Search

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements. [ABSTRACT FROM AUTHOR]

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models' accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN's 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation. [ABSTRACT FROM AUTHOR]

Abstract The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

This paper describes an innovative method to provide a micro-electric tractor which has been developed as part of an initiative that aims to provide socially inclusive and affordable green energy solutions to smallholders in sub-Saharan Africa. It utilizes a 15.0-kW solar array and battery system to power micro-electric tractors for an agricultural practice called deep bed farming in Malawi. Deep bed farming increases smallholder crop yield by about two and half times, which increases revenue by up to 12 times. The extra revenue can then be used to perpetually purchase domestic energy from the solar array, creating a self-sustaining model for decentralized energy access. However, the requirement for land preparation is onerous, and mechanization is needed to help overcome barriers to entry. This paper describes the technical requirements of a solar base station and micro-tractor for use with the deep bed farming technique. The paper covers the 5.9 kWp modular solar design and a 10.0-kWh rated micro-tractor. Testing was undertaken at an isolated grid in Loughborough as a precursor for large-scale testing in Malawi. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model's adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources. [ABSTRACT FROM AUTHOR]

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. [ABSTRACT FROM AUTHOR]

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.

The increasing energy demand in Indonesia, driven by rapid technological advancements across various industries and a growing population, necessitates the exploration of alternative energy sources. The reliance on traditional Steam Power Plants has contributed to rising greenhouse gas emissions and increasing operational costs, prompting the need for cleaner and more sustainable energy solutions. Solar energy emerges as a promising alternative to address these challenges. This study focuses on developing a solar power generation system capable of meeting the electrical energy needs in isolated areas. Using the PVsyst application, a new renewable energy system was designed to cater to the electricity requirements of residential homes. The study findings indicate that the system can generate 13,683 watts of electrical energy using eight solar panels, each with a capacity of 330 Wp. Additionally, the system includes 15 batteries with a capacity of 250 Ah to store energy. The designed system successfully meets the daily electricity demand of 8,210 watts for residential homes. To maximize solar energy absorption, the optimal tilt angle for the solar panels was determined to be 7 degrees. This study demonstrates that solar power generation systems are a viable and effective alternative for fulfilling electrical energy needs in isolated areas, offering a sustainable solution to reduce greenhouse gas emissions and reliance on conventional power sources.

Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R2) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R2 score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R2 score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R2 values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.

This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields YR, Capacity Factor CF, and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability.

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons. [ABSTRACT FROM AUTHOR]

High‐accuracy predictions of future solar power generations are important for monitoring, maintenance, dispatching, and scheduling. The goal of this study is to create a forecasting workflow that increases prediction accuracy independent of the machine learning method and has minimal computational requirements. The proposed trend decomposition method incorporates irradiance and seasonal features as exogenous inputs. In order to extract the linear part of the data, a moving average filter is used. The nonlinear (stable) component of the time series is then calculated by subtracting this linear part from the original data. The stable portion is modeled using several machine learning methods, while the ordinary least squares method is applied to the linear series. By aggregating both results, the final forecast is obtained. The forecasting performances of the machine learning algorithms on unprocessed data are used as baselines for evaluations. Improvements up to 39% in the mean absolute error and up to 31% in the root mean square error metrics are observed compared to the baselines. Experimental results show that the proposed trend decomposition with extrapolation method increases the forecasting performance and generalization capacity of machine learning algorithms. [ABSTRACT FROM AUTHOR]

The demand for sustainable energy is increasingly urgent to mitigate global warming which has been exacerbated by the extensive use of fossil fuels. Solar energy has attracted global attention as a crucial renewable resource. This study conducted a bibliometric analysis based on publication metrics from the Web of Science database to gain insights into global solar power research. The results indicate a stable global increase in publications on solar power generation and a rise in citations, reflecting growing academic interest. Leading contributors include China, the USA, South Korea, Japan, and India, with the Chinese Academy of Sciences emerging as the most prolific institution. Multidisciplinary Materials Science, Applied Physics, Energy and Fuels, Physical Chemistry, and Nanoscience and Nanotechnology were the most used and promising subject categories. Current hot topics include the systematic analysis of photovoltaic systems, perovskite as a solar cell material, and focusing on stability and flexibility issues arising during photovoltaic-grid integration. This study facilitates a comprehensive understanding of the status and trends in solar power research for researchers, stakeholders, and policy-makers. [ABSTRACT FROM AUTHOR]

This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields Y R , Capacity Factor CF , and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability. [ABSTRACT FROM AUTHOR]

Sandstorms pose several challenges for solar power generation in desert environments, such as dust accumulation, surface abrasion, structural stress, long-term degradation, and potential deterioration. This study aims to address these challenges by proposing a sandstorm-resistant solar tracking system that does not require complex and costly protection mechanisms, cleaning, or maintenance practices. Instead, we present an innovative approach to protect solar systems from sandstorms by integrating wind data readings into conventional solar tracking tools. Initially, sandstorms are detected when the wind speed exceeds a predetermined threshold. Then, the tracker adjusts its position based on wind direction data to reduce exposure and prevent damage. The system will resume regular tracking once the wind speed drops below the specified threshold. Our research indicates that positioning the tracker at a 20° angle and exposing it to the wind at an attack angle of 100° can reduce wind pressure on solar panels and minimize dust accumulation, thereby safeguarding the panels during sandstorms. The results show that the proposed tracking system consumed only 0.26–2.24% of the generated energy throughout the day, which is lower than other solar tracking systems and protective mechanisms. This efficient design has the potential to influence the future of photovoltaic (PV) systems and contribute to climate change adaptation and the economic feasibility of PV systems in desert environments. [ABSTRACT FROM AUTHOR]

The generation of power from solar energy by using Photovoltaic (PV) systems to convert the irradiation of the sun into electricity has been adopted over the past years. However, the PV system's P–V and I–V characteristics become unstable when solar irradiation and temperature change. In this paper, the incremental conductance (INC) has been improved using signals to measure the current and voltage from the PV systems directly which quickly changes with the environmental conditions, and the conventional particle swarm optimization (PSO) is modified so that under multiple shaded peak PV array curves with fast-changing solar irradiance and temperature, more power is extracted at a faster rate without any tracking failure at high-speed tracking of both individual maximum power point (IMPP) and global maximum power point (GMPP) under varying solar irradiance and temperature at a longer distance to enhance the power generated. The individual and global coefficients are also improved to change with multiple shaded peak PV array curves with fast-changing solar irradiance and temperature. DC-DC converter converts DC power from one circuit to another and DC-AC inverter converts DC power to AC power. Simulation was carried out in MATLAB Simulink with different solar irradiance and temperature whereby the conventional INC and PSO were compared with the proposed INC and PSO. An experiment was carried out for a whole day from 8 am to 5 pm to test the validity of the proposed algorithm and compared it with the conventional INC and PSO by using the solar irradiance and temperature received. From both the simulation and experimental results, the proposed INC and PSO performed better by attaining high power and tracking speed with stable output results than the conventional INC and PSO. [ABSTRACT FROM AUTHOR]

The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants.

This paper describes an innovative method to provide a micro-electric tractor which has been developed as part of an initiative that aims to provide socially inclusive and affordable green energy solutions to smallholders in sub-Saharan Africa. It utilizes a 15.0-kW solar array and battery system to power micro-electric tractors for an agricultural practice called deep bed farming in Malawi. Deep bed farming increases smallholder crop yield by about two and half times, which increases revenue by up to 12 times. The extra revenue can then be used to perpetually purchase domestic energy from the solar array, creating a self-sustaining model for decentralized energy access. However, the requirement for land preparation is onerous, and mechanization is needed to help overcome barriers to entry. This paper describes the technical requirements of a solar base station and micro-tractor for use with the deep bed farming technique. The paper covers the 5.9 kWp modular solar design and a 10.0-kWh rated micro-tractor. Testing was undertaken at an isolated grid in Loughborough as a precursor for large-scale testing in Malawi.

Benefiting from renewable energy (RE) sources is an economic and environmental necessity, given that the use of traditional energy sources is one of the most important factors affecting the economy and the environment. This paper aims to provide a review of hybrid renewable energy systems (HRESs) in terms of principles, types, sources, hybridization methods, cost of unit energy produced, and applications. The works were reviewed for HRESs with and without energy storage. The results can be summarized as follows: It is noted from the studies that Greenius, SAM, HOMER, and TRNSYS were often used in simulating, designing, evaluating, and optimizing these systems. There is often a difference in the economic and environmental indicators between different projects due to the type, fraction, price of energy and components, and efficiency of RE sources. All the studies showed that there are environmental benefits from hybrid systems, not only compared with conventional energy systems but also with RE systems with a single source. All of the related studies showed that hybridization between biomass and concentrated solar energy (biomass-CSP) presents a promising option for producing thermal energy and electricity, and this option also provides a solution for environmental problems related to waste biomass, such as municipal solid waste and wastewater and many industrial wastes, and provides high-quality fertilizers for agriculture. In addition, the multi-use of HRESs increases the economic and environmental benefits, which makes these systems more sustainable. There are various options available for hybridizing RE sources, particularly in the context of energy source integration. The selection of the appropriate options depends on several factors: system type, size of the system, type of energy needed, availability and prices of RE sources, technical knowledge, and experience in operation and maintenance. Several parameters play a crucial role in evaluating HRESs: system makeup and capacity, the fractions of RE in the overall energy produced, efficiency, investment, and energy costs, technical knowledge requirements, and environmental effects. [ABSTRACT FROM AUTHOR]

As the penetration level of solar power generation increases in smart cities and microgrids, an automatic energy management system (EMS) without human supervision is most communly deployed. Therefore, assuring safe and reliable data against cyber attacks such as false data injection attacks (FDIAs) has become of utmost importance. To address the aforementioned problem, this paper proposes detecting FDIAs considering visual data. The aim of visual state estimation is to enhance the resilience and security of renewable energy systems. This approach provides an additional layer of defense against cyber attacks, ensuring the integrity and reliability of solar power generation data and facilitating the efficient and secure operation of EMS. The proposed approach uses a modified VGG-16 neural network model to obtain an intermediate representation that provides textual and numerical explanations about the visual weather conditions from sky images. Numerical results and simulations corroborate the validity of our proposed approach. The performance of the modified VGG-16 neural network model is also compared with previous state-of-the-art machine learning models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Owing to high energy-intensive operations, cement production is responsible for global 6-8% of CO2 emissions and, thus, can be a major contributor in the net zero mission. Pakistan's cement industry can produce 69 million tons of cement per year and has an overall share of 5.3% in economy. One ton of cement production releases approximately one ton of carbon dioxide. Therefore, it is necessary to decarbonize this industry. Two strategies can be employed (waste heat utilization and CO2 capturing) for the decarbonization of the cement industry. This comprehensive review article is focused on the waste heat recovery potential and the technologies employed to utilize this potential for the cement industry of Pakistan. [ABSTRACT FROM AUTHOR]

This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration. [ABSTRACT FROM AUTHOR]

In order to achieve a sustainable energy future, it is of utmost importance to harness solar energy for the production of liquid hydrogen. This undertaking is justified by the significant role that liquid hydrogen plays as a clean and highly efficient fuel source, effectively addressing the pressing concerns regarding greenhouse gas emissions and reducing reliance on conventional fossil fuels. An additional advantage lies in the easy storage and transportation capabilities of liquid hydrogen, making it a viable solution for energy storage across diverse sectors. In this regard, the current research presents a novel solar-powered setup consisting of parabolic trough solar collectors (PTSCs), a sequential organic Rankine cycle (ORC), a liquefied natural gas (LNG) regasification unit, and a module dedicated to the production and subsequent liquefaction of hydrogen. A meticulously crafted MATLAB code is utilized to replicate the operations of the proposed system, facilitating a thorough examination of its energy, exergy, environmental, and economic performance. The proposed system exhibits a net output power of 1.13 MW and possesses the capacity to produce hydrogen at a rate of 34.92 kg/h, featuring a levelized cost of hydrogen (LCOH) of 3.59 $/kg. Additionally, the system encompasses a cooling capability of 192 kW. From an environmental perspective, this configuration contributes to a decrease in carbon dioxide emissions by 255.96 kg/h. After conducting a dynamic analysis with meteorological data, it was determined that the system has the potential to generate an annual hydrogen output of 536.84 tons in San Francisco.

This report presents creative engagement activities based on the Designing with the Sun: Solar Curriculum Project that teaches high school and undergraduate students the principles of solar design and the steps needed to design and build a solar charging station. This in-depth curriculum covers renewable energy, electricity basics, solar design principles, and solar-supported mobility. Each chapter has a PowerPoint presentation, an active learning activity, video clips, and links to learn more. The solar curriculum materials are free for educators and self-learners to download and explore at their own pace. Small-scale solar charging stations provide a living lab for research and a place to recharge e-bikes and e-scooters. Shared micromobility (e-bike and e-scooter fleets) has exploded in popularity on college campuses and can help reduce car ownership and carbon emissions when recharged with the sun. As universities plan for the challenges of the 21st century, incorporating multifaceted forms of renewable energy with electric vehicle charging is a step toward climate action and decarbonization. Creative rethinking on a massive scale is required to meet the goals set by the Intergovernmental Panel on Climate Change and the COP 21 Paris Agreement to limit global warming to 1.5°C. The UN’s Sustainable Development Goals such as numbers 7, Affordable and Clean Energy;9, Industry, Innovation and Infrastructure; and 11, Sustainable Cities and Communities are all important guides for modeling solar education.View the NCST Project Webpage

Renewable energy generation cannot be consistently predicted or controlled. Therefore, it is currently not widely used in the electricity market, which requires dependable production. In this study, reliability- and variance-based controls of energy storage strategies are proposed to utilize renewable energy as a steady contributor to the electricity market. For reliability-based control, photovoltaic (PV) generation is assumed to be registered in the power generation plan. PV generation yields a reliable output using energy storage units to compensate for PV prediction errors. We also propose a runtime state-ofcharge management method for sustainable operations. With variance-based controls, changes in rapid power generation are limited through ramp rate control. This study introduces new reliability and variance indices as indicators for evaluating these strategies. The reliability index quantifies the degree to which the actual generation realizes the plan, and the variance index quantifies the degree of power change. The two strategies are verified based on simulations and experiments. The reliability index improved by 3.1 times on average over 21 days at a real power plant.

Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy. Doi: 10.28991/ESJ-2023-07-04-02 Full Text: PDF

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.

The world is undergoing an irreversible shift towards clean energy. Microgrids are recognized as a key technology that holds significant potential to make a substantial difference in this regard. The paper provides a comprehensive overview of how microgrids work and their impact on climate. The research presented in this paper focuses on reducing carbon dioxide (CO2) in the main campus of Qassim University, Saudi Arabia, through the development and implementation of an engineering model that facilitates the installation of a microgrid system designed to meet the university's sustainability goals. The study aims to explore possible solutions that can reduce emissions in the administrative building (A7) at Qassim University and meet the university environmental plan. Therefore, a comprehensive study is conducted to investigate the potential reduction in emissions associated with the installation of a microgrid system. This microgrid system operates in a grid-connected mode and comprises three main components: the load, a photovoltaic (PV) system, and batteries. The results of the study indicate that the microgrid reveals a notable transition in the primary sources of electricity. Moreover, the microgrid system proves its capability to meet a substantial portion of the daily energy requirements, highlighting its efficiency and effectiveness in addressing energy needs. The findings of this study highlight the significant potential of the proposed model in curbing carbon emissions, as it demonstrates a reduction from 615.8 to 147.4 Mt of CO2. This reduction aligns with the university's commitment to sustainability and green initiatives. The computed decrease in carbon footprint emphasizes the possibility of the suggested model to encourage sustainable practices among the university community and mitigate the environmental consequences of energy usage. [ABSTRACT FROM AUTHOR]

Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems. [ABSTRACT FROM AUTHOR]

Renewable energy generation cannot be consistently predicted or controlled. Therefore, it is currently not widely used in the electricity market, which requires dependable production. In this study, reliability‐ and variance‐based controls of energy storage strategies are proposed to utilize renewable energy as a steady contributor to the electricity market. For reliability‐based control, photovoltaic (PV) generation is assumed to be registered in the power generation plan. PV generation yields a reliable output using energy storage units to compensate for PV prediction errors. We also propose a runtime state‐of‐charge management method for sustainable operations. With variance‐based controls, changes in rapid power generation are limited through ramp rate control. This study introduces new reliability and variance indices as indicators for evaluating these strategies. The reliability index quantifies the degree to which the actual generation realizes the plan, and the variance index quantifies the degree of power change. The two strategies are verified based on simulations and experiments. The reliability index improved by 3.1 times on average over 21 days at a real power plant. [ABSTRACT FROM AUTHOR]

Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar power is entirely unpredictable since solar power generation is dependent on the intensity of solar irradiation and solar panel temperature. Further, these parameters are weather dependent and thus intermittent in nature. To conquer intermittency, power converters play an important role in solar power generation. Generally, photovoltaic systems will eventually suffer from a decrease in energy conversion efficiency along with improper stability and intermittent properties. As a result, the maximum power point tracking (MPPT) algorithm must be incorporated to cultivate maximum power from solar power. To make solar power generation reliable, a proper control technique must be added to the DC–DC power converter topologies. Furthermore, this study reviewed the progress of the maximum power point tracking algorithm and included an in-depth discussion on modern and both unidirectional and bidirectional DC–DC power converter topologies for harvesting electric power. Lastly, for the reliability and continuity of the power demand and to allow for distributed generation, this article also established the possibility of integrating solar PV systems into nanogrids and picogrids in a sustainable environment. The outcome of this comprehensive survey would be of strong interest to the researchers, technologists, and the industry in the relevant field to carry out future research. [ABSTRACT FROM AUTHOR]

Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions. [ABSTRACT FROM AUTHOR]

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation’s attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach’s prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.

Increasing global energy consumption also raised concerns about environmental issues. Solar energy is one of the most promising sustainable solutions for this problem. However, due to the dynamic behavior of environmental conditions, energy conversion efficiencies of Solar photovoltaic systems decrease considerably. Therefore, to extract the highest energy from photovoltaic systems, the use of maximum power point tracking methods is a necessity. Under partial shading conditions where local maximum power points also occur, global maximum power point tracking is required. In this study, fundamental concepts for photovoltaic systems, conventional maximum power point tracking methods, modern maximum power point tracking methods – which can perform global optimization under partial shading conditions – based on automatic control and artificial intelligence approaches, and advantages and disadvantages of all these methods are provided briefly. Also, a comprehensive review, including more than a hundred prestigious studies carried out on these methods in the last decade is presented categorically and chronologically. Further, discussion and evaluation of reviewed methods in various aspects are given in a nutshell. This study aims to be a guide that may be useful for interested consumers, producers, and researchers and shed light on the last decade of maximum power point tracking methods for photovoltaic systems.

The continuing growth in greenhouse gas (GHG) emissions and the rise in fuel prices are the primary motivators in the wake of attempts to efficiently utilize diverse renewable energy resources. Direct solar radiation is regarded as amongst most potential energy resources in many regions of world. Solar energy is a renewable energy resource which may be used for a variety of purposes in present society. The successful utilization of solar energy necessitates the employment of a storage media capable of storing excess energy and then supplying this stored energy when needed. However, large-scale usage of this type of energy is merely viable if potential storage technology could be created having reasonable capital and operating costs. The use of phase change materials is one of the potential methods for storing solar energy (PCMs). Superior thermal characteristics of innovative materials, like phase change materials, are basically needed to maximize solar energy usage and to increase the energy and exergy efficiency of the solar absorption system. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. This chapter discusses the fundamentals of phase change materials (PCMs), how they function, thermal energy augmentation in PCMs, commercially accessible PCMs, and active and passive solar heating systems. This chapter presents a detailed study of PCMs usage for solar energy employment as well as storage like for solar power production, solar cookers along with water heating systems. The chapter would help researchers in better understanding on solar power production, water heating systems, and solar cookers employing PCMs for marketable growth. [ABSTRACT FROM AUTHOR]

The traditional maximum power point tracking technology has the disadvantages of slow convergence speed, poor accuracy and easy to fall into local optimum. Aiming at the above problems, a combined MPPT control method based on improved cuckoo algorithm and conductance increment method is proposed. Firstly, the iterative convergence speed and global optimization ability are increased by the adaptive change of Levy step size and nesting rate in CS algorithm. Secondly, the maximum position difference between nests is judged to switch INC to prevent it from falling into local minimum. Finally, static comparison and dynamic illumination simulation experiments are carried out. The static comparison simulation results show that this control method has more advantages in tracking speed and accuracy. The dynamic simulation test results show that this method is robust in complex environment. [ABSTRACT FROM AUTHOR]

This research study proposes a methodology based on data science to estimate the technical feasibility of implementing hybrid solar-wind power systems by calculating the plant factor, also known as capacity factor (CF). Data is gathered from the NOVALYNX 110-WS-18 weather station, between the years 2018 and 2020, to determine the energetic potential available for the Candelaria campus at the La Salle University (Colombia). A k-means data clustering algorithm, developed in Matlab, is applied over the following descriptors: time, solar radiation, wind speed, and temperature. The results show that the photovoltaic and wind power systems have a CF of 36.69% and 2.15%, respectively. It is concluded that it is technically feasible to implement a photovoltaic system at the La Salle University, but not a wind power system as it does not meet the minimum CF required. [ABSTRACT FROM AUTHOR]

California has goals to rapidly expand electric vehicle adoption, with executive orders calling for 1.5 million electric vehicles on the roads by 2025 and 5 million by 2030. Significant charging infrastructure will be needed to support these new vehicles. While many urban areas in California have prioritized construction of charging stations, most rural areas lack charging infrastructure. This deficit hinders electric vehicle adoption in rural areas and makes long distance electric vehicle travel difficult.To address this issue, Caltrans has begun investing in charging infrastructure in rural and underserved areas around the state, particularly at highway rest areas. However, an understanding of potential future intercity charging demand will be needed to inform continued investments in support of a growing electric vehicle fleet.This policy brief summarizes findings from researchers at the University of California, Davis, who collected state travel data and electricity demand data to run a model that identified optimal highway rest areas for electric vehicle charger installation and calculated how an increase in charging demand would affect the California electricity grid at selected highway locations. The project aimed to maximize the use and generation of solar and wind energy, while also increasing electric vehicle adoption and mobility in the state.View the NCST Project Webpage

California policy is incentivizing rapid adoption of zero emission electric vehicles for light-duty and freight applications. This project explored how locating charging facilities at California’s highway rest stops might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix. Assuming a growing population of electric vehicles to meet state goals, state-wide growth of electricity demand was estimated, and the most attractive rest stop locations for siting chargers identified. Using a California-specific electricity dispatch model developed at UC Davis, the project estimated how charging vehicles at these stations would impact renewable energy curtailment in California. It estimated the impacts of charging infrastructures on California’s electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.View the NCST Project Webpage

With the growth in the solar photovoltaic (PV) market, there is a renewed interest in increasing the system’s annual produced energy. Most of the proposed solutions in the literature require special additional equipment, which might adversely affect the cost of the produced energy. This paper proposes an integrated PV-reflector system that augments the solar irradiance on already installed PV modules. A new mathematical model has been developed in MATLAB R2020a to simulate and techno-economically assess the proposed integrated PV system during the entire year using real meteorological data. A genetic algorithm has been employed in MATLAB R2020a to obtain the optimal geometric and optical parameters that maximize the annual produced energy and minimize the Levelized Cost of Electricity (LCOE) at two selected locations (Cairo in Egypt and Ma’an in Jordan) and for three module technologies (monocrystalline, polycrystalline, and thin film) and different commercial reflectors. The optimal system configurations increased the annual energy production by up to 6.05, 5.14, and 8.34% for the three PV technologies, respectively. For both locations, the optimal tilt angles of the PV module and reflector range between 24° and 34° for Cairo, and between 19° and 36° for Ma’an, depending on the PV technology. Finally, all the system configurations have favorable and low payback periods (∼3 and 6.5 years for Ma’an and Cairo, respectively), as well as attractive LCOE, which varies between 0.0369 and 0.044 USD/kWh in Ma’an and between 0.0543 and 0.065 USD/kWh in Cairo.

The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly.