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The reliable prediction of solar energy production and surplus is crucial for the stability of the electricity grid and effective energy distribution, particularly during peak consumption periods. Encouraging solar adoption by customers is also important despite fluctuations in solar energy generation. This paper addresses the need for an intelligent surplus solar energy prediction model, referred to as the Green Model, which aims to accurately predict surplus electrical energy that can be returned to the distribution grid. The Green Model utilizes a combination of a developed long short-term memory (LSTM) neural network and a novel optimization technique called deterministic selection network by gain sharing knowledge (DSN-GSK). The DSN-GSK optimizes the structure of LSTM by determining the optimal number of hidden layers, nodes in each layer, biases, weights, and activation functions based on the most important features that represent the relationship between generated solar energy and weather factors. The Green Model was evaluated and analysed, demonstrating high accuracy in the short-term prediction of surplus electrical energy with a minimal percentage of error. The model shows promising prospects for the development of returned electrical energy prediction. The main novelty of this paper lies in the development of the Green Model, which combines LSTM and DSN-GSK to enhance the accuracy of surplus solar energy prediction. The benefits of the Green Model include improved stability and efficiency of the electricity grid, effective utilization of renewable energy sources, and the reduction of negative environmental impacts. The contributions of this paper include advancing the field of intelligent energy prediction, optimizing the structure of LSTM, and providing valuable insights for the integration of renewable energy into the existing energy infrastructure. [ABSTRACT FROM AUTHOR]

The energy requirement of the world will keep on rising, and fossil fuels have been increasingly used in this regard. The use of fossil fuels brings about different sets of problems like climate change, greenhouse gas emissions, etc. So, a desperate need for an alternative source of energy which can be cheap and sustainable is being felt. Renewable forms of energy are environment friendly and can be looked into for our energy needs. Among renewable forms of energy, solar energy is free and extensively obtainable. This paper reviews solar air heater (SAH) that utilizes solar energy to provide medium temperature air output. The main disadvantage of SAH is its low efficiency. So, researchers have used different ways to improve its efficiency. This paper looks into the numerical methods and new techniques implemented by investigators to numerically model a SAH while organizing their results in a tabular form. Subsequently, the study analyses the thermal performance of SAH in terms of thermohydraulic performance parameter (THPP) and organizes their results in tabular form. The maximum THPP achieved among the cases studied was from Jouybari and Lundstrom (Energy 190:116437, 2020) and equalled 5.5. Finally, the current study will provide researchers with a quick view of the turbulence models used by investigators along with information on the number of grids to gauge the computational power required for the study and concludes with few key points on the subjects that can be further explored. [ABSTRACT FROM AUTHOR]

This review presents the technical and operational performances of solar and biomass energy technologies viz photovoltaic thermal (PVT) and biomass gasification systems. This work aims to offer a reference and guidelines to the renewable energy‐related players, especially for those at the operational level and investors. This paper highlights the technical advantage of hybrid PVT with phase change material in terms of electrical and thermal efficiencies. While the operational performance of biomass gasification is thoroughly discussed via sensitivity analysis, the potential integration between solar and biomass gasification technologies is explored to complement and bolster the capabilities of both renewable systems within the power energy mix portfolio. Finally, few directions and significant takeaways considering the technical criteria are addressed for identifying efficient renewable energy generation. [ABSTRACT FROM AUTHOR]

Among renewable resources, solar energy is abundant and cost effective. However, the efficiency and performance of photovoltaic panels (PVs) are adversely affected by the rise in the surface temperature of solar cells. This paper analyzes the idea of utilizing thermoelectric modules (TEMs) to enhance the efficiency and performance of PV panels. The proposed hybrid solar thermoelectric generation (HSTEG) system employs TEMs as thermoelectric coolers (TECs) to enhance panel efficiency and as thermoelectric generators (TEGs) to convert excess heat into additional electricity. This study includes an extensive evaluation of the proposed idea using MATLAB Simulink and experimental validation in indoor as well as outdoor environments. The use of TECs for the active cooling of the PV system leads to an increase in its efficiency by 9.54%. Similarly, the passive cooling by TECs along with the additional power generated by the TEGs from the excessive heat led to an increase in the efficiency of the PV system of 15.50%. The results demonstrate the HSTEG system's potential to significantly improve PV panel efficiency and energy generation, offering a promising avenue for advancing solar energy technology. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque's energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque's energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region. [ABSTRACT FROM AUTHOR]

A desire to shift from fossil fuels to non-carbon-emitting energy sources has become an imperative supported by national, state, and local policies. In the U.S., a diverse array of policies at multiple levels of government have helped the solar industry achieve exponential growth. These advancements, however, are mediated by local implementation processes, which might be expected to have a large impact in the context of U.S. federalism. This paper investigates the effects of two countervailing forces – policy incentives and implementation disincentives – on residential solar adoption in New Jersey. The New Jersey case study includes two complementary analyses designed to illuminate policy incentives and implementation disincentives, respectively. The first is an interrupted time-series analysis that evaluates the effects of federal and statewide renewable energy policies on residential photovoltaic (PV) growth. The second is a set of semi-structured in-depth interviews with solar industry experts, providing implementation insights from the solar industry and indirectly portraying residential customers' experiences. Results confirm that market-based instruments at the state-level play a crucial role in increasing the relative financial advantage of these systems and, thus, the attraction of residential solar PV to adopters. In addition, the behaviours of future adopters and the valuation of this technology as an investment in the housing market will impact the spread of residential PV systems in the future. Unfortunately, case study findings also confirm that an absence of standardized solar application procedures and outdated interconnection standards is a significant drag on the adoption rate. This highlights a need for policymakers to place greater emphasis on local implementation pathways in solar policy design. Options to achieve this include more robust ex-ante coordination among state and local levels of government and with industry to standardize implementation processes. While U.S. federalism may at times be constraining, our results suggest that even minimal levels of ex-ante coordination can lead to implementation gains that will have a large impact on solar diffusion outcomes. Federal and state-level financial incentives significantly impact the residential photovoltaic solar adoption rate. The absence of streamlined interconnection application procedures and outdated interconnection standards for the grid limit the residential PV adoption in New Jersey. Our findings underline the importance of paying equal attention to both policy design and to technical standards to underpin implementation processes, especially in the decentralized context of U.S. federalism. [ABSTRACT FROM AUTHOR]

The use of solar energy is now a common and modern alternative that many countries throughout the world have adopted. Different studies on PV systems have been documented in the literature; however, several reviews focus excessively on particular facets of solar modules. In this paper, the literature on PV systems published between 2000 and 2023 was reviewed thoroughly. This review is structured in three main parts. Primarily, the main factors impacting dust deposition on solar modules are discussed. These include temperature, wind speed, inclination angle, location, climatic conditions, photovoltaic module surface characteristics, and dust characteristics. Many methods for mitigating and reducing dust as well as approaches to cleaning PV modules are also reviewed in this study. The many types of solar modules, together with their most important characteristics and operational effectiveness, are presented. As more solar photovoltaic panels expand their end of life (EOL), solutions are required to recycle and dispose of solar photovoltaic panels at the lowest economic cost and with the least environmental damage through reduced carbon emissions and greenhouse gases. Subsequently, this paper further reviews solar PV energy for a green environment and PV waste recycling and its costs. Moreover, integrating solar energy with other clean energy constituting an energy source for hard-to-reach areas and an alternative to fuel are discussed. Therefore, this comprehensive review of the use of photovoltaic systems for green energy production is helpful in an increased share of clean energy for various energy sectors in the future. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

This paper takes a cursory look at the problem of inadequate power supply in the rural healthcare centres of a developing country, specifically Nigeria, and proffers strategies to address this issue through the design of hybrid renewable energy systems combined with the existing unreliable grid in order to meet the healthcare load demand, thus ensuring higher reliability of available energy sources. The simulations, analysis and results presented in this paper are based on meteorological data and the load profiles of six selected locations in Nigeria, using which hybrid grid-connected systems integrating diesel, solar and wind energy sources are designed with configurations to give optimum output. The optimised design configurations in the considered case study, Ejioku, Okuru-Ama, Damare-Polo, Agbalaenyi, Kadassaka and Doso, produce very low energy costs of of 0.0791 $/kWh, 0.115 $/kWh, 0.0874 $/kWh, 0.0754 $/kWh, 0.0667 $/kWh and 0.0588 $/kWh, respectively, leveraging solar and wind energy sources which make higher percentage contributions at all sites. The load-following-dispatch strategy is adopted at all sites, ensuring that at every point in time, there is sufficient power to meet the needs of the healthcare centres. Further works on this topic could consider other strategies to optimise general energy usage on the demand side. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations' energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues. [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]

This paper utilizes social network analysis to examine the structural characteristics and trade dynamics of the renewable energy (hydropower, wind energy, and solar energy) trade network within the RCEP region from 2011 to 2020. The findings reveal: (1) The renewable energy trade network within the RCEP exhibits dynamism, heterogeneity, and an uneven development. The solar energy network is the most balanced and stable, while the wind energy network lags and shows marked fluctuations, with the hydropower network falling between these two. This demonstrates the diversity of energy trade within the region. (2) China, Singapore, and Japan are identified as the key exporting and importing countries, with Vietnam showing substantial growth potential. Individual analyses shed light on the stark disparities in trade status among nations, reflecting the diverse roles and future potential of member countries. (3) The QAP regression analysis reveals a significant influence of environmental pressure, particularly carbon dioxide emissions, on the renewable energy trade network. This study contributes to promoting environmental sustainability and energy security in the RCEP region and provides empirical evidence for global renewable energy trade strategies. [ABSTRACT FROM AUTHOR]

In photovoltaic power plant inspections, techniques for module assessment play a crucial role as they enhance fault detection and module characterization. One valuable technique is luminescence. The present paper introduces a novel technique termed passive luminescence. It enhances both electroluminescence and photoluminescence imaging acquisition in photovoltaic power plants under normal operation in high irradiance conditions. This technique is based on the development of an electronic board, which allows the polarity of the module to be changed, enabling the current generated by the photovoltaic string to be injected into the module and producing electroluminescence effects. Additionally, the board can bypass the module and set an open circuit, inducing photoluminescence emission using sunlight as an excitation source. The proper coordination of the board and an InGaAs camera with a bandpass filter has allowed for the integration of a lock-in technique, which has produced electroluminescence and photoluminescence pictures that can be used for fault detection. [ABSTRACT FROM AUTHOR]

In recent decades, the world has witnessed a great expansion in the world of technology and electronics, in addition to the tremendous development in various industries, which has led to an increase in the need for electrical energy significantly. Renewable energy generated from environmentally friendly sources such as energy (solar, water, windmills, etc.) is the solution. The alternative is to provide that energy, especially as it is clean energy that does not cause the emission of carbon dioxide, which pollutes the air and the environment in general. This work presents a software model for producing the largest amount of energy by developing one of the best prediction techniques and using multi-parameter objective functions, where the proposed model called zero to maximum energy based on developing gradient boosting machine (DMP-DGBM) consists of several stages. The problem of this work is divided into parts: The first part is related to programming challenges, while the second part is related to application challenges; as we know, the prediction techniques are split based on the scientific field into two fields: prediction techniques related to data mining and predictions related to deep learning techniques; this work deals with the first type of prediction technique. (a) One of the data mining prediction techniques is the gradient boosting machine characterized by many features that make it the best. These features are GBM gives high accuracy results and works with huge data stream of data, but on the other hand, the core of that algorithm is a decision tree (DT) that has many limitations such as requiring choosing the root of the tree, determining the maximum number of levels of the tree, and also having high computation and long time. Therefore, the first challenge of this paper is how can avoid these limitations (i.e., high computation and implementation time) of this algorithm and benefit from their features. (b) The problem of generating electrical energy from environmentally friendly sources with high efficiency is one of the most important challenges in this field. Therefore, the second challenge of this paper is how can avoid these limitations by building an efficient technique to predict maximum energy from solar energy. DMP-DGBM model consists of many stages applied through a stepwise style. The first stage presents capturing datasets from scientific site which contains the data related to both weather and solar plant. The second stage is preprocessing which contains multi-steps including: (a) merging between two datasets; (b) splitting readings into intervals and deleting the duplicate; and (c) applying Pearson's correlation to the new dataset. In the third stage, the ZME-DGBM model is constructed based on developing gradient boosting techniques by replacing its kernel (i.e., decision tree function) with multi-parameter optimization functions. The stage begins with dividing the dataset into two sets using five cross-validation methods, and the training dataset is used to construct the DMP-DGBM models, while the testing dataset is used to evaluate them. Finally, the results of the DMP-DGBM are evaluated based on three measures (i.e., coefficient of determination, mean error, and root mean square error. The stage of constructing the predictor relied on replacing the GBM kernel with four different multi-parameter functions, as these were the parameters with the highest correlation with the target, and a threshold value of 0.95 was adopted as determining the importance of the parameters. The proposed model was characterized by giving the best results using a three-parameter function MPF4 for the GBM kernel, and those parameters were AC, TEM, and IRR, where the scale was (R2 = 0.9742), while for MSE = 0.0099 and RMSE = 0.0522 also the system is taken only 80 Ms to implement. [ABSTRACT FROM AUTHOR]

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration. [ABSTRACT FROM AUTHOR]

Solar energy has gained prominence because of the increasing global attention received by renewable energies. This shift can be attributed to advancements and innovations in solar cell technology, which include developments of various photovoltaic materials, such as thin film and tandem solar cells, in addition to silicon-based solar cells. The latter is the most widely commercialized type of solar cell because of its exceptional durability, long-term stability, and high photoconversion efficiency; consequently, the demand for Si solar cells has been consistently increasing. PV modules are designed for an operation lifespan of 25–30 years, which has led to a gradual increase in the number of end-of-life PV modules. The appropriate management of both end-of-life and prematurely failed PV modules is critical for the recovery and separation of valuable and hazardous materials. Effective methods for end-of-life PV waste management are necessary to minimize their environmental impact and facilitate transition to a more sustainable and circular economy. This paper offers a comprehensive overview of the separation processes for silicon PV modules and summarizes the attempts to design easily recyclable modules for sustainable solar module development. Based on the studies summarized in this paper, suggestions are provided for future research. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to evaluate the hypothetical situation in a resembling airport to Esenboga Airport and analyzes the condition of all ground support equipment (GSE) equipment to be supplied by electricity produced by solar panels mounted on the rooftop of the terminal building. The case is discussed using environmental emissions and economic feasibility. The results of the resembling case can be generalized to all airports for the reduction of emissions caused by ground operations of aviation. Design/methodology/approach: GSE fleet data which has been prepared by TGS operated in the Esenboğa Airport have been used to calculate emissions, and equivalent electricity consumption. A hypothetical solar panel construction on the rooftop of the terminal building and also the electricity production case was analyzed. Based on the calculations, both fuel and electricity use cases are compared by means of emissions and production costs using real data. Findings: The electricity production and transmission pose a high value of emissions. Thus, electrification of GSE in the airport need a new approach such as producing the electric energy in the site. This research analyzes the case that the electricity is produced on the rooftop of terminal building and consumed by the GSE fleet. The authors discussed that it is both feasible and possible to electrify all the GSE except a shortage of two cold months with high fuel demand by using electric storage options. Practical implications: Ground handling is performed by using GSE which is historically powered by diesel and such internal combustion engines which are well known for their high emission rates. As most of the airports reside in populated areas, GSE emissions need to be evaluated for reduction. However the electric energy could be an alternative for GSE emissions reduction Originality/value: Aviation is a system of many subsystems in which the performance of each unit plays a crucial role in the final success of the system. Concerns on environmental protection make the aviation industry focus on reducing emissions produced during operations. Although aircraft emissions are widely discussed in the literature, ground handling systems which are an integral part of the whole aviation system, also need to be studied regarding the environmental issues. Besides, the European Union has set out targets of reducing emissions at the airports during ground operations to zero. This paper discusses the possibility of the target by comparing various scenarios [ABSTRACT FROM AUTHOR]

This paper focuses on the design and use of a control system for a renewable energy production plant based on hydrogen. The proposed control system aims at ensuring the stability and smooth functionality of the plant, which consists of a (i) photovoltaic system connected to an electrolyzer through a battery, (ii) a DC/DC step down transformer, and (iii) an electrolyzer heat exchange system. In this study, solar irradiance is the main system input, and hydrogen production the main output. Since the system utilizes solar energy as input, it depends on the random input of solar irradiance, ambient temperature, and wind flow. Furthermore, the electrolyzer's functionality is subject to several operational variables including cell voltage, current, temperature, and pressure. The electrolyzer heat exchange system operates at specified water temperatures and flow rates. The DC/DC output voltage, and therefore the voltage supplied to the electrolyzer, is regulated by changing its duty cycle. To regulate hydrogen production in the renewable energy production plant, an efficient control system is required. Accordingly, in this work, a control system is designed accounting for three different electrolyzer technologies, alkaline, PEM (proton exchange membrane), and E-TAC (electrochemical - thermally activated chemical water splitting). Subsequently, the effectiveness of the control system is analyzed using Matlab and Simulink models. The main results indicate that the battery is a crucial element in the whole system as it supplies the necessary energy to the electrolyzer. By regulating the appropriate components, the proposed control system proved capable of minimizing power fluctuations and increasing system efficiency up to 20% depending on ambient conditions. Additionally, the results indicate E-TAC and PEM efficiencies 13% and 7% higher than alkaline, respectively. [Display omitted] • Design of a control system for three different electrolyzer technologies. • Use of control systems for electrolyzer voltage and heat exchange. • Photovoltaic system connected to an electrolytic one through a battery. • Proposed control system increases system efficiency up to 20%. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model. [ABSTRACT FROM AUTHOR]

In this paper, we argue using smart technologies beyond the grid disrupts the access and use of existing energy sources, with profound impacts on everyday social life. We show how off‐grid smart energy solutions constitute their own politics when considering existing conceptualisations of urban infrastructures in geography and the social sciences. To expose its politics, or "micropolitics", we consider how tensions occur at the interface between infrastructures, where there are additions and modifications. We draw on an empirical example of Qandu‐Qandu, an informal settlement in South Africa, to highlight how the placement, technical capabilities, and flexible financing options associated with off‐grid solar energy create micropolitics with profound implications for everyday life. To conclude, we reflect on the value of using disruptions for understanding and enhancing equity in off‐grid settings, contributing to the broader sustainability transitions narrative, and its "liveliness". [ABSTRACT FROM AUTHOR]

Renewable energy sources are seen as a sustainable solution to the problem of global energy security. Among the renewable energy sources, wave energy has great prospects for development in a few countries, including South Africa. However, South Africa's wave energy projects are still underinvested in practice compared to wind energy and solar PV. This paper investigates several questions related to the influence of both quantitative and qualitative factors on the effectiveness of wave energy projects (WEPs) and the appropriate placement for WEP in South Africa. For this purpose, a novel two-phase framework novel two-phase data envelopment analysis (DEA) supporting integrated multiple criteria decision-making (MCDM) framework is proposed. The first phase aims to identify potential locations for WEPs and calculate the technical advantage of these locations using the DEA model. In the second phase, other qualitative criteria, which affect the sustainable development of WEPs, are identified. Based on expert surveys, potential locations are analyzed and ranked by a novel integrated MCDM approach called OPA-EDAS. In the proposed approach, the advantages of the Ordinal Priority Approach (OPA) are utilized to simultaneously determine the weight of the criteria and prioritize the alternatives. However, the results of OPA depend on the priorities of the experts participating in the assessment. Therefore, this study attempted to overcome that by integrating the EDAS technique to assess the expertise. Furthermore, the DEA model is applied to increase the objectivity of the results. In addition, the robustness of the study's findings is validated by comparing the location with upcoming WEPs in South Africa. The result shows that the coast of Cape Nature Walker Bay has the most effectiveness for South Africa's WEPs. [ABSTRACT FROM AUTHOR]

Heating and thermal comfort have remarkable share of final energy consumption. Until now, most of the demand for heating applications in buildings is supplied by fossil fuels and electrical technologies. Concerning the exhaustion of fossil fuels in the future and the environmental problems related to their consumption, making use of renewable energy sources can be a practical alternative. On this point, solar energy is an appropriate source to be applied for heating by utilizing different technologies. The function and output of solar heaters depends on numerous factors, and this causes difficulties in the prediction of their performance and modelling. In this scenario, intelligent techniques are helpful and have been used by several scholars in recent years. This paper reviews proposed models for the prediction of the performance of different solar heaters. The literature review reveals that artificial neural Networks represent one of the most used approaches for the performance prediction of solar heaters; however, other intelligent techniques, namely support vector machines, have been used for this purpose too. Moreover, it is found that these methods have the ability to predict with great precision by applying the appropriate approach and architecture. In addition, it can be noted that the function of the models generated based on intelligent techniques are associated with some elements such as the employed function and architecture of the model. [ABSTRACT FROM AUTHOR]

This research paper aims to design a solar-powered boat and analyze the effects of environmental and form-related factors on power consumption and battery duration by utilizing a system dynamics approach-based simulation. The boat form is designed as the planing hull and its hull resistance analysis was ensured in Maxsurf package program. PV panels with 548 W power output and two battery packs with 4660 Wh capacity were placed on the hull body to employ an electric motor with a 10-kW nominal power output. Two MPPTs were implemented in the system to increase solar system efficiency. The relationships between all system components were modelled in Vensim software to observe battery endurance changes under different conditions. Results demonstrated that the ideal vessel speed is calculated to be around 7 knots with roughly 8 hours of battery duration for the designed boat. A critical stage of charge for sailing is 40% since 1.63 hours of cruising time may be achieved while maintaining a speed of 5 m/s (9.72 knots). Indeed, the boat's rising trim angle shortens the battery discharge time; thus, navigation by no trim angle is the most effective usage for the vessel. [ABSTRACT FROM AUTHOR]

This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) and wind speed were computed using four standalone forecasting models (random forest, k-nearest neighbors, support vector regression, and elastic net) to compare their performance against two dynamic ensemble methods, windowing and arbitrating. The standalone models and the dynamic ensemble methods were evaluated using the error metrics RMSE, MAE, R2, and MAPE. This work's findings showcased that the windowing dynamic ensemble method was the best-performing architecture when compared to the other evaluated models. For both cases of wind speed and solar irradiance forecasting, the ensemble windowing model reached the best error values in terms of RMSE for all the assessed forecasting horizons. Using this approach, the wind speed forecasting gain was 0.56% when compared with the second-best forecasting model, whereas the gain for GHI prediction was 1.96%, considering the RMSE metric. The development of an ensemble model able to provide accurate and precise estimations can be implemented in real-time forecasting applications, helping the evaluation of wind and solar farm operation. [ABSTRACT FROM AUTHOR]

Recently, the proportion of renewable energy (RE) sources in a power system has been increasing worldwide to reduce carbon emissions. To effectively accommodate renewable energy, there is a growing need to develop integrated planning strategies for both renewable energy sources and transmission lines in a power system, taking into account the location-specific characteristics of renewable energy. Economically viable and rapid system expansion plans are required to resolve the problem of delayed integration of renewable energy into existing power system, which arises due to inadequate transmission facilities. To address these problems, this paper presents an integrated economic evaluation method that considers the inherently uncertain output characteristics of renewable energy, contingent on location, the costs associated with installing transmission lines, and environmental benefits. The case study is conducted using a modified power system in Korea. In the case study, it is assumed that by integrating solar power generators in a low-demand area and wind power generators in a high-demand area, wind power plants have higher construction costs than solar power generators. However, the transmission costs for wind power plants are lower than those for solar power generators due to the regional characteristics of the power grid in the installation areas because renewable energy in high-demand areas typically requires less additional power infrastructure than in low-demand areas. The results of this study show that the net benefit for the wind power plant is estimated at USD 225.4 M, while the solar power plant yields a net benefit of USD 22.9 M in the example system. These results demonstrate the effectiveness of the proposed method, underscoring its potential to mitigate the challenges faced in the practical implementation of renewable energy projects. [ABSTRACT FROM AUTHOR]

This paper reviews progress towards the establishment of an Association of Southeast Asian Nations (ASEAN) Power Grid (APG) and the key barriers to multilateral cross‐border electricity trade in ASEAN. An analysis across political, technical, institutional, economic, environmental, social and time dimensions is employed. Using a policy sequencing framework, the paper concludes it remains premature for ASEAN to pursue a strong form of power sector market integration on account of the sizeable barriers that currently remain, especially economic and institutional barriers. Focusing on bilateral power purchase agreements and large‐scale investments in solar and wind power over 2022–2030 would help to develop stronger foundations for ASEAN to make steps towards deeper regional integration in the electricity sector in subsequent years, while also being consistent with renewables adoption goals. [ABSTRACT FROM AUTHOR]

Solar energy for water pumping is a possible alternative to conventional electricity and diesel-based pumping systems, particularly given the current electricity shortage and the high cost of diesel. The literature survey includes a comparison between previous studies of pumping systems using photovoltaic cells, and the extent of the influence of external factors such as radiation intensity and temperature on the efficiency of the system. Additionally, the use of water storage to generate electrical energy through potential energy by means of hydraulic generators and the effect of the amount of flow and height on the amount of energy generated, as well as the types of hydraulic generators are discussed in this paper. Nowadays, solar power is a major contributor to the world's electrical energy supply, either by generating electrical energy directly from solar cells or through water storage, which will be covered in this review. When compared to electricity or diesel-powered systems, solar water pumping is more cost-effective for irrigation and water supply in rural, urban, and remote areas. This paper also highlights the challenges that must be overcome to develop high-quality, long-lasting solar power technology for future use. [ABSTRACT FROM AUTHOR]

This review is inspired by the increasing shortage of fresh water in areas of the world, and is written in response to the expanding demand for sustainable technologies due to the prevailing crisis of depleting natural water resources. It focuses on comprehending different solar energy-based technologies. Since the increasing population has resulted in the rising demand for freshwater, desalination installation volume is rapidly increasing globally. Conventional ways of desalination technologies involve the use of fossil fuels to extract thermal energy which imparts adverse impacts on the environment. To lessen the carbon footprint left by energy-intensive desalination processes, the emphasis has shifted to using renewable energy sources to drive desalination systems. The growing interest in combining solar energy with desalination with an emphasis on increasing energy efficiency has been sparked by the rapid advancements in solar energy technology, particularly solar thermal. This review paper aims to reflect various developments in solar thermal desalination technologies and presents prospects of solar energy-based desalination techniques. This paper reviews direct and indirect desalination techniques coupled with solar energy, and goes on to explain recent trends in technologies. This review also summarizes the emerging trends in the field of solar thermal desalination technologies. The use of nanoparticles and photo-thermal materials for localized heating in solar desalination systems has decreased energy consumption and enhanced the efficiency of the system. Solar power combined with emerging processes like membrane distillation (MD) has also a recent resurgence. [ABSTRACT FROM AUTHOR]

The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future. [ABSTRACT FROM AUTHOR]

In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems. [ABSTRACT FROM AUTHOR]

South Africa has committed to reducing its greenhouse emissions by sixty-five percent by 2030 in their National Integrated Energy Plan (NEIP). The lack of investment and development for renewable energy sources put the country on an uncertain trajectory in fulfilling its 2030 energy commitments. At the same time, the country has been labeled as a region with one of the highest solar energy potentials. Provinces such as Mpumalanga and Northern Cape are on opposite ends of the matter, with Northern Cape is one of the leading provinces for renewal energy, while the Mpumalanga province remains the host to eighty-five per cent of the country's coal plants. Solar energy is an abundant renewable energy source and can be assessed using Geographic Information Systems (GIS) techniques. In this paper, the geostatistical technique, Kriging, is employed to predict, estimate, and compare the regional distribution, potential, and variability of annual optimum solar energy (irradiance) between the Mpumalanga Province and Northern Cape Province. Spot-based radiation data are available for solar energy analyses from the GIS Web-based tool Photovoltaic Geographical Information Systems (PVGIS). Kriging was used to estimate the spatial variability of solar energy at an average error of 1.98505% for the Northern Cape Province and 2.32625% for the Mpumalanga Province. It was identified that the Northern Cape receives the highest annual optimum irradiation and has a low overall spatial variation in irradiation over its provincial area. Mpumalanga receives lesser amounts of irradiation but has high overall spatial variation over its provincial area. Most of Northern Cape's central to northwestern regions have the highest annual optimum irradiation ranging from 2583 kWh/m2 to 2638 kWh/m2, while Mpumalanga's highest regions of annual irradiation occur primarily on its western and northwestern parts and ranges in highs of 2345 kWh/m2 to 2583 kWh/m2. [ABSTRACT FROM AUTHOR]

The goal of this review is to offer an all-encompassing evaluation of an integrated solar energy system within the framework of solar energy utilization. This holistic assessment encompasses photovoltaic technologies, solar thermal systems, and energy storage solutions, providing a comprehensive understanding of their interplay and significance. It emphasizes the importance of solar energy as a renewable resource and its role in addressing global energy demand and mitigating climate change. The review highlights the significance of advancements in various solar energy technologies, focusing on their environmental benefits, including greenhouse gas emissions reduction and air and water pollution mitigation. It explores the evolution of photovoltaic technologies, categorizing them into first-, second-, and third-generation photovoltaic cells, and discusses the applications of solar thermal systems such as water heaters, air heaters, and concentrators. The paper examines key advancements in energy storage solutions for solar energy, including battery-based systems, pumped hydro storage, thermal storage, and emerging technologies. It references recent published literature to present findings on energy payback time, carbon footprint, and performance metrics. Challenges to widespread adoption are discussed, including cost and economic viability, intermittency, environmental impacts, and grid integration. Strategies to overcome these challenges, such as cost reduction, policy support, energy storage integration, and sustainable practices, are presented based on published literature. By bridging gaps in existing literature, this comprehensive resource aims to equip researchers, policymakers, and industry professionals with insights into forging a sustainable and renewable energy future. [ABSTRACT FROM AUTHOR]

It is a known fact that fossil fuels will be depleted in the near future owing to the negative effects on the environment. The number of applications using renewable energy sources instead of fossil fuels to obtain energy has increased significantly. It is aimed to provide effective energy production with the proposed rotary energy system (RES) installation for regions with high wind energy and solar energy potential. In the paper, the design, manufacturing process, installation, and output power prediction of a novel RES are presented. In the proposed system, a hybrid system whose energy is derived from solar and wind energy is envisaged. The electrical characteristics of the solar panels with dimensions 140 × 60 × 2.5 mm mounted on the prototype are 6 V 100 mA. The prototype has been tested at different rotation speeds to evaluate the effect of wind energy. Moreover, the output power prediction based on Feedforward Neural Network (FFNN) and Particle Swarm Optimization trained Feedforward Neural Network (PSO-FFNN) has been performed with the data obtained from the prototype system. The three quantitative standard statistical performance evaluation measures, root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil's inequality coefficient (TIC) are employed to compare the performances of these architectures. FFNN architecture, the RMSE, MAPE and TIC values are calculated as 0.0690, 0.0455 and 0.0278, respectively. For the PSO-FFNN architecture, RMSE, MAPE and TIC values are 0.0530, 0.0383, and 0.0213, respectively. It has been proved that it will be produced energy more effectively thanks to the hybrid RES in meeting energy demand. [ABSTRACT FROM AUTHOR]

In this paper, solar irradiance short-term forecasts were performed considering time horizons ranging from 5 min to 30 min, under a 5 min time-step. Global horizontal irradiance (GHI) and direct normal irradiance (DNI) were computed using deep neural networks with 1-dimensional convolutional neural network (CNN-1D), long short-term memory (LSTM), and CNN-LSTM layers on the benchmarking dataset FOLSOM, which is formed by predictors obtained by recursive functions on the clear sky index time series and statistical attributes extracted from images collected by a camera pointed to the zenith, characterizing endogenous and exogenous variables, respectively. To analyze the endogenous predictors influence on the accuracy of the networks, the performance was evaluated for the cases with and without them. This analysis is motivated, to our best knowledge, by the lack of works that cite the FOLSOM dataset using deep learning models, and it is necessary to verify the impact of the endogenous and exogenous predictors in the forecasts results for this specific approach. The accuracy of the networks was evaluated by the metrics mean absolute error (MAE), mean bias error (MBE), root-mean-squared error (RMSE), relative root mean squared error (rRMSE), determination coefficient (R2), and forecast skill (s). The network architectures using isolated CNN-1D and LSTM layers generally performed better. The best accuracy was obtained by the CNN-1D network for a horizon of 10 min ahead reaching an RMSE of 36.24 W/m2, improving 11.15% on this error metric compared to the persistence model. [ABSTRACT FROM AUTHOR]

Summary: Solar photovoltaic (PV) systems are emerging progressively due to their wide compatibility range, ease of installation, and environmental friendliness. The module mismatch (MM) losses and module open circuit (MOC) fault can cause the power mismatch between different PV modules. This significantly affects the energy output from the PV module. Also, the intermittent nature of solar PV power makes the solar PV systems unreliable; to compensate for this, conventional PV systems utilize a battery storage system (BSS) with separate bidirectional converter. These bidirectional converters require two‐stage power conversion and massive battery bank connected at the DC link. This leads to the further reduction in overall efficiency. To address these issues, this paper proposed a multi‐input PV battery system (MIPBS) that uses nonisolated buck and boost converter combinations. It requires single‐stage power conversion to extract maximum power from each PV module along with BSS charging and discharging. Its modular design allows it to function across a wide range of voltage, power, and BSS choices. The proposed system is capable of mitigating the MM loss and sustaining the MOC fault. This paper discusses the operation, steady‐state analysis, and dynamic analysis of the proposed MIPBS. The performance of the proposed MIPBS is validated using a state‐of‐the‐art experimental setup with two PV modules, each having a rating of 60.53 W. [ABSTRACT FROM AUTHOR]

Dryers are utilized in food industry and agriculture in order to extend the useful lifespan of corps. Thermal energy is required for water removal in the process of drying which can be provided by different sources. Solar thermal energy is one of the most applicable sources for drying processes with several benefits such as avoidance of greenhouse gas emission and availability. Regarding the involvement of various factors in the performance of solar dryers, this paper focuses on the works conducted on these systems. In this regard, various types of solar dryers including direct, indirect, mixed-mode and hybrid supplied by solar energy are discussed. According to the outcomes of this review paper, it can be determined that the performance of the solar dryers depends on various parameters such as the type of dryer, solar irradiation, drying time and operating condition. Moreover, it is found that there are several approaches applicable for improving the overall performance of the solar dryers such as utilizing thermal energy storage units, applying solar tracker and using modified materials. Furthermore, there is high potential for integrating the solar dryers with other systems to achieve higher efficiency and reliability. In addition to energy analysis and drying capacity, the solar dryers have been investigated more deeply by applying exergy analysis and the dependency of exergy efficiency on the operating factors are discussed. Environmental analysis conducted on solar dryers reveals high potential of these systems in carbon dioxide mitigation. In addition, the determined payback periods of these systems are acceptable in majority of the investigated cases which shows their advantage in term of economy. [ABSTRACT FROM AUTHOR]

Currently, the industrial sector consumes more than 60% of the energy produced in Mexico, mainly from fossil fuels, causing negative impacts on the environment and human beings. Solar energy helps companies diversify their energy sources, generate savings, and reduce dependence on fossil fuels. Moreover, the environmental impact can be reduced when CO2 emissions are reduced. Nevertheless, in Mexico, less than 3.5% of the electricity comes from solar energy, and along with a lack of information about the technical and social aspects involved in photovoltaic (PV) systems, it is difficult for companies to analyze and evaluate relevant data, and thus make effective decisions based on their needs. As such, companies cannot understand the complete lifecycle of PV systems, and, usually, the economic, environmental, and technical decisions are made only using the installation analysis, which is only one stage in the lifespan of PV systems. This paper proposes an S4 framework with the sensing, smart, sustainable, and social features that small and medium-sized companies must consider to install, operate, and dispose of PV systems, considering the Mexican context. The current literature does not show a complete classification to cover the essential S4 features to describe PV systems, so companies only have partial information when deciding about the installation of PV systems. This framework considers all the needs that may exist during the PV systems' lifecycle, making a detailed evaluation of each of its elements in each lifecycle stage. Consequently, this S4 framework gives a complete guideline allowing companies to decide on PV systems. Finally, this paper presents a case study about a Mexican company that uses the proposed S4 framework to analyze the PV's lifespan. [ABSTRACT FROM AUTHOR]

In this paper, stochastic synchronization of the wind and solar energy using energy storage system based on real-time pricing in the day-ahead market along with taking advantage of the potential of demand response programming has been analyzed. Since renewable energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the demand response program and real-time pricing system. [ABSTRACT FROM AUTHOR]

Energy is essential to sustain life and development and to fulfill daily requirements. A clean source of energy like the Solar Chimney Power Plant (SCPP) is essential or hybridized with conventional sources to meet the demand. Also, it will provide energy during the night and it will reduce the need and expense of physical batteries in comparison to the photovoltaic energy storage system. In this paper, a mathematical study has been done for the generation of 10 MW power from the SCPP. This paper optimized the core parameter of SCPP such as chimney height and collector radius under different operating conditions. It has been evaluated on the basis of data produced by the developed mathematical model. The current research work highlights the modern application of state‐of‐the‐art Deep Learning (DL) techniques that is, Functional Link Convolutional Neural Network (FLCNN) to map developed mathematical models. The proposed FLCNN is compared with various other Machine Learning algorithms including Support Vector Regression, K‐Nearest Neighbors, Random Forest, Naïve Bayes, and Multilayer Perceptron. Proposed FLCNN performed efficiently and achieved 91% and 93% of Adjusted R2 score for predicting values of collector radius and chimney height, respectively, which is comparable to other ML algorithms. [ABSTRACT FROM AUTHOR]

Access to inexpensive, safe, consistent, and clean energy is a critical necessity for all to achieve the SDGs. India's renewable energy (RE) currently accounts for more than a third of the 482 GW of installed capacity and more than 40 percent of power production (including large-scale hydropower). Reforms such as the establishment of a single national power grid have improved access to electricity for people, and the ambitious development of renewable energy, which is the world's third-largest energy generator and third-largest electricity user, has helped in achieving these aims. As a result, the expansion of national targets signifies and reflects the country's optimism and goal for the forthcoming generation. Standardization of the guidelines and development of the stable grid and transmission networks will only enable the country to achieve the ambitious target of 500 GW of green and clean energy by 2030. This paper highlights the important development in the power sector regarding the energy security of India. As well as specifically examining the initiative of NSMs for achieving the 2030 targets, the key challenges, and the way forward to increase the cumulative installed capacity, comprehensive studies of various policies and government initiatives are also discussed. Furthermore, the key challenges usually faced by the developers in the industry, along with the steep decline and rise in the tariffs of solar projects and the previous trends in capacity installation, are also pointed out. This research work also highlights the potential key challenges to achieving the targets, and will thus provide a focus for power developers, policy makers, researchers, and industry practitioners and help with their planning. In the current scenario, the supply of food and the clean energy nexus are required to meet the demands of people's livelihoods. [ABSTRACT FROM AUTHOR]

The demand for on-site production of energy is showing a rapid increase as the trend of decentralisation and energy self-reliance gains momentum. This paper studies and compares three of the main solar energy technologies: photovoltaic, solar thermal panels and hybrid photovoltaic thermal panels. A prototype experimental installation consisting of the aforementioned technologies was set up on the campus of University Politehnica Bucharest. Data were collected over several months, then the instantaneous power production and overall system performance was computed. The system was analysed in four types of weather patterns, and its suitability was assessed in each case. The results show that the performance of PVT panels is closely connected to the dissipation of the thermal energy collected in the thermal storage tank. In addition, PVT collectors can outperform the PV panels in accordance to the thermal energy demand of the end user when used in an installation with suitable dimensions. [ABSTRACT FROM AUTHOR]

A dependable and cost-effective energy distribution scheme is the need in the current scenario of distributed generation of electricity. In the recent past, different economic dispatch techniques are proposed to address this challenge of cost-effectiveness. The fossil fuels issue of pollutant emission and the limited stock has pushed the research community to focus on the direction of renewable energy generation. The aim of this paper is to present a multi-objective cost-emission-based dynamic economic dispatch (MOCEDED) algorithm, which includes thermal, solar, and wind-based power plants. Moreover, the uncertainty of solar and wind power in terms of curtailment is also included in the formulation of the algorithm. The formulated objective function is quadratic constrained programming, which is implemented in General Algebraic Modeling System software. Two paradigms, i.e. only cost-based DED and MOCEDED algorithm, have been analyzed. The outcome of the proposed MOCEDED algorithm shows more promising results than cost-based DED. [ABSTRACT FROM AUTHOR]

The demand for electricity in urban areas of the developing countries like Tanzania is increasing rapidly due to factors such as increasing population, expansion of economic activities and industrialization. Msigani ward is one of the fourteen (14) administrative urban wards in Ubungo Municipal, Dar es Salaam, located at latitude 6.8031 o S and longitude 39.1188 oE. Recently, there has been observed increase in power demand in this ward. This in turn has caused a supply deficit that sometimes led to large number of interruptions caused by frequent power outage and load shedding. The total capacity of the national grid is limited by long-standing drought. Therefore, the study whose results have been presented in this paper, focused on the assessment of renewable energy resources potential comprising solar and wind that can be used in improving electric power availability in urban areas particularly in Msigani ward. Wind speed data and solar insolation level for a long term of the past twelve years (2010-2021) acquired from NASA Power Data Access Viewer were used in this study. The study considered the worst-case scenarios for monthly solar insolation and wind speeds in order to have sustainable energy system at the time of little wind or solar. Homer Pro software was used for simulation and optimization of renewable energy resources. The study revealed that there is enough potential of wind and solar which can generate electricity for Msigani ward and supplement to the national grid. The cost simulation shows that the system can provide a cost of energy (COE) of $0.0399 per kWh ≈ TZS 93.3 per kWh which is lower in comparison to the residential grid tariff of TZS 350/kWh for residential consumers using above 75 kWh per month. [ABSTRACT FROM AUTHOR]

• A new Free-Drift Solar Power Satellite concept is proposed. • Large amount of fuel is saved because of the free-drift orbit and attitude. • Symmetric layout is adopted to reduce attitude disturbance. • Two transmitting antennas are used to reduce the total length of cables. Solar power satellite is known as a promising source of clean energy from space in the future. One of the key problems of current solar power satellite concepts is the huge cost because of the excessively large mass and fuel consumption. The objective of this paper is to propose a new Free-Drift Solar Power Satellite concept that reduce mass and fuel consumption. Firstly, Sun-frozen orbit in geosynchronous Laplace plane and quasi-Sun-pointing attitude are designed to significantly save orbital and attitude control fuel. To this end, an orbit-attitude coupled modelling method considering space perturbations is proposed for multibody systems. Orbital and attitude dynamics and control are studied to substantiate the orbit and attitude design. Secondly, two transmitting antennas and three solar arrays are adopted and allocated elaborately to reduce structural deformations and attitude disturbance. The mass of the transmitting cables from solar arrays to transmitting antennas is also reduced by the proposed layout. Finite element analysis is performed to validate the design of supporting structures. Finally, other subsystems are briefly introduced and the system parameters are summarized. [ABSTRACT FROM AUTHOR]

The stability performance of smart grid power systems is critical and requires special attention. Additionally, the combination of Battery Energy Storage (BES) systems, Solar Photovoltaic (SPV), and wind systems in the intelligent grid model provides utilities with excellent efficiency and dependability. However, a coordination grid with PV and other resources frequently results in severe issues, such as outages or power disruptions. A power outage in the grid might result in a power loss in the delivery system. As a result, the distributed grid model's dependable performance is intended for integrated wind energy, SPV arrays, and BE systems. This paper proposes a renewable intelligent grid model to sustain solar power generation. The model incorporates a boost converter to optimize the performance of solar panels by converting the DC power generated by the panels into AC power for use in the grid. The boost converter is optimized using a novel Horse Herd Optimization Algorithm (HOA) method. In this case, the HOA method is used to optimize the control parameters of the boost converter, such as the duty cycle and the inductor and capacitor values. According to the final results, the proposed method has reduced the Total Harmonic Deformation (THD) and power loss. Additionally, the proposed method outperformed existing strategies related to the Expected Energy Not Supplied (EENS), Loss of Load Probability (LOLP), and Loss of Load Expected (LOLE), indicating the sustainability of power generation. [ABSTRACT FROM AUTHOR]

In the article, the authors present the approach of combining energy production systems, subsystems by obtaining specific advantages, facilitating the efficient conversion of resources subject to advanced energy-thermal treatment, and solutions are proposed for the creation of a "photovoltaic - other combining resources" matrix. obtaining the generational energy mix. The paper describes the endogenous economic growth through the knowledge of hybrid technological processes, the technical and technological changes in the field of energy, the relational implications in the field and advances the proposal of a hybrid energy system based on the mix of geothermal energy resources from closed coal mines) and renewable energy resources solar. Also, the breakdown of energy/geothermal heat sources recovered from galleries and long-front abattoirs as lots of a sub-system of energy production in a hybrid energy system based on the mix of geothermal mining and renewable solar energy resources is reported. The authors conclude that the addition of the "hydrogen production" sub-system to the hybrid energy system based on the mix of fossil and renewable energy resources represents extensive conceptual and operational specific development. [ABSTRACT FROM AUTHOR]

Solar power occupies a significant position among global renewable energy sources due to its abundant energy potential. Consequently, its contribution to electricity generation is steadily increasing. However, obtaining peak efficiency from fixed solar photovoltaic (PV) panels is a formidable task due to their limited ability to consistently tap into solar energy. To tackle this issue and mitigate energy efficiency losses, the utilization of solar tracking systems has emerged as an exceptionally effective solution. These systems enable continuous adjustment of the panels' position to align with the sun's trajectory, optimizing energy absorption and enhancing overall performance. This paper presents the performance and cost analysis of three distinct solar panel tracking systems, namely, a fixed system, a single-axis system, and a dual-axis system. The systems are operated under identical coordinates and conditions. The production data are collected over a period of 15 days for comparative analysis. The tracking movements of the systems are controlled using Arduino. The mechanical components are specifically designed for the establishment of each system. The findings of this study indicate that both single-axis and dual-axis solar tracking systems outperformed fixed systems in terms of power generation. The single-axis system demonstrated a 24.367% increase in power production, while the dual-axis system showed a 32.247% increase compared to the fixed system. Moreover, a cost analysis was carried out considering the installation expenses and power production data of the three systems. It was determined that the single-axis tracking system achieved payback in 0.39 years less compared to the fixed system, while the dual-axis system achieved payback in 1.48 years less compared to the fixed system. Overall, this study underscores the advantages of implementing solar tracking systems, particularly in the single-axis and dual-axis configurations, as they contribute to higher power generation and cost-effectiveness compared to fixed systems. [ABSTRACT FROM AUTHOR]

The application of fuzzy hybrid methods has significantly increased in recent years across various sectors. However, the application of fuzzy hybrid methods for modeling systems or processes, such as fuzzy machine learning, fuzzy simulation, and fuzzy decision-making, has been relatively limited in the energy sector. Moreover, compared to standard methods, the benefits of fuzzy-hybrid methods for capturing complex problems are not adequately explored for the solar energy sector, which is one of the most important renewable energy sources in electric grids. This paper investigates the application of fuzzy hybrid systems in the solar energy sector compared to other sectors through a systematic review of journal articles published from 2012 to 2022. Selection criteria for choosing an appropriate method in each investigated fuzzy hybrid method are also presented and discussed. This study contributes to the existing literature in the solar energy domain by providing a state-of-the-art review of existing fuzzy hybrid techniques to (1) demonstrate their capability for capturing complex problems while overcoming limitations inherent in standard modeling methods, (2) recommend criteria for selecting an appropriate fuzzy hybrid technique for applications in solar energy research, and (3) assess the applicability of fuzzy hybrid techniques for solving practical problems in the solar energy sector. [ABSTRACT FROM AUTHOR]

This research paper presents a detailed review about the recent advances concerned with the heat pipe-evacuated tube solar collectors. The reviewed papers covered various subjects related with this type of the collectors such as enhancement techniques, its structure designing and the methods used to modify it. Moreover, the effects of integration of (CPCs), types of working fluids and using of storage systems on the performance of this collector were reviewed and summarized in different tables. Depending on the reviewed papers, various conclusions and recommendations were outlined to be very useful to the readers and give them a clear road map to produce more future papers about the heat pipe- solar collector systems. [ABSTRACT FROM AUTHOR]

The production of hydrogen is still a major challenge, due to the high costs and often also environmental burdens it generates. It is possible to produce hydrogen in emission-free way: e.g. using a process of electrolysis powered by renewable energy. The paper presents the concept of a research, experimental stand for the storage of renewable energy in the form of hydrogen chemical energy with the measurement methodology. The research involves the use of proton exchange membrane electrolysis technology, which is characterized by high efficiency and flexibility of energy extraction for the process of electrolysis from renewable sources. The system consist of PV panel, PEM electrolyzer, battery, programmable logic controller system and optional a wind turbine. Preliminary experimental tests results have shown that the electrolyzer can produce in average 158.1 cc/min of hydrogen with the average efficiency 69.87%. • Zero-emission hydrogen production is possible. • Hydrogen production using a proton exchange membrane (PEM) electrolyzer for Polish conditions. • Proposal for experimental studies on hydrogen production. [ABSTRACT FROM AUTHOR]