Boosting solar energy generation through recycling: Synthesis, characterization and simulation of a ceramic-based diffuse reflector

Solar energy penetration levels have been increasing steadily in recent years, becoming a significant factor in the energy system in the United States and the world. The increase in photovoltaic generation leads to a potential increase in grid disturbances. This study analyzes the relationship between solar energy generation and error (difference between the demand and the forecast energy demand) in seven subregions of the United States. Three types of errors were calculated mean error (ME), mean absolute error (MAE) and mean squared error (MSE). Correlation analysis was performed between solar energy generation and the three types of errors. Furthermore, graphical comparisons were made between the percentage of energy generated and the three types of errors for each of the seven subregions. As a result, negative correlations were found between the generation of solar energy and ME in five analyzed subregions. When analyzing the correlation between solar energy generation with MAE and MSE, a correlation was found in all the subregions. Besides, ME, MAE, and MSE values decrease in the subregions with the highest solar energy generation percentage. The results indicate that the generation of solar energy impacts the demand forecast. Also, Balancing Authorities (BAs) with a high percentage of solar energy consider the solar generation's effects on demand forecasting. Due to these results, even BAs with low solar generation should consider solar energy as a relevant factor to improve the demand forecast accuracy. Additionally, it is necessary to incorporate methodological standards across all BAs that consider solar generation effects in demand forecasting.

Generation of solar energy has tremendous scope in India. The geographical location of the country stands to its benefit for generating solar energy. The reason being India is a tropical country and it receives solar radiation almost throughout the year, which amounts to 3,000 hours of sunshine. This is equal to more than 5,000 trillion kWh. Almost, all parts of India receive 4-7 kWh of solar radiation per sq metres. This is equivalent to 2,300–3,200 sunshine hours per year. States like Andhra Pradesh, Bihar, Gujarat, Haryana, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, and West Bengal have great potential for tapping solar energy due to their location. Since majority of the population live in rural areas, there is much scope for solar energy being promoted in these areas. Use of solar energy can reduce the use of firewood and dung cakes by rural household. Many large projects have been proposed in India, some of them are: i).Thar Desert of India has best solar power projects, estimated to generate 700 to 2,100 GW, ii). The Jawaharlal Nehru National Solar Mission (JNNSM) launched by the Centre is targeting 20,000 MW of solar energy power by 2022, iii).Gujarat’s pioneering solar power policy aims at 1,000 MW of solar energy generation, and Rs. 130 billion solar power plan was unveiled in July 2009, which projected to produce 20 GW of solar power by 2020. Apart from above, about 66 MW is installed for various applications in the rural area, amounting to be used in solar lanterns, street lighting systems and solar water pumps, etc. Thus, India has massive plan for Solar Energy generation that may not only fulfill the deficit of power generation but also contribute largely in Green Energy Production to help to reduce the Climatic Changes globally.

This paper presents a development of low-cost intelligence tracking system for Solar (PV) cell energy generation. As well known, the electrical energy as power source is used in several electrical devices and applications. However, the conventional electrical energy has produced from fuels that makes it costly and increasing the pollution level around the world. Solar energy generation has gained significantly attention due to its properties of providing clean energy and replenishing the electrical energy. Nevertheless, the solar energy still considered as high cost technology and produced losses in the solar cell power. Therefore, the proposed low-cost intelligence tracking system is aimed to adjust the PV cell direction toward a sunlight with minimizing the losses in the solar energy generation. The proposed intelligence tracking system is modelled based on PSO algorithm and designed using MATLAB and SIMULINK Software. The PSO Algorithm is useful in utilizing and managing the neural networks to steer the directions and speed of intelligence tracking system. Then the proposed model is implemented on low cost FPGA board circuit. The performance of the proposed model is analysed using MATLAB software. This proposed model would benefit the solar energy applications.

It is well known that the rampant increase for the demand of electricity and rapid depletion of the fossil fuels has called for immediate response in the direction of energy sufficiency. To accomplish this, one of the important tasks is to identify the locations of high potential for renewable energy generation. It is a well-established fact that solar energy proved to be the most sought after source for energy generation. Although, solar energy potential maps of India have been prepared based on solar irradiation maps in the earlier studies, the present research study has been carried out with a focused attention directly on solar energy generation considering various parameters. In this work it is shown that solar energy generation does not depend on solar radiation alone at a location. Instead, there are various other factors that influence the energy generation. Some of them are ambient temperature, wind velocity and other parameters like weather and topographic conditions. In this study the locations with high and low solar energy generation potential in India have been identified through systematic analysis by computing the solar energy parameters at every grid point (1° × 1°). The work has been extended with more detailed study for Gujarat, Andhra Pradesh and the newly formed Telangana states. The data points considered for the states are 0.25° × 0.25° having resulted in adding more number of locations. Our results indicate that the total annual energy generation in India varies from 510,000 KWH to 800,000 KWH per acre of land. The least energy generation location pertains to the eastern parts of Arunachal Pradesh and eastern part of Assam and the highest annual solar energy generation has been identified in the eastern parts of Jammu & Kashmir and eastern part of Uttarakhand.

Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy generation technologies remains limited. This paper proposes a multi-criteria decision support system (MCDSS) for selecting the most suitable PV set (comprising PV modules, inverters, and batteries) for microgrid installations. The MCDSS employs two multi-criteria decision-making methods (MCDM) for analysis and decision-making: AHP and TOPSIS. The system was tested in two case studies: Barreiras, with a global efficiency of 14.4% and an internal rate of return (IRR) of 56.0%, and Curitiba, with a worldwide efficiency of 14.8% and an IRR of 52.0%. The research provided a framework for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. This research highlights the potential of MCDSS to improve the efficiency and accessibility of solar energy generation.

Multi-criteria decision making for different concentrated solar thermal power technologies

Despite its popularity, the nature of solar energy is highly uncertain and\nweather dependent, affecting the business viability and investment of solar\nenergy generation, especially for household users. To stabilize the income from\nsolar energy generation, there have been limited traditional options, such as\nusing energy storage to pool excessive solar energy in off-peak periods or\nfinancial derivatives from future markets to hedge energy prices. In this\npaper, we explore a novel idea of "parametric solar energy insurance", by which\nsolar panel owners can insure their solar energy generation based on a\nverifiable geographically specific index (surface solar irradiation).\nParametric solar energy insurance offers opportunities of financial subsidies\nfor insufficient solar energy generation and amortizes the fluctuations of\nrenewable energy generation geographically. Furthermore, we propose to leverage\nblockchain and remote sensing (satellite imagery) to provide a publicly\nverifiable platform for solar energy insurance, which not only automates the\nunderwriting and claims of a solar energy insurance policy, but also improves\nits accountability and transparency. We utilize the state-of-the-art succinct\nzero-knowledge proofs (zk-SNARK) to realize privacy-preserving blockchain-based\nsolar energy insurance on real-world permissionless blockchain platform\nEthereum.\n

Solar energy strategies in the U.S. utility market

Solar generation has increased rapidly worldwide in recent years and it is projected to continue to grow exponentially. A problem exists in that the increase in solar energy generation will increase the probability of grid disturbances. This study focuses on analyzing the grid disturbances caused by the massive integration to the transmission line of utility-scale solar energy loaded to the balancing authority high-voltage transmission lines in four regions of the United States electrical system: (1) California, (2) Southwest, (3) New England, and (4) New York. Statistical analysis of equality of means was carried out to detect changes in the energy balance and peak power. Results show that when comparing the difference between hourly net generation and demand, energy imbalance occurs in the regions with the highest solar generation: California and Southwest. No significant difference was found in any of the four regions in relation to the energy peaks. The results imply that regions with greater utility-level solar energy adoption must conduct greater energy exchanges with other regions to reduce potential disturbances to the grid. It is essential to bear in mind that as the installed solar generation capacity increases, the potential energy imbalances created in the grid increase.

In this study, an artificial neural network (ANN) based approach is studied about the prediction of solar energy generation in a microgrid using weather forecasting. The ANN is trained using historical data of solar energy generation and weather forecast data. The input parameters for the ANN include weather variables such as temperature, humidity, wind speed, and solar irradiance. The output parameter is the solar energy generation in kilowatt-hour (kWh). The proposed approach is implemented and tested using real-world data from a microgrid. The results indicate that the ANN-based approach is effective in predicting the solar energy generation with high accuracy. The proposed approach can be used for optimizing the operation of microgrids and facilitating the integration of renewable energy sources into the power grid. This study proposes the use of an Artificial Neural Network (ANN) to predict the solar energy generation in a microgrid using weather forecast data. Weather forecasting has become more precise and dynamic with the integration of IoT data with advanced analytics and machine learning models. These models are quite accurate at predicting solar irradiance and analyzing patterns. The microgrid comprises of a photovoltaic (PV) system which generates solar energy and a battery storage system which stores and supplies the energy to the load. Accurate prediction of solar energy generation is crucial for optimizing management of the microgrid. The inputs to the ANN model include temperature, humidity, wind speed, cloud cover and solar irradiance, which are obtained from weather forecast data. The output of the model is the predicted solar energy generation. The performance of the ANN model is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). This study presents a practical approach for predicting solar energy generation in a microgrid using weather forecast data, which can be used for efficient management of the microgrid.

Qatar has a high potential to develop renewable energy generating systems spatially through solar and wind-based technologies. Although, substantial initiatives have been undertaken in Qatar to reduce the high per capita emissions of the Greenhouse Gases (GHG), solar and wind-based energy generation can also significantly contribute to the mitigation of climate change. The mean Direct Normal Irradiance (DNI) of Qatar is about 2008 kWh/m2/y, which is suitable to develop solar power systems, knowing that 1800 kWh/m2/y is enough to establish Concentrated Solar Power (CSP) plants. Although, the cost factor for developing the solar based power generation systems is about twice the gas based power generation, it generates environmental friendly energy along with keeping the limited gas resources. Moreover, being aware that 3 m/s is the critical wind speed to generate power, Qatar experiences wind speed over the critical speed in almost 80% of time that is a great potential to develop wind-based energy systems. In terms of economic feasibility, the minimum requirement of number for full load hours is 1400 while the number for Qatar is higher than the critical value. Furthermore, establishing wind power plant is cheaper than the gas-based one in off-shore locations even though the power generation is lower. This paper explains a methodology to determine the most suitable sites for developing the solar and wind-based power plants in order to maximize the efficiency of power generation using remote sensing and GIS. Analyses are carried out on two sets of spatial data derived from a recent Landsat 8 image such as land cover, urban and built-up areas, roads, water sources, and constraints, along with bands 10 and 11 (thermal bands) of same sensor for the year 2014, a DEM (Digital Elevation Model) derived from SRTM V2 (Shuttle Radar Topography Mission) to generate slope, aspect, and solar maps, and wind data obtained from Qatar meteorology department. The data are used to conduct two parallel Multi-Criteria Evaluation (MCE) techniques based on each objective of development (solar, and wind power plant development) through the following stages: (1) data preparation and standardization using categorical data rescaling, and fuzzy set membership function, (2) Logistic Regression-based analysis to determine suitability of each pixel for desired objective of development. The analysis produces two distinct suitability maps such that each one addresses suitable areas to establish solar, and wind power plants. The obtained suitability maps then are processed under a multi-objective land allocation model to allocate the areas that show the highest potential to develop both solar and wind-based power generation. Results show that the off-shore suitable sites for both objectives are mainly distributed in the north and north-west regions of Qatar.

Preface

Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods

We exploit changes in air quality seen during the COVID-19 lockdown over China to show how a cleaner atmosphere has notable co-benefits for solar concentrator photovoltaic energy generation. We use satellite observations and analyses of the atmospheric state to simulate surface broadband and spectrally resolved direct normal irradiance (DNI). Over Wuhan, the first city placed under lockdown, we show how the atmospheric changes not only lead to a 19.8% increase in broadband DNI but also induce a significant blue-shift in the DNI spectrum. Feeding these changes into a solar cell simulator results in a 29.7% increase in the power output for a typical triple-junction photovoltaic cell, with around one-third of the increase arising from enhanced cell efficiency due to improved spectral matching. Our estimates imply that these increases in power and cell efficiency would have been realised over many parts of China during the lockdown period. This study thus demonstrates how a cleaner atmosphere may enable more efficient large scale solar energy generation. We conclude by setting our results in the context of future climate change mitigation and air pollution policies.

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system