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Combined hydrogen, heat, and power (CHHP) is the cogeneration unit simultaneously meeting hydrogen, heat, and power demands. Due to the low efficiency of the electrolyser, the main grid or other generation units supply its input power. Herein, the photovoltaic farm's (PV farm) output power is used as the input power of the electrolyser. Optimal economic and emission scheduling of CHHP‐based microgrid (CHHP_MG) is defined as an objective function. To improve system efficiency, the scheduling problem is done in two cases, reconfigured SP‐PV farm and conventional SP‐PV farm. To optimal management of both demand and generation sides, a time of use (TOU) based demand response program is employed in the proposed short‐term study. To have a comprehensive study, the optimum solar panel tilt angle is calculated, and its impacts on the main function are analysed. Multi‐objective genetic algorithm is used to solve optimization problems. By solving the objective function, the obtained values for profit and pollution amount are 620$ and 69,245.78 gr, respectively. By applying the load response program, the mentioned values are changed to 715.132$ and 6872.5 gr. Since the input power is supplied through solar panels, the placement angle of the panels is very important and with the optimal setting of the placement angle, the values change to 780$ and 6825.3 gr. The main contributions of this work are optimal reconfigurable PV farm, evaluating the impacts of reconfiguring the efficiency of CHHP, employing the DR program on the scheduling of RSF‐CHHP, and analysing the role of tilt angle in the scheduling of RSF‐CHHP. [ABSTRACT FROM AUTHOR]

Renewable energy stands as a cornerstone for a sustainable future, yet it grapples with a significant obstacle: curtailment—deliberate energy generation reduction to avert grid overload. Curtailment not only squanders clean energy but also poses economic and environmental challenges. This study seeks to enhance the value of renewable energy by proposing a comprehensive methodology to evaluate and mitigate curtailment, with a particular focus on repurposing curtailed energy for ammonia production in Jordan, a pioneering nation in renewable energy adoption. The study unfolds several pivotal contributions to the field: (1) Incorporating curtailment in the capacity factor equation for the first time. (2) Exploring green ammonia potential in Jordan for the first time. (3) Investigating, for the first time, the effects of by-products (specifically oxygen and rare gases) on the investment in green hydrogen and ammonia, utilizing system dynamics (4) Outlining hydrogen applications and highlighting the breakthrough hydrogen energy release optimizer for combustion-free hydrogen extraction. In a scenario where curtailment is restrained (514.1 GWh/year), our findings unveil promising prospects: hydrogen and ammonia can be produced at levelized costs of 2.4 USD/kg and 600 USD/ton, respectively. More significantly, the integration of free energy (curtailed energy) and the synergistic utilization of oxygen and rare gases, notably argon, precipitate a remarkable cost reduction to 1.5 USD/kg and 401 USD/ton for hydrogen and ammonia production, respectively. This remarkable cost reduction underscores a substantial improvement, with the cost of ammonia being USD 219 lower than the average market price (USD 620). We advocate for the adoption of the hydrogen energy release optimizer and strategic utilization of by-products as pivotal strategies to significantly mitigate the costs associated with hydrogen and ammonia production, thereby amplifying the value and viability of renewable energy initiatives. • Mitigation methodology for renewable energy curtailment in green ammonia production. • Integration of curtailment into the capacity factor equation for accurate assessment. • Pioneering exploration of green ammonia potential in Jordan's renewable energy landscape. • System dynamics analysis of by-products (oxygen, rare gases) impact on investment. • Utilizing by-products and deploying HERO for cost-effective hydrogen and ammonia production. [ABSTRACT FROM AUTHOR]

We quantify the effect of dust accumulation at Jezero crater by means of a Dust Correction Factor (DCF) for the solar radiation measured by the photodiodes of the Radiation and Dust Sensor of the Mars 2020 mission. After one Mars Year, dust on the photodiode surface attenuated 25%–30% of the incoming solar radiation. The DCF did not decrease monotonically; we use a model to reproduce its evolution and to derive dust deposition and lifting rates, showing that dust removal is 9 times larger at Jezero crater than at InSight's location in western Elysium Planitia. The model fit obtained using observed opacities is further improved when fed with dust sedimentation rates simulated by a GCM that considers a particle size distrtibution. Projections show seasonal net dust removal, being encouraging for the long‐term survival of solar‐powered missions to Jezero or similarly active dust lifting regions. Plain Language Summary: Dust is ubiquitous in the Martian atmosphere, accumulating on both natural and artificial surfaces. Dust particularly affects the performance and lifetime of missions: the termination of InSight and MER‐B operations are recent examples. Dust accumulation shows a seasonal behavior, and attenuated 25%–30% of the incoming solar radiation on Perseverance after the first Mars Year of the mission. Dust removal is almost 10 times larger than at InSight's location: projections indicate that surfaces at Jezero will be periodically partially cleaned. The estimations of the effect of the accumulated dust as a function of time are encouraging for solar‐powered missions to regions with similar amounts of dust lifting, which might be determined from orbital data on where dust storms originate, dust devils or their tracks are found, or seasonal albedo changes are noted. In addition, the quantification of the effect of accumulated enables future studies requiring more accurate knowledge of incoming solar radiation at the surface. Key Points: We present the evolution of dust accumulation at Jezero crater for more than one Mars YearWe derive dust deposition and removal rates: removal is 9 times more efficient than at the InSight location in western Elysium PlanitiaProjections show that surfaces at Jezero will experience seasonal net dust removal, encouraging solar‐powered missions [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor. [ABSTRACT FROM AUTHOR]

We evaluate the conditions and consequences of union strategies in three industrial transitions in California, all driven by its globally influential climate policies: in construction (solar power plants), electricity (nuclear power retirement), and manufacturing (electric vehicles). Building on recent, global frameworks in environmental labor studies, we grow the field's attention towards unions with workers transitioning between 'sunset' industries like fossil energy and 'sunrise' sectors like clean energy. Using original ethnographic and archival data, we analyze the conditions that shaped union strategies in transitions, and how these in turn impacted union power and coalitions. We argue that union strategies embraced climate transitions when they organized leverage to define a shift on their members' terms, independently from employers, often by turning environmental regulation to the advantage of labor and its allies. [ABSTRACT FROM AUTHOR]

Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting. [ABSTRACT FROM AUTHOR]

In renewable energy-sourced power systems, asynchronous motors serve as elements that both produce and consume electricity. The stability and efficiency of the asynchronous motor in renewable energy system should be considered. Although performance optimization studies have been carried out using power circuits that control the asynchronous motor fed with solar energy and control units such as proportional, integral, derivation (PID), and Fuzzy logic; They may be insufficient to eliminate and reduce vibrations in the output values of an induction motor fed with a renewable energy source. Losses occur as the system performance decreases constantly due to oscillations. In this study, an induction motor power system is optimized with the extra control coefficient (ECCA)-PID, which provides semi-linear control for the first time, unlike the power systems controlled by other linear controllers used so far to reduce oscillations, and the vibration losses are eliminated. The speed oscillations, torque oscillations, and oscillation losses of the induction motor in the system are controlled by conventional PID, and Fuzzy logic controllers in order to demonstrate the effectiveness of the proposed method in simulation and experimentation. Then, the results are compared after the proposed method are examined. As a result of the comparisons, while the motor oscillations and losses in the power system continue throughout the control period in traditional control methods, the proposed method reduces the losses to zero in a large part of the control period. [ABSTRACT FROM AUTHOR]

With the substantial increase in the amount of energy demanded and consumed today, there is also an increase in energy generation from renewable energy sources. Including renewable energy sources in an electrical grid and using them introduces the problem of planning the network in the most economical and efficient way. This thesis addresses the optimal power flow problem, which is one of the power system problems integrated with wind and solar power, both renewable energy sources. The optimal power flow problem is an optimization problem with a nonlinear structure and various constraints, where the best values of control parameters are determined. Additionally, combining the nature of solar and wind energy increases the complexity of the problem. Heuristic search algorithms, which are a type of artificial intelligence technique, are preferred in solving such problems. In this paper, the Triangulation Topology Aggregation Optimizer (TTAO) algorithm was first developed based on distance adequacy balance for the solution of the optimal power flow problem. The developed algorithm was applied to the optimal power flow problem including wind and solar energy sources and compared with the results of different algorithms in the literature. The obtained results clearly show that the proposed algorithm is effective in this power system problem. [ABSTRACT FROM AUTHOR]

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

Off-grid solar technologies have emerged as one of the remedies to Africa's severe lack of access to electricity. Evidence from Ghana, Malawi, Tanzania, and Kenya suggests that these systems positively affect income generation, education, health, and safety and security. Despite its potential, off-grid solar power's technical limitations, combined with concerns about durability and environmental impact, underscore the need for more comprehensive solutions. Addressing Africa's electrification crisis requires a multifaceted approach involving collaboration among various stakeholders to ensure sustainable, reliable, and inclusive energy access. [ABSTRACT FROM AUTHOR]

As both the population and economic output of India continue to grow, so does its demand for electricity. Coupled with an increasing determination to transition to net zero, India has responded to this rising demand by rapidly expanding its installed renewable capacity: an increase of 60% in the last 5 years has been driven largely by a quintupling of installed solar capacity. In this study, we use broad variety of data sources to quantify potential and realized capacity over India from 1979 to 2022. For potential capacity, we identify spatiotemporal patterns in solar, wind, hydro and wave power. We show that solar capacity factor is relatively homogeneous across India, except over the western Himalaya, and is highest during the pre‐monsoon. Wind capacity factor is highest during the summer monsoon, and has high values off the southern coast, along the Western Ghats, and in Gujarat. We argue that wave power could be a useful source of renewable energy for the Andaman and Nicobar Islands, which are not connected to the main Indian power grid. Using gridded estimates of existing installed capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model. We use this model to identify weaknesses in the existing grid—particularly a lack of complementarity between wind and solar production in north India, and vulnerability to high‐deficit generation in the winter. We discuss potential avenues for future renewable investment to counter existing seasonality problems, principally offshore wind and high‐altitude solar. [ABSTRACT FROM AUTHOR]

Energy sources are crucial for the development and growth of economies and civilizations. Solar energy is an alternative energy to generate electrical power. The challenges of solar photovoltaic panels (PV) are the low output power and efficiency and the huge installation area beside PVs need a tracking system for better efficiency. The motivation of this paper is to design an innovative solar sphere system, which is a new concentrated photovoltaic technology that has better performance (efficiency and output power) than the normal conventional solar panel (PV) with a smaller installation area and without any tracking system. This design consists of an acrylic solar sphere entirely filled with cooking oil (sunflower or corn oil) that captures solar radiation and concentrates it on a focal point. The focal point is adjusted over a multi-junction cell that acts as a collector device (concentrator solar cell). This focused solar energy can generate a massive amount of power, which is used to produce more electricity than normal photovoltaic panels. The experiments were carried out in order to discover the best acrylic models or shape designs, which is the sphere, the best materials or media in the sphere, that is oil, the best sphere's size and volume, and that is larger, the best sphere thickness, which at first is lower, the best fluid oil type, which is cooking oil, and finally the best fluid amount or volume inside the sphere, and this is the entire volume. Then, these factors mentioned above are compared with normal photovoltaics (PV) that have the same section area as these shapes. The results revealed that these factors have significant effects on the output power value and efficiency. It has been demonstrated that our innovative concentrated solar sphere system can produce nearly four times the output power or electricity greater than that of a conventional solar panel PV with the same cross-sectional area. This specific sort of compression is crucial because it shows that less space is required to establish this system than it would to install conventional solar panels. The performance of the system per unit of the square area it occupies was compared to the latest generation of flat panel PV available at the market performance; hence, the installation space will be decreased by 40% to 60%. Our system has about twice as much efficiency as solar PV and does not require a tracking system and maintenance. Our technology also has the benefit of not being impacted by extreme temperatures, clouds, dust, and humidity. [ABSTRACT FROM AUTHOR]

The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a modular multiport DC-DC converter for hybrid charging station. The system is supplied by renewable energy sources (RES) like solar photovoltaic system (SPV), wind energy system (WE) and fuel cell (FC). The proposed converter has several benefits like current sharing capability, providing lower current ripple and lower voltage ripple, lower current stress on the switches, lower current rating of switches and using a small-sized inductor. Due to the unavailability of continuous flow of power from the RES throughout the day, an effective energy management system using the adaptive neuro-fuzzy inference system (ANFIS) is implemented in the proposed system. ANFIS controller is implemented to identify and enable the available power from the renewable energy sources to feed the base load as well as charge the batteries in the hybrid charging station. The ANFIS controller continuously monitors the power availability for the charging station, thereby feeding minimum power to the base load. The proposed system is simulated and tested using the MATLAB/SIMULINK software, and a prototype of the same has been developed in the laboratory. From the simulation and experimental results, it is observed that the proposed converter suits best for integrating hybrid sources and used for charging the EVs. Improved life expectancy of the battery is obtained using the proposed converter. The proposed converter produces an efficiency of 97% and is superior to the other existing converters. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become an attractive alternative to IC engine cars due to the increased interest in lowering the consumption of fossil fuels and pollution. This paper presents the design and simulation of a 4 kW solar power-based hybrid EV charging station. With the increasing demand for electric vehicles and the strain they pose on the electrical grid, particularly at fast and superfast charging stations, the development of sustainable and efficient charging infrastructure is crucial. The proposed hybrid charging station integrates solar power and battery energy storage to provide uninterrupted power for EVs, reducing reliance on fossil fuels and minimizing grid overload. The system operates using a three-stage charging strategy, with the PV array, battery bank, and grid electricity ensuring continuous power supply for EVs. Additionally, the system can export surplus solar energy to the grid, reducing the load demand. The paper also discusses the use of MPPT techniques, PV cell modeling, and charge controller algorithms to optimize the performance of the hybrid charging station. [ABSTRACT FROM AUTHOR]

Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup. [ABSTRACT FROM AUTHOR]

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa's ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kW pk /taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country's dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a forecasting system of sunshine radiation for planners to quickly and accurately predict the output of solar power. The field data, including observation time, temperature, relational humidity, wind speed and global radiation, were collected, and the data clusters were embedded in the Excel Database. To improve the computational performance, the data selection technique was used in the stage of data cleaning, data integration and data reduction. Using the Integration of the Radial Basis Function Network (RBFN) and Sliding Mode Control (SMC), a Sliding Mode Radial Basis Function Network (SMRBFN) was proposed to solve this forecasting problem. Since the Sliding Mode Control has the design's sense of optimal parameters, three parameters in the SMRBFN were dynamically adjusted to promote the accurate and reliability of forecasting system. Linking the SMRBFN and Excel database, the learning stage and testing stage of SMRBFN retrieved the input data from Excel Database to perform and analyze the forecasting system. The proposed algorithm was tested on Kaohsiung district in summer and winter. The average prediction error of MAPE and RMSE obtained from the forecasting results are about 9% and 0.223, respectively. It can be proved that SMRBFN can efficiently forecast the sunshine radiation and accurately provide the output of solar power in an uncertainty environment. [ABSTRACT FROM AUTHOR]

In order to increase energy supply reliability, a regulation was issued by the Turkish Energy Market Supervisory Authority. According to this regulation, operation hydroelectric power plants are allowed to add solar power plants with the power calculated according to certain conditions. Herewith will increase electricity generation by utilizing the existing facilities of the operating hydroelectric power plant. Thus, the hydroelectric power plant will be operated more efficiently and effectively. In this study, a real case study on the hybridization of an operating hydroelectric power plant in Turkey with a solar power plant was conducted. The study investigated the amount of energy increase of the power plant, the financing need for additional investment, and the feasibility of this investment. According to the results of the study, the payback period of the additional investment in the Turkish energy market is 4 years and the annual revenue of the hybrid power plant increases by 20%. In conclusion, this study has shown that the hybrid power plant is technically and economically feasible. [ABSTRACT FROM AUTHOR]

The paper presents a solution methodology for a dynamic electricity generation scheduling model to meet hourly load demand by combining power from large-wind farms, solar power using photovoltaic (PV) systems, and thermal generating units. Renewable energy sources reduce the coal consumption and hence reduce the pollutants' emissions. Because of uncertain wind velocity and solar radiation, the share of solar and wind units is restricted to certain limits of demand, considering ramping requirements. The solar and wind units start quickly, while the thermal units start slowly. The commitment to renewable energy units is made through a proposed optimistic optimizer to restrict the power generation share. Economic and environmental objectives of thermal units are unified using a price penalty factor, and the hybrid sine–cosine algorithm (HSCA) optimization method solves the problem. A forward approach is applied to handle the dynamic electricity generation scheduling optimization problem. HSCA incorporates local mutation and hill-climbing heuristics to reduce global operating costs and emission pollutants' while allocating generation to the devoted units. To handle the constraints, replacement and heuristic techniques are applied. The proposed method is verified on a system having solar PV units and wind power plants connected to 6, 13, and 40 thermal generating units, respectively. [ABSTRACT FROM AUTHOR]

Background: The transition towards renewable energy sources has become an imperative step to mitigate climate change, reduce carbon emissions and improve energy security and economic prosperity in a sustainable manner. Maximizing the cost effectiveness of electric power generation is crucial to making renewable energy sources viable and attractive options for clean energy production. The strategic allocation of wind, hydro and solar power systems is essential to achieving this goal. This paper attempts to demonstrate how the cost effectiveness of electrical power system could be maximized through the integration of wind, solar and hydropower systems and comparison at different penetration levels of 0, 25, 50, 75 and 100% on cost effectiveness of electric power generation. The different generator technologies were designed based on their electrical output attributions. Results: The cost of electric generation for the integration of each generator at the various buses were calculated at different penetration level for fair comparison. The results indicate that the minimum money loss for the integration of solar power was $743.90 at bus 4 and at 50% penetration level, the minimum money loss for the integration of wind power was $999.00 at bus 4 and at 25% penetration level while the minimum amount loss for the integration of hydropower was $546.50 at bus 4 and at 75% penetration level. Conclusions: The magnitude to which the integration of the different generator affects the cost effectiveness of power production hinges on the type of generator, the penetration level and the location of the generator in the grid. [ABSTRACT FROM AUTHOR]

The characteristics, distributions, and trends of the aerosol optical depth (AOD) and dust aerosol optical depth (DAOD) of three major concentrating solar power (CSP)-plant project areas (Hami, Turpan, and Ruoqiang) in Xinjiang, China were investigated and analyzed during 1980–2022 using the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis products. The monthly variation, seasonal variation, inter-annual variation, distributions of AOD and DAOD, and proportions of dust in the aerosols in these three CSP-plant project areas were computed and analyzed. Overall, the annual mean AOD at 550 nm in the Turpan project area was the highest (0.20–0.36), while Ruoqiang had the lowest annual mean AOD at 550 nm (0.13–0.30), and the annual mean AOD at 550 nm in Hami was distributed between 0.17 and 0.33. After 2010, the change in the rate of the annual mean AOD showed an overall downward trend in Hami and Ruoqiang, indicating that the atmospheric environmental changes in both areas were more favorable for the operation of CSP plants. In the project areas of Hami, Turpan, and Ruoqiang, more than 90% of the AOD values were mainly in ranges 0.10–0.30, 0.10–0.35, and 0.05–0.30, respectively. As expected, the AOD values in spring and summer were significantly higher than those in autumn and winter in the three study areas. In spring, the dust contents (i.e., ratios of DAOD to AOD) were the highest, accounting for 64% (Hami), 67% (Turpan), and 69% (Ruoqiang) of the total aerosol contents. In all three areas, the proportions of dust in aerosols in spring have shown an increasing trend since 2000, suggesting that the negative impact of the dust on the power generation efficiency in these areas has gradually been increasing. Therefore, it is recommended that the CSP plants in Hami, Turpan, and Ruoqiang develop a strategy for cleaning heliostats, especially in spring, to reduce the impact of dust adhesion on the efficiency of the CSP plants. [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]

Mosquitoes are carriers of diseases like malaria, dengue fever, chikungunya, zika fever, and lymphatic filariasis, which can be deadly in Tropical Africa and certain subtropical regions. The World Health Organization recognizes mosquito bites as the primary cause of over a million deaths annually due to malaria. It is crucial to reduce or eliminate the mosquito population in Africa if possible. Presently, malaria vector control methods mainly involve using insecticide-treated bed nets, long-lasting insecticidal nets, or indoor residual spraying. However, many existing mosquitos control products have not proven to be effective in eradicating mosquitoes in these regions. In this study, a solar-powered mosquito killer with voice activation was developed using an Arduino board, an ultrasonic sensor, an electric fence driver, a direct current (DC) fan, and UV lights. The Ultraviolet (UV) lights are visible to some insects and enhance the trapping effect by combining heat and carbon dioxide. The device has voice activation to minimize human contact. It is an improvement over previous methods that utilized voiceactivated equipment without harmful elements or irritating insecticides. The device mimics a mosquito's sensory receptors to attract and ultimately eliminate them. The equipment includes a 5V DC fan operating at 1800-3700 rev/min, a mist humidifier emitting mist at regular intervals (operating frequency of 0.8MHz - 1.75MHz), an electric fence producing over 1000 volts to render mosquitoes lifeless upon contact, and a voice module for device control. The device is powered by an external outlet power cord supplying 5-12 volts to activate the micro-controller. [ABSTRACT FROM AUTHOR]

This study investigated a new solar-powered and ground-source cooling organic Rankine cycle (ORC) system. In the analysis of the ORC system, three different working fluids, namely toluene, cyclohexane, and isopentane, were used, as well as therminol 66 as thermal oil in the solar energy cycle. The difference in the designed system lies in the technique of cooling the heat generated in the condenser part with soil. Moreover, system efficiency was investigated for condenser temperature, collector surface area, soil temperature, and different radiation values. The variation of condenser temperature with three different working fluids was evaluated for two different evaporator pressures. As a result, the ORC system efficiency increased at a lower condenser temperature. When the condenser temperature was reduced from 60 to 30 °C, the ORC efficiency increased for cyclohexane, toluene, and isopentane fluids in the following order: 0.251 → 0.293, 0.2798 → 0.3183 and 0.1707 → 0.2148. In the last part of the study, hourly analysis was made for the days of the 15th of January and the 15th of June in a selected region using meteorological data. The ORC efficiency (η orc) was calculated as 0.2997 and 0.2808 for these days, respectively. [ABSTRACT FROM AUTHOR]

Global energy crises, limited energy resources and damage to nature in energy production have pushed humanity to use energy more efficiently and renewable energy sources. In this context, the importance of implementing energy efficiency and sustainable strategies in buildings that consumes high energy has increased. In this study, the effect of luminaires substitution with more efficient ones and electricity production by renewable energy sources on efficiency, sustainability and environment was examined on a sample education building. The calculations were made according to standards and equipment supplier data. As a result of this study, it has been found that the use of high-efficiency LED lighting instead of halogen or fluorescence luminaires in the engineering faculty building and laboratory of Hitit University, will reduce electricity consumption by 68.2%. In addition, it has been deduced that the solar energy system to be installed on the faculty's roof, will significantly reduce CO2 emission, and has a payback period of 5.5 years. In terms of gas emissions, a roof-mounted solar energy system is 21, 45, and 32 times less hazardous to the environment than natural gas, lignite, and fuel oil, respectively. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as solar energy have great potential to alleviate some of the negative environmental problems, including climate change, caused by intensive fossil fuel use. Solar energy will play an important part in future energy systems because of its rapid installation technology and lowering costs. In order to use solar radiation intensity in a wide range in the most efficient way, the plants must either be at an optimum angle in the area where they are installed or they should be designed with moving systems. In this study, performance and efficiency comparisons of solar tracking systems and fixed-designed systems based on real-field conditions have been made. In this way, price performance ratios can be determined clearly and it will be easier to decide on the types of power plants to be built. While solar tracking systems move according to the most efficient position that they can get from the sun through radiation sensors, they are designed with elements such as PLCs using multi-year solar data that operate with an open loop and based on meteorological data. In this study, three different power plant types designed as 1-axis, 2-axis, and fixed angle were controlled as open loop with PLC software and production differences between power plants were evaluated for 1 year. According to this research we have done, it has been concluded that 2-axis tracking systems produce approximately 32% more energy annually than a fixed-angle system, with an annual efficiency rate of 23% higher than a 1-axis system. [ABSTRACT FROM AUTHOR]

This paper proposes a fully distributed scheme to solve the day‐ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud‐fog‐based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud‐fog‐based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small‐scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24‐h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exergetic analysis, facilitated by computational modeling, of a solar-powered air conditioning system directly linked to a photovoltaic generator. The system, designed without an energy storage component, aims at addressing the cooling requirements of a library by harnessing the natural regulation of solar energy, under two distinct solar irradiance profiles. Simulations revealed that the photovoltaic generator necessitates a minimum solar incidence of 240 W/m² to fulfill the basal cooling capacity of the system. The results indicated that only 25% of the library's operational period on a sunny day, and a mere 12.3% on a cloudy day, met the thermal comfort standards for occupants. Furthermore, the study discerned an increase in exergy destruction in the system components over the course of the day. The photovoltaic system exhibited the highest level of exergy destruction, followed by the compressor, condenser, evaporator, and expansion valve respectively. This exergy analysis offers a holistic evaluation of the system's overall efficiency. It transcends mere energy efficiencies and incorporates power quality losses that transpire during the conversion and transport processes. With this indepth assessment, the study not only identifies the areas of greatest energy losses but also offers insights that could enhance the system's design and operational efficiency. [ABSTRACT FROM AUTHOR]

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain. [ABSTRACT FROM AUTHOR]

Aspiring to India's COP26 pledge of attaining 500 gigawatts of non-fossil energy capacity by 2030 will entail nothing short of a colossal transformation of rural spaces. Solar park development has already disrupted the lives and livelihoods of marginalized peasants through land dispossession, uneven provisioning of electricity and water resources, dislocation of fuelwood and grazing access, and the diminution of labor opportunities. However, it remains unclear how affected peasants internalize the burdens of decarbonization. The aim of the paper is to address the following questions: How are the geographies of solar energy transitions embodied? How do dispossessed peasants respond affectively? Drawing on feminist political ecology literature and fieldwork that included household surveys and semi-structured interviews, the author finds that the dispossessed embody a range of affective responses, including emotional geographies and embodied resistance, that enable or constrain the place-making of equitable alternative sites of solar energy generation. The author concludes that as India rapidly erects large-scale solar infrastructures—indisputably imperative efforts to confront the climate crisis—the embodiment of injustices suffered by the dispossessed will haunt low-carbon futures. [ABSTRACT FROM AUTHOR]

Modeling of photovoltaic (PV) water pumping system for rural water localities is vital especially in these modern days, in Tanzania. This is due to the rise of high number of solar-powered rural water projects constructed lacking appropriate design. The fate result into poor and substandard projects delivering below expectations. Kikombo Solar Water Project situated in Dodoma; Tanzania is one among poorly designed solar project chosen as a case study to validate proposed model. The main problem of Kikombo project is unsatisfactory amount of water from domestic points to satisfy population demand. This matter has been raised by pumping-station attendants and the Kikombo village community. The Kikombo water project can only deliver 14.21 m 3 /day while required demand is 50 m 3 /day. In order to solve the existing problem, solar water pumping model was developed. It consists of PV array, a motor-pump (AC), inverters (DC–AC converter), maximum power point tracker (MPPT) integrated with a controller and a storage water tank. Analysis was done in MATLAB to estimate which parameters should be upgraded to meet the water requirements. Moreover, the input and output variable for each model component were identified and simulated in MATLAB software. Finally, PV model was validated and system capacity improvement to rectify designs limitation for Kikombo is proposed. The model results were validated through real measurement data at Kikombo water pumping station. The differential error of 0.04062 was observed as a system error. PV model displayed an error of 0.0509 for power output, 0.0383 for current and 0.0131 for voltage; inverter displayed error of 0.0686 and 0.0406 for motor pump. In order to acquire sufficient water demand of 50 m 3 /day, the PV panel and pump capacities should be increased to 8.82 kW and 7.35 kW respectively from the existing PV panel’s power of 2.5 kW and motor-pump of 4 kW. [ABSTRACT FROM AUTHOR]

In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power's dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model's strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field. [ABSTRACT FROM AUTHOR]

The main goal of this work is of developing a control approach, which is able to obtain the smooth switching between energy sources, battery, and Supercapacitor (SCAP). With four separate math functions, a new math function-based (MFB) controller is designed, and this MFB will generate four output signals corresponding to the motor's speed. Further, the MFB is combined with an FLC/PI controller to reach the theme of the work. Two-hybrid different controllers are intended as per the proposed control strategy termed as MFB with FLC and MFB with PI and both are implemented individually in MATLAB/Simulink in four different modes. The entire model is implemented including a solar panel to charge the battery, this solar panel (SP) is connected to the battery and UDC through various control switches. Finally, a comparative analysis is made between two hybrid controllers to know the better-performed controller. [ABSTRACT FROM AUTHOR]

The power generation of the photovoltaic plant is related to the cleanliness of the photovoltaic modules. The accumulation of natural dust is the main source of pollution, which is affected by human activities and meteorological factors such as temperature, humidity, wind speed, and rainfall concentration in the current region. On the basis of particle swarm optimization (PSO) and the least‐squares support vector machine (LSSVM), the density of dust on photovoltaic modules was estimated. The authors proposed that the inertia weight decreased as a concave function to improve the efficiency of optimization and let the two positive constants change dynamically to improve convergence performance. The dust accumulation prediction model was established considering natural rainfall and the authors obtained the attenuation rate of the photovoltaic power output. Finally, the experiments in Hangzhou showed that the model can predict the density of accumulated dust quickly, which provides a theory for predicting PV power generation and managing the cleaning frequency. [ABSTRACT FROM AUTHOR]

The energy demand generated by fossil fuels is increasing day by day, and it has drastically increased after the COVID-19 pandemic as industries and household utilities rejuvenate. Renewable sources are thus becoming more essential as easily available, alternative methods of low-cost energy generation. Among these renewables, solar energy, i.e., solar power, is a promising energy source and can be used for solar-based H2 evolution because H2 technology is a leading source of eco-friendly electricity generation, and most of the worldwide efforts to develop this method involve heterogeneous catalysis for H2 evolution via water splitting and its storage, i.e., using a fuel cell. In the current scenario, there is a need to develop a stable, recyclable, and reusable heterogeneous catalyst system, which is a great challenge. In the current study, we have focused on novel ferrite magnetic nanomaterials for recyclable and reusable robust photocatalysis. Moreover, discussions of the factors contributing to the photocatalytic hydrogen evolution, low-cost synthesis techniques, and prospects for making them ideal photocatalysts are uncommon in the literature. The study will impart possible approaches for the design and development of novel ferrite nanomaterials and their nanocomposites for H2 generation in the forthcoming years. [ABSTRACT FROM AUTHOR]

The expected increase of hydrogen fuel cell vehicles has motivated the emergence of a significant number of studies on Hydrogen Refuelling Stations (HRS). Some of the main HRS topics are sizing, location, design optimization, and optimal operation. On-site green HRS, where hydrogen is produced locally from green renewable energy sources, have received special attention due to their contribution to decarbonization. This kind of HRS are complex systems whose hydraulic and electric linked topologies include renewable energy sources, electrolyzers, buffer hydrogen tanks, compressors and batteries, among other components. This paper develops a linear model of a real on-site green HRS that is set to be built in Zaragoza, Spain. This plant can produce hydrogen either from solar energy or from the utility grid and is designed for three different types of services: light-duty and heavy-duty fuel cell vehicles and gas containers. In the literature, there is a lack of online control solutions developed for HRS, even more in the form of optimal online control. Hence, for the HRS operation, a Model Predictive Controller (MPC) is designed to solve a weighted multi-objective online optimization problem taking into account the plant dynamics and constraints as well as the disturbances prediction. Performance is analysed throughout 210 individual month-long simulations and the effect of the multi-objective weighting, prediction horizon, and hydrogen selling price is discussed. With the simulation results, this work shows the suitability of MPC for HRS control and its significant economic advantage compared to the rule-based control solution. In all simulations, the MPC operation fulfils all required services. Moreover, results show that a seven-day prediction horizon can improve profits by 57% relative to a one-day prediction horizon; that the battery is under-sized; or that the MPC operation strategy is more resolutive for low hydrogen selling prices. [Display omitted] • Model Predictive control application for green hydrogen refuelling station. • Linear modelling of green hydrogen refuelling station. • Parameter sensitivity analysis of the multi-objective function. • Prediction horizon sensitivity analysis. • Model Predictive Control performance advantage compared to rule-based control. [ABSTRACT FROM AUTHOR]

Among developing countries in Asia, Indonesia has realized the importance of transitioning from fossil fuels to renewable energy sources such as solar power. Careful consideration must be given to the strategic placement of solar power installations to fully leverage the benefits of solar energy. This study proposes a methodology to optimize the site selection of solar power plants in Indonesia by integrating Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (F-MARCOS) models. The proposed methodology considers quantitative and qualitative criteria to evaluate potential locations for solar power plants. In the first stage, DEA is used to identify the most efficient locations based on quantitative measures such as solar radiation, land availability, and grid connectivity. In the second stage, qualitative factors such as technological, economic, environmental, and socio-political aspects are evaluated using F-AHP to prioritize the most important criteria for site selection. Finally, F-MARCOS ranks potential locations based on the selected criteria. The methodology was tested using data from Indonesia as a case study. The results show that the proposed hybrid model optimizes Indonesia's solar power plant site selection. The optimal locations can contribute to a cost-effective long-term renewable energy supply nationwide. The findings from this study are relevant to policymakers, industry stakeholders, and researchers interested in renewable energy development and site selection. However, to promote sustainable solar energy development, governments and local authorities must also enact supportive policies and mechanisms that encourage the adoption and growth of renewable energy technologies in Indonesia. [ABSTRACT FROM AUTHOR]

Managing microgrids (MGs) with variable renewable energy (VRE) is challenging because of uncertainties of electricity production, loads, and energy price, so we need flexible control strategies for battery energy storage system (BESS) to handle those challenges. Model‐based approaches require precise models of the MG to give accurate results but having an accurate model can be difficult in continually changing environments. We introduced a new day‐ahead optimization method to control BESS scheduling and power exchange between the utility grid and an MG with a load, photovoltaics, and BESS with the aim of energy cost minimization. Deep reinforcement learning (DRL) was used for the optimization of sequential actions of BESS over a time horizon. A theoretical optimum scheduling was derived using a linear programming optimization to be compared with the DRL agent. Both no‐battery and greedy control algorithms were used as baselines. It was shown that the results of the proposed technique were better than the baselines through numerical simulations using whole year's data. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

• Induced vibrations deteriorate the performance of solar Photo-Voltaic module. • Vibrations were recorded and analyzed for different locations near metro. • Recorded vibration levels were compared with FTA limits. • Vibration measurement results were in good correlation with the literature.Induced vibrations and PV modules' natural frequencies fall in the same range. Structures in the vicinity of elevated, surface and subway metro trains are subjected to induced vibrations generated due to train movements. Nowadays PV (Photo-Voltaic) modules are heavily installed in the vicinity of metro rail. These metro vibrations are induced in PV modules mounted on adjacent structures, which accelerates the formation of cracks and micro-cracks in PV cells. PV modules show a detrimental effect on their performance and life due to these cracks. Therefore, to understand the vibration levels, this study aims to find out the frequency content and amplitude of vibrations at different locations in the metro vicinity. Vibrations are recorded at locations such as metro tracks, stations (elevated and ground), and adjacent buildings/structures where solar PV systems are installed. Vibrations induced in the nearby structures show peak velocity amplitudes in the frequency range of 20–250 Hz. In conclusion, the vibrations in the vicinity of the metro train are below the FTA limits. However, PV modules' natural frequencies and induced vibrations fall in the same frequency range. This study shows that even when the induced vibrations are below the FTA limit, they can still cause a damaging effect on the PV modules, hence degrading solar energy generation. [ABSTRACT FROM AUTHOR]

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses. [ABSTRACT FROM AUTHOR]

To satisfy the energy demand from renewable sources, accurate weather predictions are necessary. The Analog ensemble (AnEn) technique aims to correct a (weather) prediction given historical observational data. In this work, the AnEn is applied to the wind speed and solar radiation predictions used in the AQMEII multi-model ensemble, spanning a whole year, to produce probabilistic forecasts over Europe. The skill of each deterministic model in forecasting the wind speed, the solar radiation and the respective renewable energy potential is compared to the skill of the AnEn as well as to the skill of the multi-model ensemble mean, either unconstrained (mme) or analytically optimized (mmeW). Results show that the AnEn significantly improves the wind (radiation) forecast skill of the numerical models in the range 25–43% (13–24%), being larger for moderate or low skill models. Compared to mme, the AnEn improvement is larger across all quartiles except the upper one. AnEn and mme are mostly comparable with the mmeW at intermediate values of wind speed and solar radiation. At higher values, the AnEn should benefit from additional auxiliary inputs and a larger dataset. A hybrid model combining the advantages of AnEn and mmeW and providing even more accurate forecasts is proposed. [ABSTRACT FROM AUTHOR]

Over the past few decades, consumers have become more and more interested in electric automobiles. As a result, there is an increase in demand for ecologically friendly and more effective charging stations for electric vehicles (EVs). The energy required to charge an EV comes from fossil fuels, despite the fact that EVs are promoted as CO2 emission-free alternative. There have been initiatives to enhance the effectiveness and ecology of solar-powered electric vehicle charging infrastructure. This paper describes the use of a photovoltaic charging station with G2V and V2G solutions for EVs. The proposed model is developed and implemented using MATLAB/Simulink. There are various input parameters and the results are determined. [ABSTRACT FROM AUTHOR]

In this study, a cogeneration cycle in a time-transient state is investigated and optimized. A quasi-equilibrium state is assumed because of the small time increments. Air temperature and solar power are calculated hourly. The cycle is considered in terms of energy, exergy, and economic and environmental analyses. Increasing the net present value (the difference between the present value of the cash inflows and outflows over a period of time) and reducing exergy destruction are selected as two optimization objective functions. The net present value is calculated for the period of 20 years of operation according to the operation parameters. The optimization variables are selected in such a way that one important variable is selected from each system. To optimize the cycle, the particle swarm optimization method is used. The number of particles used in this method is calculated using the trial-and-error method. This cycle is optimized using 13 particles and 42 iterations. After optimization, the energy efficiency increased by 0.5%, the exergy efficiency increased by 0.25%, and the exergy destruction decreased by 1% compared to the cycle with existing parameters. [ABSTRACT FROM AUTHOR]

An adaptive driver motor was developed to use in PV panel cleaning systems in this study. The amount of energy produced from PV panels is directly related to parameters such as the rate and angle of solar irradiance falling on the panel. Covering the surface of PV panels with dust, dirt, etc., significantly reduces the amount of energy. A lot of research/development is being done with panel cleaning. In this study, a Linear Brushless Direct Current (BLDC) motor was designed as an alternative drive element to be used in the methods used for cleaning the panels. In this study, analytical design and electromagnetic and structural analyses of linear BLDC were performed. The motor prototype was produced, and appropriate driver was selected and then mounted on the motor test setup. The test results on the system have shown that the designed motor and the system can be used for panel cleaning. Depending on this, cleaning technic produced energy was increased by 22%. Most of the external systems used either create an additional load on the panels or need an additional motor or reducer for linear motion. The unique aspect of the work is that it can provide linear motion for the cleaning equipment without additional load on the panels. In addition, due to the large thrust produced by the motor, it will be possible to clean the panels placed on the same axis with a single motor in successive panel series. [ABSTRACT FROM AUTHOR]

Solar chimney power plant (SCPP) technology is considered among the most viable ways to exploit solar energy on a large scale. This technology possesses several advantages, including low operation and maintenance costs, high reliability, low environmental impact, and a long lifetime. A novel configuration of the solar chimney system is proposed in this work by the integration of transparent PV cells coupled with water desalination. Transparent PV cells were incorporated within this configuration to enhance total electricity production. ANSYS Fluent software was used as a simulation tool for the steady flow and the natural convection problem within the solar chimney. A complete parametric study was also performed for conventional SCPP integrated with transparent PV to evaluate the effect of different parameters on air velocity, static pressure, mass flow rate, air temperature inside the chimney, and turbine power output under 850 W/m2 solar radiation. The effect of radiation on the turbine and PV power output was investigated. The results of the present model showed that the net output power and the freshwater production for the hybrid system could reach up to 261 kW and 8.867 kg/s, respectively. • Hybrid solar chimney-PV power plant is reported and simulated using ANSYS Fluent software. • The simulations showed the viability to use solar energy and produce fresh water. • High power and enhanced efficiency were reported as opposed to conventional systems. [ABSTRACT FROM AUTHOR]

The Galapagos Islands have been declared a World Heritage site due to their unique biodiversity, which makes them a living museum and a natural laboratory for humankind. However, to fulfill the energy needs of its habitants and foreign visitors, the islands have depended on fossil fuel energies that have produced levels of lead and chemical agents that are affecting the islands' air quality, flora, and fauna. Therefore, zero-carbon initiatives have been created to protect the islands, wherein solar and wind power plants have been studied as reliable alternatives. In this way, Geographical Information Systems based on Multicriteria Decision Methods constitute a methodology that minimizes the destruction and disturbance of nature in order to assess the best location for the implementation of these alternative energy sources. Therefore, by exploring the geographical information along with the Analytical Hierarchical Processes and the Ordered Weighted Average methods, it was possible to identify the potential for solar power plants of 10 MW on each island; likewise, for wind power plants, it was found that the islands possess implementation potential that has been analyzed in the field, showing that the best location is on Baltra Island, but is not limited to it. [ABSTRACT FROM AUTHOR]

Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important dispersion between the actors that we have observed. [ABSTRACT FROM AUTHOR]