Search

This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges. [ABSTRACT FROM AUTHOR]

Rapid greenhouse gas emission reductions are necessary to limit climate change. Barriers to emission reductions must be addressed, including in the built environment, which is one of the largest contributors. An ordinary case where a resident erected solar panels (photovoltaics) on the roof of a house in a heritage area reveals how barriers to such initiatives arise. The local planning authority, despite supporting climate mitigation strategies, refused a permit and the solar panels had to be removed. Climate mitigation strategies need to be integrated with built heritage. Policy revision, provision of decision-making guidance, and development of professional competency is recommended. [ABSTRACT FROM AUTHOR]

In the quest for advanced coatings suitable for foldable electronics and photovoltaic systems, there is a pressing need for materials that combine transparency with durability. To address this, innovative special horizontal stripes transparent (SHT) coating is prepared by capillary gravity self‐assembly methods. This coating is derived from the structural principles of corn bracts and is created through the crosslinking of epoxy hydrophobic modified SiO2 with an epoxy organosilicon prepolymer, bridged by a double terminal amino polydimethylsiloxane. The special pattern of the surface makes the SHT coating more transparent than glass, and the special bionic structure is proven to be highly durable under extreme temperature fluctuations, withstanding tests from 150 to −20 °C over 192 h, and enduring 30 days of ultraviolet radiation exposure at 365 nm with an intensity of 30 W m−2. Moreover, even after 3000 cycles of scissors abrasion, the SHT maintained its anti‐fouling properties and mechanical resilience. It also demonstrated remarkable chemical stability across a range of solvents. The SHT coating can be easily applied to various flexible and rigid substrates using a brush, the SHT coating is poised to find broad applications in the realm of foldable optical devices. [ABSTRACT FROM AUTHOR]

The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed are nonlinear and stochastic, thus affecting the estimation of solar photovoltaic (PV) power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar power installations. The paper aims to develop a novel deep learning-based photovoltaic power prediction model on different weather conditions. The proposed model utilises a two-stage deep learning framework for accurate solar PV power prediction, which combines the long short-term memory (LSTM) and convolutional neural network (CNN) deep learning architectures. The key role of CNN layer is to identify the weather conditions, i.e., sunny, partially cloudy, and extremely cloudy, while the LSTM layer learns the patterns of solar power generation that depend on weather variations to estimate photovoltaic power. The proposed hybrid models consider meteorological factors, such as wind speed, irradiance, temperature, and humidity, including cloud cover and UV index to provide precise solar PV power prediction. The presented hybrid model has better performance metrices having root mean square error of 0.0254, 0.03465, and 0.0824, mean square error of 0.000645, 0.00120, and 0.00679, R2 of 0.9898, 0.9872, and 0.9358, and mean average error of 0.0163, 0.0236, and 0.2521 for sunny, partially cloudy, and extremely cloudy weather conditions, respectively. The results demonstrate that presented deep learning-based novel solar PV power prediction model can accurately predict solar PV power based on instantaneous changes in generated power patterns and aid in the optimisation of PV power plant operations. The paper presents an effective methodology for prediction of solar power that can contribute to the improvement of solar power generation and management. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient energy management between the distribution system and electric vehicle charging system with a hybrid technique. The novelty lies in the joint execution of Golden Jackal Optimization (GJO) and Attention Pyramid Convolutional Neural Network (APCNN). This leverages the strengths of both techniques. GJO provides a good initial solution, while APCNN refines it with its data-driven learning capabilities. This technique analyzes system data and grid conditions to find an optimal control signal for the inverter which improves performance and results in reduced system cost. The primary goal of the proposed method is to improve the performance of the charging station and the distribution grid while reducing the overall system cost. The GJO method is utilized to optimize the control signal of the inverter. Develop a model that translates the control signal for the inverter into a format compatible with the GJO algorithm. The charging station is then provided with intelligent control and decision-making abilities through the application of the APCNN approach. After that, the proposed framework is implemented into practice using the MATLAB platform, and its results are compared to those of the current approaches. The proposed method shows better results compared to existing methods like Latent Semantic Analysis, Fertile Field Algorithm, and Salp Swarm Algorithm. From the result, it is concluded that the proposed technique reduces system cost more than the existing techniques. The 95% attained efficiency indicates a significant performance improvement with the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper proposes utilizing a recent metaheuristic technique, artificial rabbits' optimization (ARO), enhanced with the quasi-opposition-based learning (QOBL) technique to improve global search capabilities. Furthermore, the novel line stability index (NLSI) is used to show weak buses in radial distribution systems (RDSs), aiding in the optimal placement and sizing of renewable energy sources (RES) such as photovoltaic (PV) systems. This enhanced algorithm, named the hybrid quasi-oppositional ARO (Hybrid QOARO) algorithm, addresses both single-objective and multi-objective functions. The single-objective approach focuses on reducing active power loss in the RDS, while the multi-objective function seeks to minimize active power loss with total voltage deviation (VD) and maximize the voltage stability index (VSI). This multi-objective approach helps determine the appropriate sizing of PV and battery energy storage systems (BESS) over 96 h (four seasons), considering the variability of photovoltaic power generation. To evaluate the effectiveness of the proposed approach compared to different optimization strategies, the IEEE 33-bus RDS is used. The highest reduction in energy losses and VD, at 92.48% and 99.78%, respectively, is achieved by applying PV + BESS at optimal power factor (PF) compared to PV only, PV + BESS at unity PF, and PV + BESS at 0.95 lagging PF. [ABSTRACT FROM AUTHOR]

Evaporative cooling technique is considered one of the effective methods for improving efficiency and power generation of a photovoltaic (PV) module by reducing the operating temperature of its surface. In this paper a theoretical study of heat transfer through a PV module was conducted to investigate how the calculated cell temperature and module efficiency are influenced by the ambient temperature, solar irradiation, and water flow rate, which affect the heating and cooling rates of the module surface. Experimental investigation was done to confirm the theoretical findings concerning the decrease of cell temperature and hence the increase of module efficiency with the increase of either the air flow on module cooling by using sprinkler for water misting or the mass flow of water on module cooling by using nozzles for making a water film over the module surface. The experimental results show a reduction of 26.94 % in cell temperature on using sprinkler against 28.32 % for nozzles with continuous cooling and 24.14 % using sprinkler against 26.75 % for nozzles with intermittent cooling. Experimental results show that evaporative cooling on using sprinkler and nozzles methods increase the electrical efficiency from 13.04 % without cooling to 14.5 % and 14.75 with continuous cooling against increase of the electrical efficiency to 14.29 and 14.7 with intermittent cooling. The maximum electrical efficiency in the datasheet at standard condition records 15.4 %. This means that the evaporative cooling over the PV module strongly improves the system performance to approach its efficiency at standard test condition STC. There is no significant difference between continuous and intermittent cooling in reducing the cell temperature and thus increasing efficiency. Moreover, intermittent cooling reduces the amount of water used for cooling. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Each solar cell is characterized at the end‐of‐line using current‐voltage (IV) measurements, except shingle cells, due to multiplied measurement efforts. Therefore, the respective host cell quality is adopted for all resulting shingles, which is sufficient for samples with laterally homogeneous quality. Yet, for heterogeneous defect distributions, this procedure leads to (i) loss of high‐quality shingles due to defects on neighboring host cell parts, (ii) increased mismatch losses due to inaccurate binning, and (iii) lack of shingle‐precise characterization. In spatially resolved host measurements, such as electroluminescence images, all shingles are visible along with their properties. Within a comprehensive experiment, 840 hosts and their resulting shingles are measured. Thereafter, a deep learning model has been designed and optimized which processes host images and determines IV parameters like efficiency or fill factor, IV curves, and binning classes for each shingle cell. The efficiency can be determined with an error of 0.06 %abs enabling a 13 %abs improvement in correct assignment of shingles to bin classes compared with industry standard. This results in lower mismatch losses and higher output power on module level as demonstrated within simulations. Also, IV curves of defective and defect‐free shingle cells can be derived with good agreement to actual shingle measurements. [ABSTRACT FROM AUTHOR]

Background: This research used satellite imagery data to conduct a regional-based potential assessment for solar photovoltaic generating in the northern section of Nigeria. The mean annual global solar radiation from satellite image data was calculated and to compare it with meteorological data, the study area's temperature was ascertained using satellite images. ArcGIS was also used to determine the appropriateness axis for the development of solar photovoltaic stations in the northern part of Nigeria. Photovoltaic technology was utilized to transform the calculated amounts of solar radiation on a global scale into potential electrical energy. Results: The findings show that the northeast region has the highest annual mean value of global solar radiation (2179.5 kilowatts-hours/square-meter/Year), followed by the northwest region (2137.5 kilowatts-hours/square-meter/Year), and the north central region (1909.6 kilowatts-hours/square-meter/Year). The influence of global solar radiation also caused an increase in temperature. With an area value of 174,886 kilometer-square, the northeast region has the greatest suited axis for the development of solar PV stations, followed by the north central region (145,187 kilometer-square) and the northwest region (127,470 kilometer-square). The greatest photovoltaic technology, single-crystalline silicon (500.5 Gigawatts), is followed by multi-crystalline silicon (462.5 Gigawatts), cadmium-telluride (362.7 Gigawatts), and amorphous-silicon (81.10202.8 Gigawatts), which has the lowest value. Conclusions: It is discovered that Nigeria's north receives more solar radiation from the sun than the rest of the country, and that the region has the ability to produce enough electricity to power the entire nation. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed. [ABSTRACT FROM AUTHOR]

Deserts are ideal places to build photovoltaic (PV) power plants, but this plants often face challenges from strong wind and sand activities during the operation and maintenance period, exploring the effects of PV power plant construction on wind disturbances and the control of wind and sand activities by different sand fixation measures is necessary. This study investigated the wind speed outside the PV plant, inside the plant without sand barriers measures (CK), and under three different sand-protecting barriers (gauze sand barriers (GZ), polylactic acid sand barriers (PLA), and grass grid sand barriers (GG)) inside the plant. Though calculated the surface roughness, friction velocity, wind protection effectiveness, and wind turbulence to determined the effectiveness of the barriers by these indexes comprehensively. The results show that: (1) The construction of desert PV power plant can effectively reduce the wind speed. Compared with CK, all three mechanical sand barriers within the plant reduced wind speed. Especially when the height less than 50cm, the GZ sand barriers reduced the wind speeds the most, with an average reduction rate of 101.5%. (2) All three sand barriers increased soil roughness and friction velocity within the power station. (3) At heights below 50cm, the GZ and GG sand barriers have better wind protection effectiveness than PLA sand barriers, while at hights above 100cm, the wind protection effect of PLA and GG sand barriers became less significant or even negligible (4) The wind disturbance caused by the three sand fixation measures increased with wind speed, the comprehensive performance of GZ and PLA sand barriers was superior than that of GG sand barriers and CK. [ABSTRACT FROM AUTHOR]

Rising energy costs, climate change impacts, and transmission losses have increased demand for renewable energy sources and decentralized solutions. As more people seek smart living and working environments, integrated smart microgrids powered by hybrid renewable systems have become attractive solutions for off-grid and on-grid communities. This study proposes designing a solar-wind-battery hybrid microgrid supplying a medical load et al.-Ain Al-Sokhna, Egypt. The optimization objectives aim to minimize the loss of power supply probability (LPSP %) and the levelized cost of energy (LCOE, $/kWh). A key consideration when designing and optimizing hybrid microgrids is the energy management strategy, which coordinates different generation sources and fluctuating load demand. Therefore, optimization algorithms were applied to balance energy flows while meeting loads, mitigating weather impacts, and preventing overcharging/deep discharge of battery storage. Models of wind turbines, photovoltaic panels, and battery storage were developed to simulate and analyze proposed microgrid operations. A multi-objective optimization approach evaluated LPSP and LCOE metrics using transit search, grey wolf, and particle swarm algorithms to find optimal system configurations. The optimization algorithms demonstrated varying performances in minimizing the multi-objective functions for the on-grid and off-grid microgrids. The particle-swarm optimization technique is the best solution for the off-grid system, which contains PV, wind, and battery storage, with a minimum LCOE of 0.3435 $/kWh and an LPSP of 4.5334%. Meanwhile, the transit-search optimization algorithm found the optimal solution for the on-grid configuration according to the objective function, yielding an LCOE of 0.116 $/kWh and an LPSP value of 3.0639 × 10−16. Statistical analysis confirmed that the algorithms generally exhibited stable and robust optimization capabilities. Of the methods, transit search was the most effective overall optimization approach. [ABSTRACT FROM AUTHOR]

The decarbonization of energy systems is forcing the development of renewable energy generation and consumption technologies. Photovoltaic systems are being used in almost every industry, including autonomous power systems used on ships, space vehicles, or flying platforms, where the voltage supplying specific equipment can change in an overridingly controlled manner. Feeding energy from a renewable source into a power system with highly dynamic frequency changes is not possible for traditional grid converter control strategies. This is caused by the synchronization system, which is designed for a fixed value of the grid voltage frequency, and by the proportional-resonant controllers used. In this paper, it is shown that frequency tracking correction causes deviations from the unit amplitude of synchronization signals, causing errors in the reference signals responsible for the active and reactive components of the converter current. To solve this problem, a new variable frequency adaptation system using a generalized second-order integrator was proposed. As a result, synchronization signals of unit amplitude were obtained. Due to the proposed method, the proportional-resonant controller was able to control the active and reactive components of the current even when the voltage frequency changes, adjusting the resonant frequency. [ABSTRACT FROM AUTHOR]

Korea is making efforts to reduce carbon emissions from buildings and, as part of this initiative, the government is promoting the expansion of modular housing. This study analyzed the feasibility of achieving zero-energy certification and the economic viability of applying photovoltaic (PV) systems to modular housing in Korea. Energy consumption and self-sufficiency were calculated for low-rise (4 floors), mid-rise (13 floors), and high-rise (30 floors) buildings. Economic viability was assessed according to the payback period, net present value (NPV), and benefit–cost ratio (B/C ratio). Construction costs of modular buildings are higher than those of reinforced concrete (RC) buildings, and when only PV systems are applied, it is not possible to achieve a high grade in high-rise buildings. The results indicate that economic feasibility is lacking across all building heights, leading to the conclusion that further research on cost reduction and expanded government support are necessary. This study presents a sustainable building model for Korea that is expected to contribute to future policy decisions. [ABSTRACT FROM AUTHOR]

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations. [ABSTRACT FROM AUTHOR]

Solid-state perovskite solar cells (PSCs) were first discovered in 2012, where a power conversion efficiency (PCE) of 9.7% was demonstrated along with stability for 500 h at ambient atmosphere. Since then, the PCEs of PSCs have increased amazingly to over 26%. Moreover, perovskite/silicon tandem solar cells achieved a PCE as high as 34%. Such a superb photovoltaic performance is due to the inherent optoelectronic properties of halide perovskites. Here, the progress of PSCs is reported following a detailed description on the discovery of PSCs. The first solid-state PSCs were based on sensitization concept using a sub-micrometer mesoporous TiO2 film whose surface was coated with nano-sized methylammonium lead iodide (MAPbI3), which had evolved to n-i-p and p-i-n planar device structures. Recent high efficiencies were mostly demonstrated using formamidinium lead iodide (FAPbI3) perovskite. To increase further the PCE to more than 30%, the current density should approach 28 mA/cm2 and fill factor 90% while keeping the voltage near 1.2 V using a perovskite with bandgap less than ~ 1.47 eV (theoretical current density = 29.4 mA/cm2). Thus, a strategy should be well established to make a defect-minimized perovskite film and the interface-engineered PSCs as well. Finally, effective methodology for improving stability of PSCs is discussed. [ABSTRACT FROM AUTHOR]

Among the different photovoltaic technologies, bifacial photovoltaic modules outperform monofacial ones by being able to harvest the rear incident irradiance. In fact, they achieve higher power output under identical operating conditions. Consequently, the transition from monofacial to bifacial photovoltaic modules is progressing in residential and utility contexts. However, it remains to be fully clarified which installation conditions allow bifacial modules to perform best under different operating conditions. After discussing the different modeling techniques presented in the literature, this paper isolates and evaluates the influence of ground reflectivity, module height, and cloudy weather conditions on the annual incident irradiance and, consequently, the optimal tilt angle of a bifacial photovoltaic module. To focus on the bifacial aspect, each factor is analyzed from the perspectives of the front surface, the back surface, and both. Therefore, different patterns are isolated. The results show that ground reflectivity is key in determining the optimal tilt angle, as it affects the back incident irradiance by up to 431% when compared to a low reflectivity scenario. In contrast, module height and weather conditions do not affect the optimal tilt angle, although they do affect the incident irradiance by up to 5% and 24%, respectively. [ABSTRACT FROM AUTHOR]

Tibet, with its abundant hydraulic, solar, and wind resources, stands at the forefront of China's renewable energy development. This paper provides a comprehensive analysis of the current state of clean energy development in Tibet, highlighting the region's vast potential and the challenges it faces. We find that, while Tibet has made significant strides in harnessing its natural endowments, infrastructural limitations, seasonal fluctuations, and technological hurdles constrain the development of clean energy. This paper offers a multifaceted set of recommendations aimed at accelerating clean energy development in Tibet, including policy reforms, infrastructure enhancements, and technological innovations. Our study's unique contributions lie in its holistic approach to clean energy development, its detailed analysis of the regional energy policies, and its forward-looking recommendations that balance ecological protection with energy security. By adhering to the principle of ecological priority and conducting innovative research in clean energy development, Tibet can leverage its carbon sequestration capabilities for environmental protection while promoting sustainable economic and social development. This paper provides valuable insights for policymakers and scholars, offering a roadmap for the sustainable development of Tibet's economy and a reference for similar regions embarking on clean energy transitions. [ABSTRACT FROM AUTHOR]

ABSTRACT A hybrid DC microgrid (DC MG) integrates diverse renewable energy sources (RESs), each exhibiting unique nonlinear generation and power response characteristics. The intrinsic nonlinearity of these interacting RES necessitates the design of nonlinear control strategies to ensure stable operation and reliable power supply within the DC MG system. This paper presents a novel approach for nonlinear control of DC link voltage in hybrid DC MGs. Hybrid backstepping‐based controllers with event‐triggered (ET) control are proposed to enhance the energy quality produced by the DC MG during multiple disturbances while minimizing stress on converters by reducing the frequency of trigger signals. The PV controller is designed to maintain stability amid fluctuations in cell temperature and variations in solar irradiation. The wind controller is optimized for efficient wind power extraction, ensuring high performance in both static and dynamic conditions. The ESS is coordinated to mitigate constraints associated with RESs. Additionally, an ET controller is implemented for DC link voltage control, improving channel bandwidth efficiency and reducing strain on converters. This leads to longer interevent intervals compared with other nonlinear controllers, minimizing the need for frequent control adjustments. Lyapunov stability analysis is performed on the controllers to guarantee the asymptotic stability of the closed‐loop system. The proposed control schemes are then validated through numerical simulations in MATLAB/Simulink and further tested on an OPAL‐RT real‐time simulator. The results demonstrate that the proposed scheme improves coordination challenges among multiple RESs under uncertainties while also lowering computational overhead compared with traditional control methods. [ABSTRACT FROM AUTHOR]

Agricultural Energy Internet (AEI), representing a key evolutionary direction in the integrated energy landscape of rural regions, holds a vital position in advancing the electrification of agricultural sectors. However, the disjointed control between agricultural loads and grid operations hinders the collaborative development of agriculture and energy. Addressing these issues, this paper investigates the current applications of artificial intelligence in the fields of agriculture and energy. The authors examine the evolutionary path of AEI, particularly emphasizing the critical technologies emerging from the intersection of agriculture, energy, and digital networks. Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. In response to the challenges posed by low energy efficiency in rural areas, a reinforcement learning framework is proposed for coordinating fisheries, agriculture, livestock farming, and rural distribution networks. This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning. [ABSTRACT FROM AUTHOR]

The environmental challenges posed by conventional energy production necessitate a shift towards sustainable alternatives. This study explores the potential of solar power, particularly focusing on Solar Power Tower (SPT) systems utilizing the supercritical carbon dioxide (sCO2) Brayton cycle, as a viable solution to reduce carbon emission in Azerbaijan. Through data collection and analysis, the study evaluates the efficiency and feasibility of SPT systems. Results indicate that the sCO2 Brayton cycle offers a net cycle efficiency of 50.8%, surpassing traditional power generation methods. The Direct Normal Irradiance (DNI) analysis identifies the Nakhchivan Autonomous Republic as an optimal location for deploying SPT technology due to its high DNI levels. This transition to advanced solar technologies promises to meet rising energy demands, reduce carbon emissions, and mitigate environmental degradation, aligning with global efforts to address climate change and secure a sustainable future. [ABSTRACT FROM AUTHOR]

The large-scale adoption of renewable energy from solar sources has gained significant traction. Solar energy conversion using photovoltaic devices is an alternative to meet the electricity demand. However, the excessive heat generated by solar radiation presents a challenge in maximizing the power output of photovoltaic panels. To tackle this issue, a passive cooling system employing aluminum fins was installed on the rear side of the photovoltaic panels. This study focused on two key configurations: the geometry and arrangement of the fins. The study was conducted experimentally indoors using a halogen lamp solar simulator with a uniform intensity for each variation of 1000 W/m² . A total of forty-one fins were installed beneath the panel in various configurations. This study used a 50 Wp photovoltaic panel with a polycrystalline cell structure. The results indicated that rectangular fins lowered the temperature by 36.85℃ in the perforated β 45° configuration. Furthermore, this setup achieved a 13.06% increase in electrical output efficiency. The efficiency value increased by 3.80% compared to the uncooled photovoltaic. Statistical analysis conducted through two-way ANOVA without replication revealed a significant difference between the two configurations. Notably, the fins' geometric shape significantly influenced temperature reduction and electrical efficiency more than the arrangement model. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system transforms sunlight into electricity. PV cells have non-linear I-V relationships with one point producing the maximum power output from the PV cells. The best efficiency is obtained at the maximum power point (MPP) of PV system. This paper proposes a hybrid MPP control strategy that incorporates both fuzzy logic controller (FLC) and the Perturbation and Observation (P&O) algorithm under various cases of whether conditions. The proposed algorithm is continuously searching for maximum power point and modify it as needed under rapidly changing weather conditions (irradiance and temperature) and be able to perform successful tracking of the MPP under partially shaded conditions. The performance and the power output of the system will be evaluated using simulation under specific weather conditions. The developed controller is implemented in two stages to overcome the drawbacks of the conventional P&O algorithm; the first stage uses fuzzy controller to provide an initial guess for P&O where it combines the speed of FLC approximation with the accuracy of P&O. The second stage uses another fuzzy controller to find a proper step size of P&O to enhance the transient response and reduce the steady-state oscillations. The results show that the efficiency of the proposed remains high under various scenarios; Uniform irradiation, sudden irradiation, partial shading (weak, moderate, and strong). Furthermore, results demonstrated that the proposed hybrid FLC-P&O can effectively improve the accuracy of the conventional P&O algorithm. [ABSTRACT FROM AUTHOR]

Syngas rich in hydrogen, generated through renewable‐powered co‐electrolysis of water (H2O) and carbon dioxide (CO2) using solid oxide electrolysis cells (SOEC), have gained significant attention due to its high efficiency and conversion rates. This method offers a promising solution for mitigating global warming and reducing CO2 emissions by enabling the storage of intermittent renewable energy. This study investigates solar‐integrated co‐electrolysis of H2O and CO2 via SOEC to produce hydrogen‐rich syngas, which is then utilized for methanol synthesis through a series of heat exchangers and compressors. Parabolic dish solar collectors supply thermal energy, while photovoltaic modules provide electricity for SOEC operation. CO2 from industrial processes is captured and combined with steam at the SOEC inlet for co‐electrolysis. The proposed system is modeled using engineering equation solver software, incorporating mass, energy, and exergy balance equations. The system's performance is analyzed by varying key parameters such as direct normal irradiance, heat exchanger effectiveness, current density, cell temperature, and pressure. The proposed system achieves a solar‐to‐fuel efficiency of 29.1%, with a methanol production rate of 41.5 kg per hour. Furthermore, an economic analysis was conducted to determine the levelized cost of fuel. [ABSTRACT FROM AUTHOR]

In the upcoming decades, renewable energy is poised to fulfill 50% of the world's energy requirements. Wind and solar hybrid generation systems, complemented by battery energy storage systems (BESS), are expected to play a pivotal role in meeting future energy demands. However, the variability in inputs from photovoltaic and wind systems, contingent on environmental conditions, introduces fluctuations in their power outputs. Effectively managing constant power at the DC-link and enhancing power quality (PQ) at the AC-bus present formidable challenges. In this article, the hybrid power generation (HPG) system has been analyzed in different stages of the proposed controller. The initial stage focuses on mitigating power fluctuations at the DC-link by employing a hybrid phase-locked loop (PLL)-based voltage source converter (VSC) controller. Subsequently, the second stage delves into the analysis of power quality aspects, addressing issues such as sag, swell, harmonics, and voltage interruptions. To tackle these challenges, a distribution static compensator (D-STATCOM) is introduced, leveraging a hybrid technique that integrates the cuckoo search (CS) algorithm and recurrent neural network (RNN). This innovative approach, a unique contribution of this research, was implemented and simulated using MATLAB/Simulink. The obtained results demonstrate comparability with existing applications of the controller, thereby validating the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Phase-change material (PCM) can enhance the efficiency of photovoltaic (PV) modules by reducing their temperature and is widely studied for thermal management. However, their performance varies due to differences in local solar radiation and climate conditions. Previous studies have mainly focused on the thermal properties of PCM, but practical evaluation should consider specific local conditions. To investigate the thermal-management performance of PCMs in different zones and obtain optimal design parameters, this study investigated the temperature-control effect of PCMs on PV systems across different regions through experiments. The results revealed that the temperature-control performance of PCM was limited in cold regions. Furthermore, the study developed a PCM-PV model and employed response surface methodology along with an NSGA-II to analyze the temperature-control effectiveness of the PCM-PV system in nine regions of China. Pareto solutions were obtained for nine regions in China, balancing annual power generation and system costs. PCM effectiveness is limited in colder regions like Naqu, where it increases power generation by only 0.5%, while in other regions, it improves annual power generation by 1.4% to 3%, especially in areas with high temperatures and abundant solar resources. However, when considering life-cycle gains and initial investment, PCM technology may not always be economically efficient, highlighting the need for region-specific evaluations. [ABSTRACT FROM AUTHOR]

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R2) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN. [ABSTRACT FROM AUTHOR]

The global push towards sustainable energy usage and the increasing adoption of renewable energy sources, such as solar power, requires innovative approaches to energy management, particularly in energy-intensive sectors such as education. This study proposes a change in school start time from 7 a.m. to 9 a.m. to align operational hours with periods of off-peak electricity demand and maximum solar availability. Four scenarios are compared: baseline (current schedule without solar), shifted schedule without solar, baseline with solar, and shifted schedule with solar integration. The analysis reveals that shifting the school's operational hours alone leads to a peak demand reduction of 40%, mitigating strain on the grid during high-demand periods. Solar integration without schedule has a less pronounced effect on peak demand (26%). The combination of schedule shifting and solar integration delivers the most significant benefits, with the highest cost reductions (28%) and peak demand reductions (60%). This study demonstrates that synchronised solar energy generation and optimised scheduling can enhance energy efficiency and long-term financial savings, offering a practical solution for reducing operational costs and improving sustainability in schools. This study demonstrates how public institutions can contribute to the energy transition by adapting their operational schedules to align with renewable energy availability, rather than relying on conventional fixed schedules. [ABSTRACT FROM AUTHOR]

The central inverter topology presents some advantages such as simplicity, low cost and high conversion efficiency, being the first option for interfacing photovoltaic mini-generation, whose shading and panel orientation studies are evaluated in the project planning phase. When it uses only one power converter, its control structures must ensure synchronization with the grid, tracking the maximum power generation point, appropriate power quality indices, and control of the active and reactive power injected into the grid. This work develops and contributes to mathematical models, the principles of formation of control structures, the decoupling process of the control loops, the treatment of nonlinearities, and the tuning of the controllers of a single-stage photovoltaic system that is integrated into the electrical grid through a three-phase voltage source inverter. Using the parameters and configurations of an actual inverter installed at the power plant CRESP (Reference Center for Solar Energy of Petrolina), mathematical modeling, implementation, and computational simulations were conducted in the time domain using MatLab® software (R2021b). The results of the currents injected into the grid, voltages, active powers, and power factor at the connection point with the grid are presented, analyzed, and compared with real measurement data during one day of operation. [ABSTRACT FROM AUTHOR]

The tomato pomace (TP), which is a by-product of the production of tomato paste, was dried in a novel custom-designed daylight simulated photovoltaic assisted dryer (DPVD). The different light applications (daylight, UV light, daylight + UV light, and without light), different air velocities (1.5 and 2 m/s), and different heating source modes (hot air and infrared) were applied to dry TP having a moisture content of 80.60 ± 0.73% to the moisture content of 7.66 ± 1.72%. The average water activity values of all dried samples were measured as 0.52 ± 0.08. Analysis was conducted to compare sun drying with the effects of process conditions on the quality (color properties, lycopene, β-carotene, and total mesophilic aerobic bacteria count) and performance (energy efficiency, exergy efficiency, specific moisture evaporation rate, and improvement potential) characteristics of TP. The effects of process conditions for each heating source mode were determined separately, and the improvement of the system performance for each mode was investigated. The effect of the process conditions on total aerobic mesophilic bacteria (TAMB) count was similar in general. In the infrared heating mode, the loss in lycopene and β-carotene contents was 59.55 ± 2.22 and 57.87 ± 2.51 minimum for 1.5 m/s air velocity without light application and for 2 m/s with ultraviolet + daylight application. In general, the performance of the system decreased in case of using ultraviolet light. The retention in the lycopene and β-carotene contents was higher in the infrared mode with light applications compared to hot air mode without light. The optimum drying conditions were air velocity of 2 m/s with "daylight" assistance in the hot air heating mode and with "ultraviolet + daylight" assistance in the infrared heating mode. All the energy and the daylight source used in drying applications were obtained from the sun, a renewable energy source, thanks to the photovoltaic panel and the solar tube units in the novel custom-designed drying system. [ABSTRACT FROM AUTHOR]

Partial shading is one of the crucial bottlenecks in solar photovoltaic (PV) system. The performance of a PV system is affected due to partial shading. This paper highlights the impact of partial shading condition (PSC) on the performance of PV systems with an experimental analysis using a PV emulator. A reduction of 37% in maximum power, 38% in fill factor, and 60% in efficiency as a result of PSC was observed in the experimentation work. PSC also results into multiple peaks on power-voltage (P-V) curve. One of these peaks is the Global Maximum Power Point (GMPP) and other peaks are local MPPs. The GMPP cannot be tracked using conventional MPPT algorithms. This paper proposes a new optimization method called as Firefly Algorithm (FA) built on a metaheuristic approach for Maximum Power Point Tracking (MPPT). Results obtained through the simulation show the enhancement in the tracking efficiency and tracking time over the conventional MPPT methods by achieving the tracking efficiency of 98.12% with a response time of less than 1ms. The proposed system is also able to reduce the oscillations around MPP and achieve stable performance under dynamically varying environmental conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a non-communication power management plan for a renewable solar-photovoltaic (PV) hybrid direct current (DC) microgrid consisting of batteries and supercapacitors (SCs). An effective control strategy for bidirectional converters has been proposed for power supply and load generation at different operating modes and state of charge (SOC) limits of the hybrid energy storage system (HESS). The battery and SC combination provides power to the load during normal or peak operating hours. The proposed hybrid power management system was tested under uneven load and generation using MATLAB. The proposed control enhanced the operation of microgrids by utilizing HESS with a novel control strategy based on the SOC. Seamless mode switches among different operating modes are also presented in this paper. This approach ensures stable current control, minimizes charging-discharging mode changes, mitigates the risk of overcharging and over-discharging batteries, extends battery service life, and balances the SOC across different energy storage units. Consequently, this strategy enhances the operational stability and economic efficiency of the DC microgrid. The proposed methodology provided better power allocation and improved the life of the battery. [ABSTRACT FROM AUTHOR]

The increasing demand for cleaner energy solutions has led to the exploration of renewable energy sources, particularly solar photovoltaic (PV) technology. This research focuses on the thermal management of solar PV systems in Tembalang Village, Central Java, Indonesia, where the efficiency of PV panels is significantly affected by temperature fluctuations caused by environmental factors such as pollution, wind speed, humidity, solar radiation, and ambient temperature. The study highlights the importance of effective thermal management systems to enhance the energy efficiency of PV panels, which typically operate at an average efficiency of around 20%. As temperatures rise, the efficiency of these panels can drop by 10% to 25%, necessitating the implementation of cooling technologies. [ABSTRACT FROM AUTHOR]

The move toward sophisticated sensor networks in ecological applications requires a substantial amount of energy. Energy storage solutions based simply on batteries are often not sufficient to cover the energy needs, so a standalone power supply using solar energy harvesting is generally required. However, designing an appropriate solar power supply without oversizing and avoiding output power disruption all year long is not a trivial task. This paper provides a set of guidelines as well as useful information and advice for environmental researchers and other non-experts to select the right components when designing their own autonomous solar power supply for a range between 10 mW and 10 W. The design steps are compiled into a comprehensive document, free of irrelevant information yet still presenting a general overview of the solar power supply design process, in order to make this task more accessible and understandable for non-experts. The methodology for simple initial dimensioning was carried out and applied to a real-life use case by using the estimated or measured daily consumption combined with free meteorological data of the deployment site provided by various websites. Next, an hourly simulation completed the first sizing. A year of experimental results validated the methodology. [ABSTRACT FROM AUTHOR]

A novel spectral-corrected Performance Ratio calculation method that aligns the short-term Performance Ratio calculation to the annual calculated Performance Ratio is presented in this work. The spectral-corrected Performance Ratio allows short-term measurements to reasonably estimate the annual Performance Ratio, which decreases the need for long-term measures and data storage and assists with routine maintenance checkups. The piece-wise empirical model incorporates two spectral variables, a geographical location-based variable, the air mass, a PV-technology-based variable, and a newly defined spectral correction factor that results in a universal application. The spectral corrections show significant improvements, resulting in errors across different air mass and clearness index ranges, as well as temporal resolutions. The results indicate that a spectral correction methodology is possible and a viable solution to estimate the annual Performance Ratio. The results further indicate that by correcting the spectrum, short-term measurements can be used to predict the annual Performance Ratio with superior performance compared to the well-known normal and weather-corrected PR calculation methods. This approach is the first documented effort to address the spectrum's influence on the utility-scale Performance Ratio calculation from hourly measurements. The empirical formula suggested for the Performance Ratio calculation can be of extreme value for the real-time monitoring of PV systems and enhancing PV power forecasting accuracy when the spectrum is considered instead of its usual omission. The model can be universally applicable, as it incorporates location and technology, marking a groundbreaking start to comprehending and incorporating the spectral influence in utility-scale PV systems. The novel calculation has widespread application in the PV industry, performance modelling, monitoring, and forecasting. [ABSTRACT FROM AUTHOR]

Identifying the parameters of a triple-diode electrical circuit structure in PV modules is a critical issue, and it has been regarded as an important research area. Accordingly, in this study, a differential evolution algorithm (DEA) is hybridized with an electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of the DEA. A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of the DEA. Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). The results of the proposed PV modeling method are evaluated with other state-of-the-art approaches. According to different statistical criteria, the DEIMA demonstrates superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, the DEIMA has an average execution time of 27.69 s, which is less than that of the other methods. [ABSTRACT FROM AUTHOR]

Solar energy holds significant potential for alleviating poverty, tackling climate change and providing affordable clean energy, contributing to multiple United Nations Sustainable Development Goals. However, limited research has systematically reviewed the progress in the field of solar photovoltaics and poverty (PV–PO). To address this gap, this paper aims to reveal the status, collaborative networks, research hotspots, trends and challenges by conducting a scientometric analysis based on 468 academic publications. The results indicate that research on PV–PO has received widespread attention. Notably, Energy Policy, Renewable and Sustainable Energy Reviews and Energy Research and Social Science are the most prolific journals, while China, USA and UK are the leading countries in research output. Despite regional collaborative research relationships being strong, there is room for enhancing collaboration among institutions and individuals. Key research hotspots in the PV–PO field include "Renewable Energy", "Rural Electrification" and "Energy Poverty", with recent research frontiers encompassing "Systems" and "Model". Furthermore, the PV–PO domain faces challenges related to "Performance", "Efficiency", "Feasibility", "Energy Storage", "Acceptance" and "Affordability". The findings uncover the knowledge structure of the PV–PO research field and provide insights and references for governments, researchers and decision-makers focusing on leveraging solar energy for sustainable development. [ABSTRACT FROM AUTHOR]

The performance ratio, a globally recognized metric that correlates with reported global solar radiation values, serves as a crucial indicator for evaluating the efficiency of grid-connected PV plants. Also, a large scale PV power plant alone can afford some agricultural irrigation energy requirement of a region. In this study, the actual generation data from a power plant located in Bursa province in northwestern Türkiye, during its initial six years of operation have been analyzed. The analysis reveals that the annual electricity production of the power station reaches approximately 10 GWh. Notably, the time period between April and September witnesses a monthly electricity generation exceeding 1 GWh, with September emerging as the most productive month, characterized by an average performance ratio of 94.5% during this six-year period. However, over the span of six years, the highest average electricity generation occurs in July, peaking at 1.34 GWh. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%. From an environmental impact and global warming perspective, it is noteworthy that during the 36-month period in the summer season, the performance ratio exceeded 100% only three times. However, within the 32-month period in the winter season, the performance ratio exceeded 100% 19 times. This situation indicates that while the reported radiation rates by the managements are consistent with the actual values for the summer months, they need to be revised, especially for the winter months. [ABSTRACT FROM AUTHOR]

This study proposes the hybrid topology of residential micro-grid (MG) energy management, taking into account prosumers, flexibile service opportunities, and projected uncertainties. The proposed hybrid technique is the combination of the Dilated Residual Convolutional Neural Network (DRCNN) and the Archerfish Hunting Optimizer (AHO) and is usually referred to as the DRCNN-AHO strategy. The main goal of the proposed strategy is to strengthen the framework for energy management, which works to effectively address difficulties brought on by regional weather fluctuations in the environment. Accurate forecasting is essential for future residential MGs. It uses a DRCNN-based forecaster to collect past utility price-based energy consumption data to estimate day-ahead pricing signals and energy use. The AHO is used to optimize the micro-grid's operating costs and grid energy consumption while satisfying the generation-demand balance and the accompanying limitations. The involvement of prosumers in the energy management system is crucial for optimizing energy consumption and integrating renewable sources. The proposed topology is implemented in MATLAB, and its performance is compared to existing approaches such as the Seagull Optimization Algorithm (SOA), Giza Pyramids Construction (GPC), and Color Harmony Algorithm (CHA). The proposed DRCNN-AHO method attains a higher profit (k€) of 195 in case 1 and 240 in case 2. Also, the existing methods such as SOA, GPC, and CHA attain a higher profit of 170, 160, and 170, respectively. Additionally, the DRCNN-AHO approach achieves a larger profit of 195,000. The proposed strategy generates a better profit when compared to existing techniques. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). Both DC- and AC-coupled systems were explored. The performance of each configuration was simulated over a 25-year lifespan, considering varying pricing, solar resources, battery ageing, and PV degradation. Constraints included investment costs, capacity factors, and land use. A case study conducted in Wiesenthal, Germany, was followed by sensitivity analyses, revealing that a 75% reduction in battery costs is needed to make AC-coupled PV-plus-battery systems as profitable as PV-only systems. Further analysis shows that changes in electricity and FCR pricing as well as limits on FCR charging can significantly impact NPV. The study confirms that integrating arbitrage and FCR services can optimize system profitability. [ABSTRACT FROM AUTHOR]

A novel binary electrolyte composed of the planar structured 1-allyl-3-methylimidazolium dicyanamide (AMIM-DCA) ionic liquid (IL) mixed with acetonitrile-based iodide/tri-iodide electrolyte was prepared for sustainable dye-sensitized solar cells (DSSCs). Under a 2.56 mW.cm−2 light-emitting diode (LED), binary electrolyte cells (20 wt% AMIM-DCA) showed a significant increase in open-circuit voltage (Voc) (628 mV, i.e., + 26.7% compared to cells without IL) and electron lifetime (43 ms, compared to 19 ms for cells without IL), which means a reduction in the dark current. Meanwhile, the short-circuit photocurrent density (Jsc) decreased due to the increase in viscosity, a result consistent with the increase in charge transfer resistance (Rct) at the photoanode/electrolyte interface. The conversion efficiency (η) of cells with 20 wt% IL (η = 2.59%) increased by 20% compared to cells without IL (η = 2.16%). The AMIM-DCA ionic liquid, being placed by its positive cation (AMIM+) near the photoanode, competes with the oxidant I3− and reduces the dark current. The fill factor (ff) also reached 58.5% with 20 wt% IL, compared to 49.4% without IL. The IL improved the stability of the cells under solar irradiation: the reduced volatility of the electrolyte compensates for the quality of the sealing. Operating temperatures increase the fluidity of the binary electrolyte over time, thereby increasing the Jsc and η (+ 45.4% after 30 min of solar irradiation). This indicates better thermal stability and extended cell lifecycle. [ABSTRACT FROM AUTHOR]