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Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system's performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory's load demand. Moreover, the system's performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H2. Additionally, the cost per kilogram of hydrogen (kg H2) is considerably lower when the WT is used, at USD 0.55/kg H2, while it rises to USD 1.5/kg H2 when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Sensitivity to ambient air temperatures, consuming a large amount of fuel, and wasting a significant amount of heat dumped into the ambient atmosphere are three major challenges facing gas turbine power plants. This study was conducted to simultaneously solve all three aforementioned GT problems using solar energy and introducing a new configuration that consists of solar preheating and inlet-air-cooling systems. In this study, air was preheated at a combustion chamber inlet using parabolic trough collectors. Then, inlet air to the compressor was cooled by these collectors by operating an absorption cooling cycle. At the design point conditions, this novel proposed integration resulted in a 6.87% relative increase in generated power and a 10.53% relative decrement in fuel consumption, achieving a 19.45% relative increment in the plant's thermal efficiency. This was accompanied by a reduction of 0.026 kg/s, 4.2 kg/s, and 0.278 kg/s in CO2, CO, and NOx emissions, respectively. Finally, spider diagrams were employed to assess the impact of the operating parameters on the overall system's performance and its associated environmental implications. [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]

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]

Climate concerns require immediate actions to reduce the global average temperature increase. Renewable electricity and renewable energy-based fuels and chemicals are crucial for progressive de-fossilization. Hydrogen will be part of the solution. The main issues to be considered are the growing market for H2 and the "green" feedstock and energy that should be used to produce H2. The electrolysis of water using surplus renewable energy is considered an important development. Alternative H2 production routes should be using "green" feedstock to replace fossil fuels. We firstly investigated these alternative routes through using bio-based methanol or ethanol or ammonia from digesting agro-industrial or domestic waste. The catalytic conversion of CH4 to C and H2 was examined as a possible option for decarbonizing the natural gas grid. Secondly, water splitting by reversible redox reactions was examined, but using a renewable energy supply was deemed necessary. The application of renewable heat or power was therefore investigated, with a special focus on using concentrated solar tower (CST) technology. We finally assessed valorization data to provide a tentative view of the scale-up potential and economic aspects of the systems and determine the needs for future research and developments. [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]

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners' net income by 1.25 times compared to merely installing charging infrastructure. [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]

Due to the surge in oil prices, alternative energy sources, like solar power, are being explored to meet energy demands. Solar power is utilized in various industries, including agriculture. In Iraq and other developed countries, solar power is actively being developed due to the abundance of solar radiation. In agriculture, solar standalone pump systems with variable-speed drives are used. Electric motors operate at different speeds depending on the availability of sunlight. Inverters convert this solar energy from direct current to alternating current, enabling the powering of motors with a fixed voltage per frequency ratio and regulating motor current consumption. The variation in motor speed affects hydraulic pump efficiency and water productivity, making it crucial to optimize solar energy utilization in agriculture. The angle of inclination greatly affects the effectiveness of solar panels. The optimal tilt angle depends on location, latitude, season, and time of day. Adjusting this angle based on these factors maximizes power output. Innovative installation methods are being employed to enhance the benefits of solar power, reduce costs, and optimize sunlight capture. These methods drive sustainable development in various industries, including agriculture. [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]

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to pursue clean, low-carbon, safe, and efficient energy utilization and accelerate the development of new energy, sustainability is the necessary research. In recent decades, solar power generation has rapidly formed and been widely applied. Sustainability analysis is a key aspect that directly affects the construction of solar power projects when conducting solar power generation construction. This paper attempts to start with life cycle sustainability assessment (LCSA) and study the status quo of its three pillars (These three pillars include life cycle assessment, life cycle cost assessment, and social life cycle assessment) in the field of solar power generation. At the same time, the externality indicators are considered between pillars. In addition, the application of uncertainty analysis was studied during the analysis process to address the impact of various random factors. The conclusion shows that further research is needed to support this transition by integrating life cycle assessment, life cycle cost, and social life cycle assessment into LCSA for the evaluation. To improve the effectiveness of future research, studies should focus on fulfilling major data gaps in the literature such as the lack of detailed documentation for specific materials and background process choices in life cycle assessment databases. The development of solar power generation can be an important alternative in efforts to decrease climate change impacts and pursue cleaner energy sources in countries where solar energy is more easily available by integrating life cycle assessment (LCA), life cycle cost (LCC), and social life cycle assessment (SLCA) into LCSA. The sustainable development of the solar power generation industry in terms of multiple aspects is supported. [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]

This study addresses whether the main barrier to private solar/wind investment is the network side instead of the generation side, as a hypothesis, and how the network-related barrier could be reduced to encourage a more extensive range of private investment. It mainly employs a review of the literature and semi-structured interviews with relevant stakeholders. The result showed weak grid capacity is a critical barrier in solar power projects' congested areas. Another critical barrier is policy uncertainty in that the government has not issued any alternative mechanisms for developers who failed to meet the Commercial Operation Date for approximately two years after the FITs ended. This is likely due to the fact that the Power Development Plan 8 (PDP8) for 2021-30 had been slow to be approved by the Prime Minister. In the absence of policies, the government committed to net-zero emission toward 2050 at COP26 and concluded the Just Energy Transition Partnership agreement in December 2022. These might lead the government to set its ambitious RE targets in the power mix of PDP8 approved in May 2023. In addition, amendments of the Law on Electricity which allows private firms to invest in the grid may contribute to improving quality and capacity of the grid. [ABSTRACT FROM AUTHOR]

Battery storage is an important factor for power systems made up of renewable energy sources. Technologies for battery storage are crucial to accelerating the transition from fossil fuels to renewable energy. Between responding to electricity demand and using renewable energy sources, battery storage devices will become increasingly important. The aim of this study is to examine how battery storage affects a power system consisting of solar and hydroelectric energy and to draw conclusions about whether energy storage recommends a power system. The method involves designing a model of eight real cascade hydropower power plants and solving an optimization problem. This power system model is based on existing hydroelectric power plants powered by solar energy and batteries in the Turkish cities of Yozgat and Tokat. A case study with four different battery capacities in the system was carried out to assess the implications of energy storage in the power system. The stochastic nonlinear optimization problem was modeled for 72 h and solved with the MATLAB programming tool. The stochastic Quasi-Newton method performs very well in hybrid renewable problems arising from large-scale machine learning. When solar energy and batteries were added to the system, the maximum installed wind power was found to be 2 MW and 3.6 MW, respectively. In terms of profit and hydropower planning, a medium-proportion battery was found to be the most suitable. Increased variability in hydropower generation results from the installation of an energy storage system. [ABSTRACT FROM AUTHOR]

Water scarcity is currently one of the world's major issues. Water treatment technologies are a solution to the water crisis problems. In this study, the outcomes of pre-pilot scale testing of solar-powered Ion-exchange resin technology is presented. The tests were carried out using industrial wastewater at the Kungrad Soda Plant located in Kungrad, Uzbekistan. From the plant, about 1500 m3/day of waste water containing total dissolved solids (TDS) is discharged into the environment. In order to reduce the negative impact on the environment and to reuse waste water, the factory proposed conducting water purification tests from unwanted ions (Ca2+, Mg2+, Cl−, SO42−, dissolved CO2). During the test of the technology, water with a TDS of about 2000 ppm was passed through the ion-exchange resin and clean water of around 30 ppm was obtained (purify up to 98–99%). However, according to the requirement of the plant, a certain amount of daily water is purified and added to the total water, and no more than 1600 ppm water is produced and sent for reuse. Experiments have been successfully carried out on a pre-pilot scale using this technology. [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 share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio's sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant's optimal configuration. [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]

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies. [ABSTRACT FROM AUTHOR]

Buildings are significant contributors to climate change through their energy consumption, particularly in countries with hot and dry climates where cooling services require substantial amounts of energy. Climate change is expected to increase cooling demand, further exacerbating the problem. This study investigates the impact of climate change on cooling demands in different building types in Qatar and evaluates related environmental impacts. Using a high-resolution regional climate model, future climate data were obtained for 2071 and 2100 under the Representative Concentration Pathway (RCP) 4.5. The energy consumption of eight prototype buildings was simulated under baseline and future climates, and the environmental impacts were assessed using Life Cycle Assessment (LCA) for natural gas and solar power as energy sources. The study found that the cooling demand could increase by 13–53% and 19–67% in 2071 and 2100, respectively, with buildings having higher thermal insulation and lower window-to-wall ratio being less affected by climate change. The LCA results showed that replacing natural gas with solar power can reduce cooling energy-associated CO2 emissions by 92%. However, challenges such as human toxicity and metal depletion need to be addressed. The study highlights the importance of considering potential climate change scenarios to develop more resilient buildings and encourages implementing efficient recycling and waste management strategies before implementing PV panels. [ABSTRACT FROM AUTHOR]

In the step-by-step roadmap for limiting and eliminating power sources that use fossil fuel, especially coal-fired power, as well as for setting the Vietnamese government targets for developing renewable energy for replacement, solar farms and rooftop solar power, followed by floating photovoltaic (PV) power, are considered the best candidates for meeting the goals of land space, PV system operation efficiency, and meeting environmental goals. In addition to the rapid development of PV cell technology are the government's incentives and financial support for PV plants on the water surface area, which is still very large. Floating PV plants will strongly attract investors in the near future. This study could help stakeholders in the market understand the economic–technical aspects from analyzing economic–financial indicators of floating PV plants with a capacity of 47.5 MW connected to the national utility grid 110 kV at Da Mi hydropower reservoir in Binh Thuan province, Vietnam, in May 2019. This is the first floating PV plant on the reservoir in Vietnam, and is the property of a third party. [ABSTRACT FROM AUTHOR]

The use of renewable resources has been on the rise in recent years. The use of renewable resources is also related to state support, either in the form of a fixed price for the purchase of energy or in the form of direct investment aid. The present article analyzed the use of RES in the Czech Republic and the number of installations depending on state support, which represents a significant factor affecting the end user. The article presented the development of the legislative framework that formed the basis of RES support during the years since independence and the country's entry into the EU. The breaking points of support, when support was increased, and the impact of this support on the number of new installations were indicated. The development of energy production from RES was analyzed on the basis of historical data. A numerical model using a neural network was developed to illustrate the impact of government measures. The time interval for training the network was chosen on the basis of legislative changes, while the possible course was monitored if the change did not occur, or if the support remained the same. The results of the numerical simulation point to the fact that without adequate legislation and state support, there would probably not be an expansion of the use of RES. On the other hand, if the initial high support were to remain, there would probably be a significant distortion of the energy market. Finally, a prediction for the near future was presented, while a stabilization of the situation was assumed. [ABSTRACT FROM AUTHOR]

Solar power (SP) passive optical network (PON)-based fiber-wireless (FiWi) access systems are becoming increasingly popular as they provide coverage to rural and urban areas where no power grid exists. Secure operation of such networks which includes solar- and/or battery-powered devices, is crucial for anticipating potential network issues and prolong the life of the network operation. Since optical network units (ONUs) may be powered by SP-charged batteries, energy awareness becomes an important issue, particularly when it comes to reducing ONUs' energy consumption and allowing them to operate in off-grid remote areas. With the PON as the fixed part of these networks, the optical line terminal (OLT) informs the ONUs through a message exchange mechanism when no traffic is present, allowing them to transition to a low-power-consumption sleep mode. However, man-in-the-middle (MITM) attacks pose a serious threat to the message exchange mechanisms, which can eventually drain the energy of battery-powered ONUs resulting in their shutdown. Consequently, this paper introduces two novel mechanisms for reducing ONU energy consumption, namely the wake-up and time-out mechanisms, which can be used to mitigate the effectiveness of MITM attacks that may seek to affect the unit's operation due to battery drain. The formal verification results show that these goals were effectively achieved. [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]

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]

One of the main challenges that face the reliable use of photovoltaic solar systems in hot arid regions is the prevailing high temperatures during the day. To overcome this issue, Phase Change Materials (PCM) are used for passive cooling providing different options to attain sufficient thermal management solutions for different applications. Passive cooling can be achieved by adjusting a heat sink to the solar PV module. This can be realized by attaching a PCM layer or sensible heat storage to the backside of PV panels. Few studies have reported on simplified modeling and numerical procedures using the apparent heat capacity formulation and volume averaging technique as a robust approach to solving such sophisticated problems with minimal computational efforts, high accuracy, and in a short period of time. However, there is still a need to bridge the large-scale gap between the macroscale within the PCM layer, with a moving melting front, and the length scale of PV modules. Hence, this work focuses on modeling and simulating PCM-Matrix Absorbers (PCM-MA) that consist of fibrous aluminum cellular structure filled with PCM for passive thermal management of PV panels using apparent heat capacity formulation and homogenization based on volume averaging technique. COMSOL Multiphysics FEM software was used for the numerical simulation of the phase change problem by using a Smoothed Heaviside step function to overcome the singularity of PCM challenge that arises with sharp melting temperatures. To validate the proposed model, it has been compared with a benchmark analytical solution for an ice melting problem, i.e., the Stefan problem, in a semi-finite slab, i.e., Neumann's solution under the same assumptions and boundary conditions. The specific characteristics of phase change and evolution of melting front with time, heat capacity change with the temperature at different times, and with locations along the slab height are presented. As the phase change is modeled to take place over a mushy region, i.e., a narrow temperature interval rather than a sharp melting point, the results show a good coincidence of the heat capacity profile and its peak at different times and locations. The validated model can be used for the optimization of PCM-MA for any specific geographical location and other applications such as the passive cooling of buildings with PCM integrated with the outer envelope. To this end, the results of the simulation in this work are shown to be in agreement with those obtained from the analytical solution. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks' hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO's planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network. [ABSTRACT FROM AUTHOR]

The importance of renewable energies and energy storage system forming a micro-grid and integrating it to the electrical grid is widely spread. A supervisory system plays a crucial role in controlling, managing, and planning the micro-grid. This paper demonstrates the development of a new custom supervisory system based on Internet of Things (IoT), creating an information sharing environment. The proposed supervisory system is based on open-source tools for a micro-grid, composed of a photovoltaic power plant and a storage system, employing smart devices and making non-smart devices compatible with IoT systems. The new supervisory improves the available system by incorporating new features and devices and increasing the data polling rate when necessary. A comparison between the current supervisory system and the proposed one is performed, showing that the new system is more flexible, easily modified, cost-effective, and more fault-resilient. [ABSTRACT FROM AUTHOR]

It is generally agreed that solar energy, which can be converted into usable electricity by means of solar panels, is one of the most important renewable energy sources. An energy and exergy study of these panels is the first step in developing this technology. This will provide a fair standard by which solar panel efficiency can be evaluated. In this study, the MATLAB tool was used to find the answers to the math problems that describe this system. The system's efficiency has been calculated using the modeled data created in MATLAB. When solving equations, the initial value of the independent system parameters is fed into the computer in accordance with the algorithm of the program. A simulation and a parametric analysis of a thermal PV system with a sheet and spiral tube configuration have been completed. Simulations based on a numerical model have been run to determine where precisely the sheet and helical tubes should be placed in a PV/T system configured for cold water. Since then, the MATLAB code for the proposed model has been developed, and it agrees well with the experimental data. There is an RMSE of 0.94 for this model. The results indicate that the modeled sample achieves a thermal efficiency of between 43% and 52% and an electrical efficiency of between 11% and 11.5%. [ABSTRACT FROM AUTHOR]

Imaging concentrators like the parabolic trough solar concentrators have been widely employed for energy production in solar power plants. The conventional imaging solar concentrators form a non-uniform Gaussian distribution on receiving absorbers yielding the highest temperatures. The traditional CSP system normally truncated a peripheral region of heat flux to better use the central part. CPV/T systems using the waste heat recovery method can largely improve the total efficiency. However, for the CPV module, the coolant temperature was usually below 80 °C, which limited the applications of the thermal cycle such as the ORC system. In this article, a novel trough-type free-form secondary solar concentrator (TFSC) for PV/Thermal hybrid application has been proposed. Different from other CPV/T concepts using a combined PV panel and cooling tunnel/tube, the current concept separates the receiver in two parts. The secondary free-form reflector is generated by the geometric construction method, resulting in uniform heat flux in the edge region and high concentration in the central region. Through the ray tracing method, the optical properties have been verified. Sensitivity analysis of the concentrating structure is also conducted. The results provide supports for the design and applications of novel CPV/T systems. [ABSTRACT FROM AUTHOR]

The objective of this study is to perform peak load shaving at a virtual power plant (VPP) to maximize the electricity cost-saving for local distribution companies (LDCs) while satisfying the necessary operational constraints. It can be achieved by implementing an efficient algorithm to control the conservation voltage reduction technique (CVR) with embedded energy resources (EERs) to optimize electricity costs during peak hours. EERs consist of distributed energy resources (DERs) such as solar and diesel generators and energy storage systems (ESSs) such as utility-scale and residential batteries. An objective function of mixed integer linear programming is formulated as the electricity cost function. Different operational constraints of EERs are formulated to solve the peak shaving optimization problem. The proposed algorithm is tested using data from a real Australian power distribution network. This paper discusses four cases to demonstrate the performance and economic benefits of the control algorithm. Each of these cases illustrates how EERs contribute differently each year, month, and day. Results showed that the proposed algorithm offers significant cost savings and can shave up to three daily peaks. [ABSTRACT FROM AUTHOR]

As a developing country, South Africa relies on electricity as the most critical amenity needed for development. KwaZulu-Natal (KZN) is one of the nine provinces in South Africa that faces an energy supply challenge. KZN is also the warmest province among them all due to its location. As one of the warmest provinces, solar power could be utilised to assist in dealing with the energy crisis. This paper focuses on the cost uncertainties attributed to the implementation of solar power which will assist in meeting the demand of energy in the region. The unsustainability of energy has led to a near collapse of the region's economy. This study also contributes to improving the scientific implementation of solar power in the region to deal with the unsustainability criteria observed. The employment of Cost Benefit Analysis (CBA) revealed solar power as one of the most reliable renewable energies for planned implementation bases. With the development of the Economical, Methodological and Environmental (EME) strategic framework which addresses all social cohesion of solar power, the early turn around has been seen through the use of cost-benefit analysis (CBA) in the region of KZN. [ABSTRACT FROM AUTHOR]

People are increasingly using clean energy sources, contributing to environmental protection according to the general trend of the world. In the form of renewable energy, solar energy has contributed to solving current pressing problems, such as environmental pollution and air pollution, improving people's quality of life. The design of solar power projects in Vietnam is mainly based on meteorological data sources from Meteonorm and NASA. However, the accuracy assessment of two data sources compared to the actual solar power data in Vietnam is not available, so there is no basis to determine better meteorological data source quality to serve the design of rooftop solar power projects. The content of this paper analyzes the simulation results of a typical rooftop solar power station at the Electric Power University, Hanoi city based on meteorological data sources from Meteonorm and NASA. After that, the simulation results will be compared with the actual operating data of a rooftop solar power station near the Electric Power University and other real PV systems in the world. The study results showed that the amount of electricity production using the Meteonorm meteorological data was closer to the actual data than the NASA data source. Therefore, solar power projects in Vietnam should use Meteonorm data source for the design process to determine the best economic and technical efficiency for investors. [ABSTRACT FROM AUTHOR]

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%. [ABSTRACT FROM AUTHOR]

The mass introduction of variable renewable energies, including wind and solar photovoltaic, leads to additional costs caused by the intermittency. Many recent studies have addressed these "integration costs," and proposed novel metrics that replace the traditional metric known as the levelized cost of electricity (LCOE). However, the policy relevance of those metrics remains unclear. In this study, the author investigates and re-defines the concept of system LCOE, referring to prior studies, and proposes concrete methods to estimate them. Average system LCOE allocates the integration cost to each power source, dividing that by the adjusted power output. Marginal system LCOE revises the concept of system LCOE and value-adjusted LCOE proposed by prior studies, to be clearer and more policy-relevant. These metrics are also applied to Japan's power sector in 2050, suggesting the necessity of aiming for a "well-balanced energy mix" in future power systems with decarbonised power sources. [ABSTRACT FROM AUTHOR]

Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used. [ABSTRACT FROM AUTHOR]

Climate change impacts the electric power system by affecting both the load and generation. It is paramount to understand this impact in the context of renewable energy as their market share has increased and will continue to grow. This study investigates the impact of climate change on the supply of renewable energy through applying novel metrics of intermittency, power production and storage required by the renewable energy plants as a function of historical climate data variability. Here we focus on and compare two disparate locations, Palma de Mallorca in the Balearic Islands and Cordova, Alaska. The main results of this analysis of wind, solar radiation and precipitation over the 1950–2020 period show that climate change impacts both the total supply available and its variability. Importantly, this impact is found to vary significantly with location. This analysis demonstrates the feasibility of a process to evaluate the local optimal mix of renewables, the changing needs for energy storage as well as the ability to evaluate the impact on grid reliability regarding both penetration of the increasing renewable resources and changes in the variability of the resource. This framework can be used to quantify the impact on both transmission grids and microgrids and can guide possible mitigation paths. [ABSTRACT FROM AUTHOR]

The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4 mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The measured power consumption of the developed WSTx was 0.02109 mW during the sleep mode and 11.1 mW during the operation mode. The harvesting system can harvest energy up to 1.2 mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%. [ABSTRACT FROM AUTHOR]

PV (photovoltaic) capacity is steadily increasing every year, and the rate of increase is also increasing. A desert area with a large equipment installation area and abundant solar radiation is a good candidate. PV power plants installed in the desert have advantages in themselves, but when combined with desert aquacultures, additional benefits can be obtained while compensating for the shortcomings of the aquaculture industry. The importance of the aquaculture industry is increasing, with aquaculture products approaching half of the total supply of marine products due to sea environmental pollution and reduced resources. Moreover, in deserts, where marine products are difficult to obtain, aquaculture is a good way to save marine products. However, one of the many problems that complicate the introduction of aquaculture in the desert is that it is difficult to supply and demand electricity because the site is not near a viable electric grid. However, combination with PV can solve this problem. This paper investigates the solar power and aquaculture industry in the desert and explains the limitations and challenges of the solar power and aquaculture industry in the desert. Based on this, we hope to increase interest in the solar power and aquaculture industry in the desert and help with future research. [ABSTRACT FROM AUTHOR]

In the context of the energy crisis and global climate deterioration, the sustainable development of clean energy will become a new direction for future energy development. Based on the development process of clean energy in China in the past ten years, this paper expounds on China's clean energy policy and development plan. The development of hydropower, wind power, and solar power in China in recent years is analyzed. On this basis, the Grey Forecasting Model is used to forecast the development and structure of China's clean energy in the next 10 years, point out the direction and market opportunities of China's clean energy development in the future, and put forward the implementation methods for the sustainable development of China's clean energy. It provides a reference for the policy decision-making of China's clean energy development. [ABSTRACT FROM AUTHOR]

Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer's point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system's risk, hence enhancing the system's reliability and societal welfare. As a result, it will benefit both supply and demand-side participants. [ABSTRACT FROM AUTHOR]

The receiver serves as a critical component in tower-type concentrated solar power plants. Responsible for light-heat conversion, the efficiency of the receiver significantly affects the overall performance of the power plant. In the current study, the thermal performance of external receivers was investigated. An experiment was set up similarly using the solar simulator to experimentally investigate the heat losses of a billboard receiver. A billboard-type external receiver was designed, fabricated, and experimented with. A solar simulator having seven xenon lamps characteristics similar to the sunlight spectrum was used to obtain heat flux at the surface of the receiver. Convection losses in the head-on wind direction were evaluated, along with the radiation losses. The thermal efficiency of the billboard receiver calculated experimentally was around 83.9%. Numerical simulations were also carried out to compare the results against the experimental data. A variation of ±5% observed between both results validate the model proposed in the current study. [ABSTRACT FROM AUTHOR]

The University of Alabama at Birmingham (UAB) was one of 16 collegiate teams from around the world that participated in the U.S. Department of Energy Solar Decathlon 2017 competition. An interdisciplinary team of students from across the university was engaged in a 2-year long process to design and build a house that is powered completely by solar power. The house was equipped to run all the typical appliances of an average modern house at similar levels on a conventional power grid. The net-zero house was built and tested on the UAB campus. Considering Birmingham's weather, a safe room was built to ensure the safety of occupants during events of extreme weather, such as a tornado. A ductless HVAC unit consisting of an inverter-type 3-speed outdoor unit supplied refrigerant to four high-wall indoor units providing the primary source of space conditioning. To achieve a model of efficiency and cost effectiveness, the house incorporated a heavily insulated envelope and precise glazing protection. The roof, floor framing and walls had R-30 batt and foam insulation. With a design informed by southern vernacular language, the building is oriented to maximize solar access and to use roof planes for shading the majority of the year. Peak power generation of the panels was recorded at 9.6 kW. The home has a centralized energy management system that can provide access to energy consumption data and allow control of lighting, appliances, and plug loads remotely. Energy modeling showed that the annual electricity consumption for heating and cooling with variation in wall types were 8470 to 11,661 kWh. For the month of October, it was calculated varying from 683 to 763 kWh, with varying air changes per hour from 0 to 1.5. [ABSTRACT FROM AUTHOR]

Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development. [ABSTRACT FROM AUTHOR]

Thermochemical energy storage is promising for the long-term storage of solar energy via chemical bonds using reversible redox reactions. The development of thermally-stable and redox-active materials is needed, as single metal oxides (mainly Co and Mn oxides) show important shortcomings that may delay their large-scale implementation in solar power plants. Drawbacks associated with Co oxide concern chiefly cost and toxicity issues while Mn oxide suffers from slow oxidation kinetics and poor reversibility. Mixed metal oxide systems could alleviate the above-mentioned issues, thereby achieving improved materials characteristics. All binary oxide mixtures of the Mn-Co-Fe-Cu-O system are considered in this study, and their properties are evaluated by experimental measurements and/or thermodynamic calculations. The addition of Fe, Cu or Mn to cobalt oxide decreased both the oxygen storage capacity and energy storage density, thus adversely affecting the performance of Co3O4/CoO. Conversely, the addition of Fe, Co or Cu (with added amounts above 15, 40 and 30 mol%, respectively) improved the reversibility, re-oxidation rate and energy storage capacity of manganese oxide. Computational thermodynamics was applied to unravel the governing mechanisms and phase transitions responsible for the materials behavior, which represents a powerful tool for predicting the suitability of mixed oxide systems applied to thermochemical energy storage. [ABSTRACT FROM AUTHOR]