Solar Power Generation Research Articles

Solar Power Generation in Bangladesh: Status, Challenges and Measures

Efforts toward generation of electricity from solar source by various application technologies is now emerging in Bangladesh. To date, almost 3% of total electricity generation comes from various renewable resources, where 2% is contributed by solar power. Since inception of solar practice in mid 90’s, a collaborating effort by government and private organizations successfully set up a strong foundation of solar culture in the country being financially stimulated by the world donor agencies. In fact, the expectation of renewable sharing where solar is the major contributor is much higher in terms of near extinction of country’s fossil fuel, fore-coming energy demand and environmental concern. The status of current solar power production, existing challenges to be commensurate with national vision as well as global solar trend, and potential measures have been focused and analyzed in this review paper based on reported data and information. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 80-89

Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

Using Analytical Hierarchy Process to Select the Best Power Generation Technology in Libya

Selection of an appropriate power generation technology is a complex process. Because it is necessary to consider and take into account a wide range of parameters. The investment in the power generation field is too costly while resources are limited. This paper presents the determination of these parameters, which influence the selection of power generation technology in particular for developing countries. This paper answers the question of the best suitable technology for Libya because the investment in this field is so costly and making a wrong decision is not allowed, otherwise it will consume the big margin of the initial budget. Four main criteria are included, that is economic, environmental, technical and social. A sub-criteria are also included to support this decision. The performances of six technologies of a power generation are evaluated by Analytic Hierarchy Process (AHP) using expert choice software and the best technology is selected. The results showed that the solar photovoltaic power generation is the best technology. By means of the sensitivity analysis, it was found that the best alternative is not sensitive to a change in the weights estimated by the AHP.

Using Analytical Hierarchy Process to Select the Best Power Generation Technology in Libya

Selection of an appropriate power generation technology is a complex process. Because it is necessary to consider and take into account a wide range of parameters. The investment in the power generation field is too costly while resources are limited. This paper presents the determination of these parameters, which influence the selection of power generation technology in particular for developing countries. This paper answers the question of the best suitable technology for Libya because the investment in this field is so costly and making a wrong decision is not allowed, otherwise it will consume the big margin of the initial budget. Four main criteria are included, that is economic, environmental, technical and social. A sub-criteria are also included to support this decision. The performances of six technologies of a power generation are evaluated by Analytic Hierarchy Process (AHP) using expert choice software and the best technology is selected. The results showed that the solar photovoltaic power generation is the best technology. By means of the sensitivity analysis, it was found that the best alternative is not sensitive to a change in the weights estimated by the AHP.

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. In addition, an error compensation mechanism that uses an error node to capture the temporal fluctuation caused by system degradation or failures is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The results show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92 %–95 % from 1 h to one week ahead forecasting.

Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.

Solar photovoltaic power forecasting system with online manner based on adaptive mode decomposition and multi-objective optimization

Constructing an accurate and reliable solar photovoltaic (PV) power forecasting system is crucial for smart grid management and dispatch. However, due to the intermittent, non-stationary and random nature of solar energy, current methods cannot effectively capture the dynamic change patterns of PV data, resulting in a forecasting accuracy that cannot satisfy the requirement for stable operation of smart grids. To address this issue, in this paper, a new solar PV power forecasting system is proposed based on a new adaptive mode decomposition method that combines machine learning models. First an adaptive mode decomposition method is proposed for adaptively eliminating high-frequency information of PV data, mitigating the impact of high noise on the forecasting performance. Then the powerful mapping ability of machine learning model is utilized to construct a solar PV power generation combination forecasting system. The system adopts a multi-objective optimization strategy to determine the optimal weights of the combination model, which reduces the volatility of the forecasting results and enhances the stability of the forecasting system. Experimental results demonstrate that the proposed system achieves the best overall performance with online manner on 10 state-of-the-art baselines. Furthermore, the evaluation analysis by Diebold–Mariano test, improvement ratio and sensitivity analysis further show that the stability and accuracy of the proposed system outperforms the comparative baselines.

Analysis of consumer preferences for community solar programs using choice experiment

Many countries are actively promoting a community solar program, which is a community energy program centered on solar photovoltaic power generation, as a public participation-type energy policy. In this study, we analyze the preferences of potential participants in community solar programs from the perspective of policymakers to identify public acceptance and draw policy implications. As a methodology, the choice experiment with a stated preference method is used to analyze the preferences on the individual attributes of community solar energy in terms of business model targeting Korean who are the potential participants. Furthermore, we observed the homogeneity of preferences within each group and the heterogeneity of preferences between groups by dividing the groups based on the latent class model estimation. The results of this study show that the attributes of the community solar program, such as the operating agent, contract period, expected return, and participatory management, have a significant impact on the acceptance of potential consumers, and that there are clear differences between potential classes. The results can be used as basic data for the community energy projects and can contribute to designing succecesful energy policies not only for Korea but also for governments of other countries pursuing carbon neutrality.

Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

Forecasting solar power generation (SPG) is vital for the development and planning of power systems, offering significant benefits in terms of technical performance and financial efficiency. It enhances system reliability, safety, stability and it reduces the operational costs. This paper's primary goal is to develop models that can precisely forecast solar power generation by analyzing real first-hand dataset of solar power. The value of these forecasting models lies in their ability to anticipate future solar power generation, thus optimizing resource use and minimizing expenses. To achieve this, the study utilizes various classical machine learning, deep learning, and hybrid machine learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Random Forest (RF), Support Vector Regression (SVR), Bi-directional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN). Among these, the hybrid model combining CNN-LSTM-RF demonstrated superior accuracy with R-squared of 92 %, a Root Mean Square Error (RMSE) of 0.07 kW, and a Mean Absolute Error (MAE) of 0.05 kW. This indicates that the hybrid machine learning model combining of CNN-LSTM-RF is effective in forecasting solar power generation.

Optimizing Solar Power Generation in Urban Industrial Blocks: The Impact of Block Typology and PV Material Performance

The block-scale application of photovoltaic technology in cities is becoming a viable solution for renewable energy utilization. The rapid urbanization process has provided urban buildings with a colossal development potential for solar energy in China, especially in industrial areas that provide more space for the integration of PV equipment. In developing solar energy resources, the block layout and the PV materials are two critical factors affecting the distribution of solar radiation and generation. However, few studies have analyzed how to select the most suitable PV materials for different layouts of industrial blocks to obtain the best generation. This study considered the layout of industrial blocks and PV materials simultaneously, and the generation yield was calculated when combined. A total of 40 real industrial block cases were constructed, and radiation distribution data on building surfaces of different block cases were calculated. Data on both were combined to calculate the generation of different PV materials for each block type. The findings indicated that single-story industrial blocks possessed the highest potential for solar radiation, primarily due to the higher percentage of roof area. The influence of PV materials on the installation rate of different building facades varied, with the installation rate of the west facade being the most impacted by PV performance and the roof being the least impacted. Using different PV materials in industrial blocks could lead to a 59.2% difference in solar generation capacity. For single-layer industrial blocks, mono crystalline and poly crystalline silicon were preferable to achieve higher power generation. In contrast, multi-story and high-rise industrial blocks were best suited for a-Si and CIGS to attain higher cost performance. The methods and results of this study guided the selection and installation of PV equipment in various block typologies, thereby improving the refinement of solar resource development, maximizing solar resource utilization, and promoting the development of energy conservation and carbon reduction in cities.

Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan.

Households’ preference and willingness to pay for alternative energy sources: a discrete choice experiment

As consumers play an increasingly active role in the energy market, understanding their preferences for renewable and non-renewable energy is essential for achieving Sustainable Development Goal 7. This study employs a labelled discrete choice experiment to investigate consumers' preferences and willingness to pay for solar PV panels, power generators, and biomass, considering service provider, service quality, and purchasing price. The survey was administered to 250 households in Kumasi, Ghana. This study finds that solar PV panels are the most preferred energy source, with the highest willingness to pay estimate. However, in cases where solar panels are not easily accessible, households turn to biomass as an alternative. Although there are similarities in choices, variations in preferences among consumers were identified. Furthermore, consumers value product or service quality but remain indifferent between foreign and domestic service providers. Based on these findings, policymakers are advised to engage in awareness campaigns and provide incentives such as subsidies and low-interest loans, to drive solar PV panel adoption among households. Energy developers should consider customized payment plans based on income levels to facilitate affordability. Additionally, recognizing the heterogeneity in preferences necessitates an inclusive policy approach that considers diverse consumer needs and addresses the energy access challenges faced by low-income households.

Stable Radicals in Dihydrophenazine Derivatives-Doped Epoxy Resin for High Photothermal Conversion.

Organic radicals exhibit great potential in photothermal applications, however, their innate high reactivity with oxygen renders the preparation of stable organic radicals highly challenging. In this work, a series of co-doped radical polymers ares prepared by doping dihydrophenazine derivatives (DPPs) into the epoxy resin matrix. DPPs can form radical species through the electron transfer process, which are further stabilized by the complex 3D network structure of epoxy resin. Experimental results show that the photothermal conversion efficiency is as high as 79.9%, and the temperature can quickly rise to ≈130°C within 60 s. Due to the excellent visible light transmittance and mechanical properties of co-doped systems, this study further demonstrates their practical applications in energy-saving solar windows and thermoelectric power generation.

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

Performance Optimization and Techno-Economic Analysis of an Organic Rankine Cycle Powered by Solar Energy

Abstract To improve the performance of traditional solar power generation systems, a new solar organic Rankine cycle system that can generate electricity and heat is proposed. The system incorporates the separation-flash process, regenerator, and ejector to enhance its efficiency. The optimization of the working fluid, pinch point temperature difference, evaporator outlet dryness, flash dryness, and entrainment ratio is conducted to achieve optimal performance. Aiming at maximum exergy efficiency and minimum levelized energy cost, the operating parameters are further optimized using a multi-objective optimization algorithm. R245fa is the optimal working fluid for the system, offering maximum net output power and thermal efficiency. The optimal performance can be achieved when the pinch point temperature difference is 1 K, evaporator outlet dryness is 0.6, flash dryness is 0.44, and entrainment ratio is 0.29. Moreover, the photovoltaic subsystem can further increase the net output power and thermal efficiency by 15.52% and 15.45%, achieving a maximum net output power and thermal efficiency of 33.95 kW and 10.61%. Additionally, when the solar hot water temperature is 100 °C, pinch point temperature difference is 1.8 K, evaporator outlet dryness is 0.6, flash dryness is 0.65, and entrainment ratio is 0.16, the system can achieve the optimal state of both performance and economy, exhibiting an optimal exergy efficiency and levelized energy cost of 64.1% and 0.294 $/kWh, respectively. Finally, the payback period of system is 3.43 years, indicating the potential for significant economic benefits.

Low-Temperature Fast Firing Preparation of Zn2TiO4 Crystalline Photovoltaic Glass Ink and Its Properties

A Zn2TiO4 crystalline photovoltaic glass ink was prepared by fast firing at 700 °C for 5 min by the glass crystallisation method, which effectively improved the reflectivity and acid resistance of the photovoltaic glass ink coating. The phase, morphology and properties of the samples were tested by XRD, SEM and UV-vis diffuse reflection, etc. The enhanced reflectivity mechanism was proposed. The results showed that the increase in ZnO/SiO2 ratio reduced the transition temperature (Tg) and crystallisation temperature (Tp) of the glass melt, which could promote the crystallinity of Zn2TiO4 in photovoltaic glass ink coatings and thus improve the acid resistance of photovoltaic glass inks. Significant improvement in reflectance and whiteness is due to the Zn2TiO4 crystallinity growth, which fills in the pores of the ink surface, and TiO2 fillers keep almost the same surface roughness (0.2 µm) and wetting angle (5.2°). Typical samples achieved 89.2% of the whiteness and 88.0% of the reflectance, and the weight loss in acid was 3.9 mg/cm2, which could improve the efficiency of solar power generation.

Ultra-broadband absorber based on algorithmic optimization of MgF2–Fe stacked structure

Broadband absorbers possess a wide array of potential applications, including electromagnetic cloaking, solar energy harvesting, and solar power generation. However, achieving excellent ultra-broadband absorption with plasma-metal materials remains challenging due to their inherently narrow bandwidth. To tackle this issue, we propose an ultra-broadband absorber, free from lithography, composed of a multilayer stack of magnesium-iron fluoride (MgF2–Fe). In this study, we optimized the structural parameters of the absorber using a particle swarm optimization algorithm to achieve theoretically optimal absorptivity. Numerical simulations revealed that the absorber achieved an impressive theoretical average absorption of 98.4% across a broad wavelength range from ultraviolet to near-infrared (321–1800 nm). Importantly, it exhibited stable absorption performance at large incidence angles (0–70°), while maintaining high absorption efficiency. Furthermore, it demonstrated polarization independence at perpendicular incidence angles. Subsequently, experimental preparations and tests were conducted on absorber samples to evaluate their solar energy absorption capacity. Both experimental and theoretical comparisons underscored the promising potential of this structure for applications in solar energy collection, conversion, and solar power generation.

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

The Feasibility Study of a Solar Power Plant at Building B, Universitas Multimedia Nusantara

The use of environmentally friendly energy resources is becoming increasingly important in reducing the carbon footprint of buildings. One approach is to integrate solar power generation into the building's energy system. This research aims to reduce the carbon footprint by replacing conventional electricity sources with environmentally friendly renewable energy sources and integrating energy sources without disrupting the productivity of activities in Building B of Universitas Multimedia Nusantara (UMN). This study encompasses several key parameters, including the limited use of solar panels in Building B of UMN and simulations conducted using PVsyst software with shadow-free assumptions and solar panel orientation facing north. Additionally, the research considers the potential production of clean energy from available utilization areas, including the roof and fifth-floor balcony of Building B. This feasibility study also includes an analysis of electricity usage over three months, with electricity consumption sourced from PLN. The analysis results show that the use of solar panels can yield long-term economic benefits, with capital costs recoverable within 20 years. However, further review is needed regarding potential system losses, costs, and the addition of energy storage options. Cost calculations need to be adjusted to consider building owner profitability for more accurate results. This study only considers profits without accounting for repair and maintenance costs over the 20-year system operation period.

The Influence of Environmental Factors on a Photovoltaic Panel–PVsyst Simulation

Renewable energy sources and the CO2 emissions in the atmosphere are predicted to play a very important role in attaining the climate neutrality by the half of this century. The rapid depletion of fossil fuel reserves and the looming threat of climate change have intensified the global pursuit of renewable energy sources as a viable alternative. The positive aspect of using renewable energy sources is that this kind of resources can easily supply the worlds energy needs. Despite the fact that we cannot capture all this energy, harnessing less than 0.02 % would be enough to cover all the necessary energy needs. Obstacles to expanding solar power generation involve the substantial cost of manufacturing solar cells and the dependency on weather conditions. The efficiency and performance of photovoltaic panels are inherently tied to environmental variables, prominently including climate factors. This article investigates the intricate relationship between climatic conditions and photovoltaic panel yields, offering crucial insights for optimizing the solar energy generations. It has been analysed the impact of environmental factors such as ambient temperature, wind speed and air humidity above the yield of photovoltaic panels. For each environmental factor, the variable conditions were simulated to highlight their correlation with positive or negative effect they have on the yield of a photovoltaic panel. By perusing this material, readers can develop a comprehensive grasp of how the environment conditions can influence the production of electricity using the photoelectric effect. Leveraging empirical data and simulation models using the PVsyst software, the analysis discerns regional variations and seasonal fluctuations in photovoltaic panels yields according to diverse climatic factors regimes.