEXTO AMBIENTAL E TECNOLÓGICO

This article aimed to discuss the challenges of renewable energies in Brazil, with emphasis on the exhaustion of the current development model, the role of Research and Development (R&D) and the role of the State for an environmentally sustainable energy transition. To identify the pattern of specialization and concentration of renewable energies in Brazil, a mapping was carried out using indicators: Location Quotient (QL) and Hirschman-Herfindahl Index (HHI). In addition, an exploratory analysis of Greenhouse Gas (GHG) emission data was carried out, enhancing discussions on the contribution of the energy sector to the mitigation of climate change. The results showed evidence of specialization in renewable energy in most Brazilian states and a high concentration in the production of wind and solar energy, especially in the Northeast region. Nevertheless, the energy sector was characterized as the 3rd most GHG emitter sector, with a growth of 114% between 1990 and 2019. Thus, within this framework, it is possible to identify that Brazil has numerous potentials for expansion in electricity generation through renewable sources. However, it is necessary to solve some bottlenecks and overcome some barriers that prevent a use compatible with its potential. Therefore, this work aims to contribute to the debate on environmentally correct energy transition and the development of public policies in the sector.

A measured energy use, solar production, and building air leakage dataset for a zero energy commercial building

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m2 (3,531 ft2) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour resolution by 56 microinverters (presented as daily and monthly data in this dataset). The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modeling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings.

Deciphering the thermal behavior of floating photovoltaic installations

Floating photovoltaic systems are an attractive, emerging concept to extend the area available for solar energy production to the water. Among the advantages of floating PV, frequently a cooling effect is mentioned. Yet little to no quantitative information about this cooling is provided in available literature. In this study, we present observations that challenge the role of water for photovoltaic module cooling. When comparing temperatures of two photovoltaic installation in Cambodia, we found that photovoltaic modules from a commercial floating installation at noon were significantly (9.1 ± 2.8 K) hotter than those from a nearby, small-scale installation on a metal rooftop. This large difference was all the more unexpected because the rooftop installation was on top of a room with electronic equipment that generated additional heat. When reproducing the temperatures of either system by modeling, we found that the most likely cause for this temperature difference is different exposure to wind of the two installations due to different PV module heights. Comparing temperatures at night, we find that the floating installation is 1.2 ± 0.5 K cooler than the rooftop installation. This difference can likely be attributed in parts to evaporation cooling. We conclude that the presence of water is a minor- or an indirect factor in the thermal balance of floating photovoltaic installations. Exposure to wind and module height are the differentiating elements for photovoltaic system cooling, at least in areas with small overall wind speeds.

Effects of floating photovoltaic systems on water quality of aquaculture ponds

Establishing floating photovoltaic (FPV) systems on aquaculture ponds can reduce demand for land use and affects food and solar energy production. This study investigated the water quality of aquaculture ponds with and without simulated FPV systems (40% surface area shading) at three sites: Chupei, Lukang and Cigu. Results indicated the FPV-covered ponds exhibited lower mean values in biochemical oxygen demand and plankton biomass but higher oxidation–reduction potential relative to the control ponds. The FPV-covered ponds exhibited lower pH, water temperature and level of dissolved oxygen relative to the control ponds in Chupei and Lukang. The results suggested that the FPV shading effect potentially reduced phytoplankton growth. All FPV-covered ponds exhibited 1.1, 1.2 and 1.4 times greater yields in giant freshwater prawn, tilapia and milkfish without any effect on the growth of cultured species. These results demonstrate the potential benefits and defects of combining aquaculture with FPV systems.

Photoelectrocatalytic degradation of Rhodamine B by spray deposited Bi2WO6 photoelectrode under solar radiation

In this work, visible region active Bi2WO6 thin films with variable thickness are successfully deposited by simple and effective spray pyrolysis technique. The structural, optical, morphological and photoelectrochemical properties of synthesized thin films were studied. Rietveld refinement of the structural data tested purity and degree of crystallinity of Bi2WO6 thin films synthesized with variable solution quantity. Optical study revealed visible light absorption of films and small variation in band gap energy. A thin film deposited with 70 ml spraying solution quantity exhibit higher photocurrent density (460 mA/cm2) and same film with large area (10 × 10 cm2) was used for photocatalytic and photoelectrocatalytic degradation of Rhodamine B (RhB) dye under solar radiation. The photoelectrocatalytic removal of RhB exhibit higher degradation efficiency (94%) as compared to photocatalytic process (23%). This study can trigger potential application of Bi2WO6 photoelectrode in solar energy conversion, wastewater treatment and energy production.

Improvement of BIPV Efficiency by Application of Highly Reflective Surfaces at the Building Envelope

The use of concentrated solar irradiation for the improvement of electric generation improvement has been implemented on different scales, mainly in photovoltaic systems. High-concentration Fresnel lenses are widely chosen for this approach in large installations, while low-concentration systems are rather applied in medium-low scales. For the latter, the improvement on electric performance was revealed, even when no solar tracking was implemented. The presented work aims to analyse a low-concentration photovoltaic installation by a numerical approach. First, the reflective surfaces were designed geometrically considering the optimal slope determined for each month. Subsequently, different simulation techniques were used separately for prediction of solar irradiation and energy production. Three criteria were selected to analyze power generation: the highest increase in total annual solar irradiance on panels with reflective surfaces, the highest total annual solar irradiance collected, and the optimal slope of panels for the entire year. The increase in energy was found to not exceed 10% in the winter months. Whereas in the spring and summer months the energy improvement is about 15–20%. Moreover, it was observed that the temperature of the proposed concentration photovoltaic system increased significantly, reaching more than 90 °C, while for traditional PV panels it did not exceed 75 °C.

Analysis of Building Archetypes for Optimising New Photovoltaic Energy Facilities: A Case Study

Fuel poverty rates are high in southern Europe, particularly in social housing, despite the enormous potential for capturing solar power inherent in the roofs of apartment buildings. The in situ generation, distribution and consumption of photovoltaic energy carry obvious advantages including vastly improved efficiency attendant upon the reduction in distribution-related losses and costs, and the energy empowerment afforded lower income communities. The primary drawback is the imbalance between photovoltaic production patterns and users’ actual needs (peak consumption vs. peak generation). That mismatch is difficult to reconcile without resorting to energy storage or net metering, both of which entail grid involvement and greater management complexity. The present study introduces a methodology for analysing residential archetypes to determine the values of the parameters essential to optimising photovoltaic energy production and use. The aim is to determine where excess generation can be shared with other users in the vicinity and optimally pool residential rooftop facilities to meet community-scale energy demand, ultimately enhancing such disadvantaged neighbourhoods’ self-sufficiency. The case study discussed defines archetypes for just such a neighbourhood in Madrid, Spain. The solar energy production potential of the example is promising for its application in large southern European cities, with self-sufficiency rates obtained ranging from 15% to 25% and self-consumption rates from 61% to 80%.

Hybrid Multi-Criteria Decision Making approach for the evaluation of sustainable photovoltaic farms locations

As a result of the increased demand for energy combined with the limitations of traditional energy sources, it is necessary to turn to renewable energy sources such as solar energy, which is one of the most promising technologies to assist the transition towards sustainable development. With the increasing interest in renewable energies in general and solar energy in particular, attempts have been made to ensure that solar energy technologies provide an amount of energy equal to or close to the amount of energy currently consumed. Solar photovoltaic farm applications have become so widespread that they are now relied upon to produce massive amounts of energy for use in factories and homes. In this regard, identifying and prioritizing convenient areas for the construction of expensive solar photovoltaic farms is an important issue. Additionally, determining the suitable geographical locations for photovoltaic farms is not only related to the amount of solar radiation received, but many dimensions must be taken into accounts such as technical, economic, environmental, socio-political dimensions, risks, and their sub-indicators. To overcome the problem of prioritizing and selecting the best location for solar photovoltaic farms to achieve sustainability factors resulting from the presence of many different indicators, this paper applies a hybrid multi-criteria decision-making approach under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators and available alternatives. The proportional importance of the designated dimensions and their sub-indicators was evaluated by the Decision-Making Trial and Evaluation Laboratory. Then, Vlse Kriterijumska Optimizacija Kompromisno Resenje is adopted to rank seven solar photovoltaic farm locations in Egypt. The Benban solar photovoltaic farm is shown to be the most sustainable alternative among the solar photovoltaic farm locations followed by Darb Al Arbaeen solar photovoltaic farm and so has priority for increased installation investments in solar energy production. To validate the proposed approach, results comparisons were implemented to indicate the advantages of the hybrid multi-criteria decision-making methods. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order.

Prediction of Cloud Fractional Cover Using Machine Learning

Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually projected using numerical climate methods. In this paper, we explore the potential of using machine learning as part of a statistical downscaling framework to project future CFC. We are not aware of any other research that has explored this. We evaluated the potential of two different methods, a convolutional long short-term memory model (ConvLSTM) and a multiple regression equation, to predict CFC from other environmental variables. The predictions were associated with much uncertainty indicating that there might not be much information in the environmental variables used in the study to predict CFC. Overall the regression equation performed the best, but the ConvLSTM was the better performing model along some coastal and mountain areas. All aspects of the research analyses are explained including data preparation, model development, ML training, performance evaluation and visualization.

Co-model for urban density permutations and building form optimization with high-performance energy criteria

Computational models capable of generating accurate site metrics for building performance analysis are often limited, complex and time-consuming. To address this gap, a new procedure has been developed with a five-step optimization process relevant to examining urban forms with buildings density constraints, window-to-wall ratio, incident solar radiation and energy production potential in façades. Factors analyzed include floor space intensity ( FSI ), building height ( H ), spatial daylight autonomy ( sDA ), annual sun exposure ( ASE ) and the effect of façade tilt angles on efficiency targets in cold climates. A myriad of parametric functions has been incorporated in the process through Grasshopper, DIVA, ArchSIM, and evolutionary algorithms in Galapagos. The study revealed effective results of energy production across prominent façade design options. Increasing FSI was found to decrease sDA , for a fixed H . In contrast, increasing H for a fixed FSI decreased sDA with an insignificant magnitude of 4%. The ratio of increase in glazing to increase in sDA was in the order of 1:1.47. Overall, the findings showed novelty in optimizing each façade panel to achieve best performance demonstrated by different urban densities with low/mid/high-rise building conditions. Energy production reached 13 kWh/m 2 with integrated photovoltaics , as a renewable source. High-rise buildings showed the best conceivable energy improvement as opposed to other height conditions. The improvement percentage was observed to be directly proportional to the height difference between the studied building and the surrounding context. • Five-step method to determine the impact of different urban densities on energy production. • Novel optimization method to achieve optimum performance for each façade panel. • Demonstrating the relationship between urban context and renewable energy production. • Parametric workflow that can be re-applied to different contexts and case studies.

Reconfigurable linkage structures and photovoltaics integration

A type of reconfigurable building structures is presented which comprises an arrangement of multilink planar linkages. Interconnections are applied to allow for the individual linkages to move together and the control action to be transferred between them. The linkages constitute the skeleton structure and define the building envelope. The elementary planar linkages include a central sliding member on an elevated midspan structural mechanism. On either side of the slider connects a chain of serially-connected members, the other end of which is pin-connected to the ground. Actuation is directly applied on the sliding member, while all rotational joints are equipped with brakes as required to implement a multistep reconfiguration control method. In each step, a selective application of the brakes appropriately modifies the kinematic structure of the linkage to resemble a dual version of a crank–slider mechanism. This is a 1-DOF system which is favorable from a control perspective. Simulations are employed to demonstrate the concepts. Finally, thin-film photovoltaic modules integrated on the spatial structure highlight potential benefits enabled by reconfigurable buildings with regard to the solar irradiance and energy production. • A reconfigurable building structure has been investigated. • The spatial structure consists of connected multilink planar linkages. • The system reconfiguration is based on the dual effective crank–slider concept. • Actuation is applied on the sliding member on an elevated midspan mechanism. • The study involves a photovoltaics integration in the building envelope.

Assessing the Potential of Solar PV Installation based on Urban Land Cover Analysis

This paper presents a technical assessment of solar photovoltaic (PV) installation potential in urban areas based on its urban land cover type, using a case study of Indonesian cities including Yogyakarta, Kupang and Tomohon. The assessment was performed using a free online application to assess the urban land cover types, i-Tree Canopy. This application can be used to identify and distinguish urban land cover types such as building rooftop, vegetation, grass, soil, road and water, which then can be used to assess the suitable area for Solar PV installation. Additionally, solar photovoltaic power output data from Global Solar Atlas is used to calculate potential energy production from PV installations in each city. The result shows that in an urbanised city such as Yogyakarta, the most suitable PV installation is in building rooftops. Meanwhile, Kupang and Tomohon have higher potential for ground-mounted PV installation in bare ground or grass. The approach and result of this study could be used for planners and policymakers to determine city-scale solar PV installation planning to maximise solar energy production. It can also be used to calculate the solar energy estimation using free online applications, which is easy to use and more accessible for stakeholders.