Solar Energy Output Research Articles

A review on geothermal energy resources in India: past and the present.

By 2040, India hopes to have completed its energy supply to fulfill the country's rising energy demands. Renewable and conventional sources must be used in an environmentally acceptable manner to achieve sustainable growth. India must enhance its use of renewable and clean energy sources, including geothermal, wind, and solar, to satisfy its growing demand. While solar and wind energy output has increased significantly, geothermal energy has yet to be fully harnessed. Among the many forms of geothermal energy found on the surface are volcanoes, fumaroles, erupting geysers, steaming fields, and hot springs. A total of about 340 geothermal springs may be found in India, spread throughout both orogenic (in the Himalayas) and non-orogenic (in the Peninsula). There were 31 places extensively evaluated, and deep drilling was performed in sixteen of them. Average temperatures range from 35.0°C to the boiling point of water in these springs. Medium (100.0-200.0°C) and low (100°C) enthalpy geothermal energy resources/systems are found in India, with the latter being the most abundant. The essential component of a geothermal system is understanding the heat source and harnessing it. Studies so far have indicated that some geothermal areas have sufficient geothermal potential for direct heat usage and power generation. If the Puga (J&K) field is explored to a depth of at least 500 m, reservoir simulation studies have shown that it might produce more than 3 MW of power. India's diverse geothermal sites and the current status of exploration for future research are discussed in the paper.

Analysis and study of the potential increase in energy output generated by prototype solar tracking, roof mounted solar panels.

Roof mounted solar panels come in form of fixed panels, unable to adjust to sun's position during day and throughout the year. As an effect, the efficiency of such solution is usually dependent on the roof slope and position of the building in relation to sun's day arc during seasons. These problems can be bypassed in free standing solar installations by equipping solar panels with solar tracker installations. Thanks to solar tracking, solar panels can be dynamically positioned perpendicular to the sun position and gather energy more efficiently throughout the day. This article presents a possibility of creating a roof mounted solar tracking panel to increase irradiance efficiency. A prototype of solar tracking panel with two axes of movement was designed with an intention of an easy adaptation to being mounted on sloped surfaces of building roofs. A reference stationary panel was used to compare the efficiency of both solutions. A 5-day study was carried out to determine if the proposed solution could provide any benefits. Based on the study, the authors made an attempt to draw a conclusion whether the design could considerably increase the solar energy output to be worth the extra spending associated with solar tracker installation.

Analysis and study of the potential increase in energy output generated by prototype solar tracking, roof mounted solar panels

Roof mounted solar panels come in form of fixed panels, unable to adjust to sun’s position during day and throughout the year. As an effect, the efficiency of such solution is usually dependent on the roof slope and position of the building in relation to sun’s day arc during seasons. These problems can be bypassed in free standing solar installations by equipping solar panels with solar tracker installations. Thanks to solar tracking, solar panels can be dynamically positioned perpendicular to the sun position and gather energy more efficiently throughout the day. This article presents a possibility of creating a roof mounted solar tracking panel to increase irradiance efficiency. A prototype of solar tracking panel with two axes of movement was designed with an intention of an easy adaptation to being mounted on sloped surfaces of building roofs. A reference stationary panel was used to compare the efficiency of both solutions. A 5-day study was carried out to determine if the proposed solution could provide any benefits. Based on the study, the authors made an attempt to draw a conclusion whether the design could considerably increase the solar energy output to be worth the extra spending associated with solar tracker installation.

Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction

At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies, known to have a random nature. Therefore, the main goal of this study is to investigate and exploit artificial intelligence's revolutionary potential for the prediction of the electricity generated by solar photovoltaic panels. The main algorithms that will be studied in this article are cubist regression, random forest and support vector regression. This forecast is beneficial to both providers and consumers, since it will enable for more efficient use of solar renewable energy supplies, which intermittency makes their integration into the existing electrical networks a challenging task.

Techno-Economic Analysis of a 12-kW Photovoltaic System Using an Efficient Multiple Linear Regression Model Prediction

Renewable energy sources are expected to replace traditional energy sources such as oil and gas in the future. It goes without saying that solar energy has been demonstrated to be a key source of green energy. Solar energy is used because it is abundant, pollution-free, and easily available. However, the power utility market requires highly exact solar energy forecasts. These challenges need the creation of a device that can precisely predict solar energy output via processing the location's weather data, which is accomplished through the use of machine learning and multiple linear regression (MLR). Some elements, such as the number of cloudy days, humidity, temperature, wind condition, and precipitation, should be addressed while simulating solar power output. In this paper, a 12-kW photovoltaic (PV) system on the rooftop of a house in Isfahan was studied using the System Advisor Model (SAM). The most significant research contribution of the proposed paper is to predict the output power of a solar system with the lowest possible error. According to the simulation results, by using the MLR model, the predicted power has an error of 6 % with the actual power, which is a very good estimation. In addition, this system meets each household's energy needs plus an additional 8430 kWh per year, resulting in being paid by utility companies, a fewer number of outages, and lower air pollution levels.

Solar Energy Potential on Surfaces with Various Inclination Modes in Saudi Arabia: Performance of an Isotropic and an Anisotropic Model

The present work investigated the performance of an isotropic (Liu–Jordan, L–J) and an anisotropic (Hay) model in assessing the solar energy potential of Saudi Arabia. Three types of solar collectors were considered: with southward fixed-tilt (mode (i)), with fixed-tilt tracking the Sun (mode (ii)), and with varying-tilt tracking the Sun (mode (iii)). This was the first time such a study was conducted for Saudi Arabia. The average annual difference between anisotropic (Hay) and isotropic (L–J) estimates is least ≈38 kWhm−2 year−1 over Saudi Arabia for mode (i), and therefore, the L–J model can be used effectively. In modes (ii) and (iii), the difference is greater (≈197 and ≈226 kWhm−2 year−1, respectively). It is, then, up to the solar energy engineer to decide which model is to be used, but it is recommended that the Hay model be utilised for mode-(iii) solar collectors. These results fill a research gap about the suitability of models in practice. An interesting feature for the ratio of the annual mean solar energy yield of Hay over L–J as function of the latitude, φ, and the ground albedo, ρr, is the formation of a “well” for 29° ≤ φ ≤ 31° and 1.15 ≤ ρr ≤ 1.

Rancang Bangun Rombong Listrik Dengan Menggunakan Pembangkit Listrik Tenaga Surya (PLTS) Kapasitas 200 Watt

The solar energy that reaches the earth in one hour is sufficient to meet the energy needs of all humans on the earth's surface in one year, reason enough to spur the maximization of solar energy like renewable energy, especially by converting it into electricity by cell devices/solar panels. To get the maximum output of solar cell panel energy by optimizing the direction of the solar cell panels to the sun. The solution is to design a tracking system that adjusts the position of the solar cell panels automatically following the sun based on Arduino Uno R3. The method used is to compare the two solar cell panels using the Arduino Uno R3. tracking system and solar cell panels without a tracking system. From the two measurement results in the morning to afternoon, it was found that the panel using the tracking system had a total power output of 104.3364 Watt and the panel without a tracking system had a total power output of 43.05094 Watt, so from the results of the most optimal measurement, the panel using the tracking system. It can be seen in measurements from 09.30 WIB to 16.30 WIB solar cell panels using a tracking system produce greater energy output than solar cell panels without a tracking system, an increase in the power output compared to panels.

The Linear Fitting Method for Model of PIN Array Receiver in Space Optical Communication

We have proposed PIN array as receiver in space optical communication and adopted the double diode model, the widely accepted solar energy output theory, to study its characteristics of output current and voltage. In order to make the calculation simplified, the linear fitting method is put forward. Later, we adopt both the traditional method Newton-Raphson and the new linear fitting method to calculate the values of both maximum power and the corresponding voltage of 3 × 3 PIN array. The comparison result shows the calculated values with two methods are good consistent. It validates the feasibility of the new linear fitting method. In the next step, the experiment has been carried out. The experimental result validates the reasonability of adopting the double diode model to study PIN array. At the same time, it validates the feasibility of the new linear fitting method again.

Impact of climate change on solar monofacial and bifacial Photovoltaics (PV) potential in Qatar

The considerable growth of the renewables’ shares in the energy market paves the way towards United Nations Sustainable Development Goal 7. Solar photovoltaics (PV) are more economically viable amongst other renewables because of their lower, easier installation and reliability. However, the PV, like other renewables, is more vulnerable to climatic conditions. Currently, the monofacial leads the PV market; however, it is expected that the bifacial PV will be dominant as it offers more energy yield at a lower cost. It is thus crucial to analyze the PV performance concerning climatic conditions. The merits of this study include: (i) the performance of bifacial PV for the climate change scenarios is analyzed, which is lacking in the literature; (ii) detailed monofacial and bifacial PV mathematical models are developed to estimate the PV output; and (iii) the comparison is made between monofacial and bifacial PVs performance and their dependence on climatic conditions. The climate model is used to predict the ambient conditions for the years 2050 and 2080. The average hourly responses of solar irradiance and energy yield are presented and analyzed.

Solar Energy Prediction Using Particle Swarm Optimiser Trained Recurrent Neural Network

In recent years, renewable energies have been covering an increasing part of the worldwide electrical power demand. The amount of solar radiation received by the earth’s surface is very important to numerous renewable energy applications. However, direct measurement of solar data is not widely available, especially in developing countries such as Nigeria. Numerous statistical methods have been developed and used in the areas of solarenergy prediction. Statistical approaches such as; Auto Regressive Moving Average (ARIMA), and linear regression (LR) are used. The challenges of using these statistical approaches are Linear methods (works best when the data is linear), high mathematical complexity and dependence on prior knowledge of how the time series was generated. In the meantime, data and computational poweravailability have enabled machine learning algorithms to perform improved predictions. The need to predict solar energy output is essential; machine learning models can be employed. In this work, Particle Swarm Optimization (PSO) and Back propagation (BP) algorithms were used to train a Recurrent Neural Network (RNN) to model monthly mean daily solar radiation values. The inputs to the network are month, sunshine duration, latitude, longitude, and altitude of the location. The BP algorithm is compared with the PSO algorithm trained by the RNN. The PSO outperformed the BP in most cases with average accuracy for the BP at about 0.71, 0.725, 0.76, 0.735 and 0.78, 0.8, 0.84, 0.815 for the PSO algorithm for the four (4) datasets used.

Design of wind and solar energy supply, to match energy demand

Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively E w / E s = 1.7 and P w / P s = 0.6 . The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40° slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40° slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90°) and 60° slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.

Dust cycle and soiling issues affecting solar energy reductions in Australia using multiple datasets

Dust plays a vital role in the Earth’s climate system and can also modulate solar energy yield from photovoltaics due to soiling and it also impacts concentrated solar power outputs by up to 30%. Research examining decreased energy production on solar panels due to dust accumulation is limited. This is particularly important for dry arid regions of Australia, which are prone to dust storms. Thus, this paper examines the dust characteristics over Australia by comparing two reanalysis products, the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS) with the satellite data (Himawari-8) to ascertain the capabilities of the reanalysis products in capturing dust cycles over Australia. The results are then used to explore the seasonal variability and trends in the radiative impacts, emissions, and depositions of dust. The energy reductions from seasonal averaged dust accumulations without natural removal or cleaning and later the impact of soiling with removal from precipitation bursts on selected operational solar farms are presented. Results show that MERRA-2 outperforms CAMS in capturing Dust Aerosol Optical Depth (AOD) near the Lake Eyre Basin in all seasons, consistent with satellite observations. The radiative trends showed contrasting differences during the Spring and Summer, whereas seasonal dust emissions and depositions peaked at 1.4 gm−2. Similarly, seasonal dust accumulations without natural removal on panels showed significant reductions in the energy of up to 3% around the Lake Eyre Basin, but most operational solar farms in Australia depicted energy reductions of 8–9% due to accumulated dust of up to 2.5–3 gm−2, which was naturally removed from regular seasonal rainfall bursts.

An impact of solar PV specifications on module peak power and number of modules: A case study of a five-bedroom residential duplex

This paper presents a case study using an analysis of solar PV modules peak power to determine the optimum number of PV modules required to supply energy to certain typical household appliances. The approach could be used for selection of solar PV modules that produce a satisfactory energy yield to meet pre-determined energy needs. In the analysis, the maximum daily energy consumption values corresponding to the lighting and air-conditioning loads were determined through an energy audit conducted on a residential building unit in Lagos, Nigeria. The maximum daily energy consumption values of these appliances were programmed into a NCEEC_e-EASZ VBA simulation tool to determine the solar PV array peak power and the number of modules required to produce the required solar energy yield. The methodology was validated with literature and HOMER Pro software. Eighteen different commercially available modules spanning a range of specifications including unit peak power varying from 280 to 400 W were investigated in order to select the most suitable panels for the provided conditions. The minimum required number of PV modules required to make up an array to produce the required yield was determined. The result indicates that when the unit peak power of the modules increases, the number of modules required to meet the daily energy demand reduces. The rating of solar PV modules between 280 - 400 W constituted a significant role in the process of analysing peak power as well as the quantity of solar PV modules required.

Theoretical concentration limit and maximum annual optical efficiency of static/low-concentration CPV for horizontal integration to vehicle bodies.

This study proposes an extended theoretical formula to characterize the relationship between the geometrical concentration ratio Cg and arbitrary incidence-angle range to objectively understand the theoretical limit performance of vehicle-integrated concentrator photovoltaics (VICPVs), i.e., static and low-concentration CPVs installed on a horizontal surface. Theoretical analysis revealed that the maximum annual optical efficiency (maximum annual solar energy yield) can be achieved by selectively collecting the sunlight incident only from a specific incidence-angle range of θ1-θ2 (θ1 ≠ 0°), regardless of the geographical location, which is associated with the angular distribution of the annual solar energy on the concentrator aperture. Moreover, the results of the solar concentrator design based on the obtained formula clarified that the annual optical efficiencies of the optical systems with aspheric lens or dielectric-filled crossed compound-parabolic-concentrator lens were 64-89% of the theoretical limit depending on the location for Cg = 3.5×. Nevertheless, the concentrator design can be further improved for practical applications.

Performance prediction by modelling 5KW solar penetration in power system

The research work simulated here consists of predicting the real time execution of solar penetration with inclusive of all climatic variations. Basic layout and scheme of the installed set up is identified to create a model of execution so that future research work could be practiced. Routine execution assessment along with working parameters are collected and analysed to predict the future scopes. As per the visible day availability solar penetration in the grid is estimated for the inspected site located at Jaipur (Raj.), India. Consumer demand is handled for approx. 10–12 h daily in sunny hours; this cycle is followed throughout the year. The data for solar energy output is collected to obtain its performance, for the working hours through internet of things-based sensor mobile technology on an android application and web-based software. Variation in performance due to vivid insolation, temperature as per seasonal time (winter or summer), quality of panel along with amount of sand or dirt collected over the panel sheet are the basic reasons based on which solar generation varies. As per standards are concern a reduction rate of 0.04% annually is predicted over 25-degree temperature scale. Exclusion of losses the total power obtains is concerned as performance scale in percentage rather counting the layout of panel and solar pattern. Studies analyses here obtains data of 2021–22 financial year through client based smart application. Also summed energy output from per kilo watt-wise panel installed is considered to determine performance of solar penetration or it can be said as definite gain is obtained as per the installed assembly.

Flexible Active Power Control Strategy for Photovoltaic System Based on Current Limiting Control Method

India is country with abundant solar energy availability. The annual solar energy output exceeds the total energy output of India's non renewable energy sources. As increasing installation of renewable energy sources into the grid. The fluctuations of power based on operating climate conditions like solar insulation and temperature is highly depends as it is not possible to limit such installations with time the penetration level of renewable sources will increase to meet demand with green energy. This paper proposed a flexibly power point tracking (FPPT) control of active power in photovoltaic system to achieve reserve capacity with Power Limiting Control (PLC) which will provide high stability to existing system without overloading it thus a proper integration to the grid and to mitigate adverse negative effects of high level integrations are possible with modified grid codes in stand of replacement of existing grid . Matlab/Simulink software package is used to make the model and effectiveness of the propose system is tested with Simulink environment..

Development and evaluation of empirical models for the estimation of hourly horizontal diffuse solar irradiance in the United Kingdom

Solar irradiation data is required in many applications to obtain the solar energy output. However, solar irradiation data is not always available, especially horizontal surface diffuse solar irradiation. This has led to the creation of empirical solar models to predict these values. The aim of this paper is to develop an empirical model for the estimation of hourly diffuse solar irradiance on horizontal surfaces. Initially, a set of existing empirical models have been evaluated to test their accuracy. New models have been developed based on ground level measurements of global and diffuse horizontal solar irradiation that have been obtained from three different regions of the United Kingdom: South Yorkshire, Norfolk, and West Sussex for a period between 1982 and 1999. The models created for each region correlate the diffuse fraction (kd) and the clearness index (kt), for the estimation of hourly diffuse solar irradiance on horizontal surfaces. The models developed for the three regions were evaluated to test their accuracy using error histograms and by contrasting different datasets. Finally, a comparison of the new models to the existing empirical models showed that the new developed correlations significantly improved the existing empirical models for the three regions evaluated in this study.

Ultrathin Cu(In,Ga)Se2 transparent photovoltaics: an alternative to conventional solar energy-harvesting windows

Transparent photovoltaic (TPV) devices using very thin absorbers, which are sandwiched by transparent conducting front and rear contacts, provide efficient solar energy yields and partially visible transparency. These characteristics allow such devices to be used in applications including building-integrated photovoltaics (BIPV), solar vehicles, and wearable devices. In this study, we developed the TPVs with tunable power conversion efficiencies (PCEs) and average visible transmittance (AVT) values using ultrathin Cu(In,Ga)Se2 (CIGS) absorber layers with thicknesses ranging from 30 to 300 nm, prepared using a single-stage coevaporation process. The PCE and AVT values of these devices could be easily tuned by controlling the absorber thickness; the TPV devices exhibited respective PCEs ranging from 11.0% to 2.1% (front illumination) and 6.6–1.7% (rear illumination), as well as AVTs ranging from 9.1% to 47.8% as the absorber thickness was varied from 300 to 30 nm. The TPV device prepared with a 100 nm-thick fully depleted CIGS absorber layer exhibited an enhanced bifaciality factor close to unity owing to the field-driven charge transport and reduced recombination at the rear contact and/or in the bulk of this device. Similar PCEs of ~5% under front and the rear illumination at an AVT of ~35% were achieved. This TPV device demonstrated constant PCEs under low-light irradiance (10–100 mW cm−2) irrespective of the illumination direction, as well as an enhanced bifacial performance as JSC linearly increased from 11.4 to 19.3 mA cm−2 as the rear illumination intensity was increased from 0 to 60 mW cm−2. It also exhibited high light-soaking stability.

A simplified evaluation method of rooftop solar energy potential based on image semantic segmentation of urban streetscapes

The evaluation of rooftop solar energy potential in cities has a fundamental role in the development and utilization of solar energy. The irradiance-based approach of evaluation is a repetitive calculation process combined with the complex geometry of urban forms. This research paper proposes a framework that utilizes urban streetscapes to quickly estimate the urban solar energy potential using a simplified solar energy yield model based on two indicators: sky view factor (SVF) and sun coverage factor (SCF). First, 327 Google Street View images were converted into fisheye images, which were semantically segmented based on the deep-learning Full Convolutional Network (FCN). Then, the SVF and SCF were calculated based on the method of pixel statistics. Finally, a model with the simulated solar energy yield was established. Furthermore, a validation study was conducted using rooftop solar production data, which showed that the maximum estimation error of the proposed model was less than 8% and the average relative error was 6.6%. Generally, the proposed model reduced the required computation time while preserving a satisfactory degree of accuracy.

Risk and cost benchmarking of solar energy technology based on celestial phenomenon for a power market integration

Intermittency associated with the renewable energy institutes various uncertainty with respect to power market perspective. To overcome the uncertainties associated with the renewable energy source a novel benchmarking approach is proposed. The benchmarking is done in order to compare the economical perspective of various energy sources. The celestial events considered for benchmarking are Aphelion, Perihelion, and solstice (directly affects solar irradiance). The proposed method can determine the harvest quality of solar farming based on profit returns. The solar energy output data provided by the National Institute of Wind Energy (NIWE) has is used to find out the solar energy output during Aphelion, Perihelion, and solstices. For benchmarking of an area, Levelized Cost of Electricity (LCOE), Conditional Value at Risk (CVaR) and carbon pricing index, have been considered. Levelized Cost of Electricity (LCOE) is used to determine the cost of solar energy produced during its lifetime, and CVaR is used to assess the tail-end risk for the profit return. Carbon pricing is benchmarked to understand the environmental impact of the energy sources. The duration chosen is 10.00–14.00 h which is the time of the peak solar irradiances considering a solar thermal and PV system. IEEE 5 and 14 bus systems are chosen for the case study. Based on the results obtained, market condition influences the LCOE. The LCOE is lower for stable market returns and higher for an unstable market. On comparing the benchmark scores considering the best case scenario, solar thermal is having 59% higher LCOE, 5.2 times higher carbon price index and 42% higher CVaR than PV system. Thus the proposed approach will help market participant in decision making related to the profit and risk associated with the renewable energy.