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This paper studies the energy storage and generation characteristics of the photovoltaic power generation coupling compressed air energy storage system for the 5 kW base station, and analyzes the photovoltaic power generation characteristics within 24 h and its influence on the flow characteristics of the compressed air energy storage system. The results show that, affected by precipitation and sunlight, the compressed air energy storage power reaches highest value in May and August, and the compressed air flow and cooling water flow in a single day increase with the increase of light intensity. The power generation time reaches longest value in June, shortest in December, the energy storage time reaches longest in June and shortest in January. Considering the combined demand of cold energy, thermal energy and electric energy in actual production and life, the setting scheme of the compressed air energy storage system for combined cold heat power supply is obtained, and the parameter values such as compression/expansion stage, compression/expansion pressure ratio, temperature and pressure at the inlet and outlet of the compressor/expander/heat exchanger are defined. Adding solar thermal energy storage to the photovoltaic power generation compressed air energy storage system, it is found that it can effectively increase the air temperature and thus reduce the expansion stages required at a specific time. [ABSTRACT FROM AUTHOR]

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation. • The value of predictor variables in solar estimation and forecasting is quantified. • Single-point and probabilistic regression and machine learning models are tested. • Physical-based models are used to improve the model performance and explainability. • The transposition model holds most value to the estimation and forecasting models. • A sensitivity analysis shows the dependence of the model improvement to the tilt angle. [ABSTRACT FROM AUTHOR]

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness. • A novel grey seasonal model with data reorganization is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

With the implementation of energy saving and carbon reduction, the quantitative analysis of solar energy spectral characteristics has been paid more and more attention. Currently, the international test standards provide a spectral distribution with a large span between different wavebands, which makes it difficult to meet the needs of special spectral wavebands. In this paper, based on the principle of spectral splitting, the spectral distribution of solar radiation models (SDSR models) is proposed, and the differences in the spectral distributions of natural outdoor and artificial indoor light sources are evaluated. Firstly, the differences in PV module output power between the outdoor light sources and indoor artificial light sources are assessed in terms of inclination angle and solar radiation, respectively. Secondly, based on that difference, the influencing factors of the spectral distribution of solar radiation are investigated, and the SDSR models of different wavelengths is established based on spectral analysis results. Finally, based on the established SDSR models, the spectral distribution modified models (SDM models) for different inclination angles under artificial light sources is proposed. By comparing the calculated values of the SDM models with the measured data, the calculated values of the model are close to the measured data at different incidence angles. The SDM models proposed in this paper shortens the gap between the spectral distribution of the artificial light source and the outdoor natural light source, and reveal the influence mechanism of the difference in the output power of indoor and outdoor PV modules from the perspective of the spectral distribution. The results can provide guidance for the spectral distribution of solar radiation and the quantitative modification of the artificial light source. • The difference in the output power of PV modules between solar radiation and artificial light sources is evaluated. • The effect of inclination angle and orientation on the spectral distribution of solar radiation is investigated. • The spectral distribution of solar radiation model (SDSR model) at different wavelengths is developed. • The spectral modification models for different wavelengths of of artificial light sources are developed. [ABSTRACT FROM AUTHOR]

• The efficiency of dust prevention reaches 93% when the tilt angle is 75°. • The density of dust deposition increases with wind speed. • The average efficiency of dust prevention is about 25% within 3 m/s. • The density of dust deposition decreases with the wind direction. • The efficiency of dust prevention reaches 65.85% when the wind direction is 0°. Currently, solar photovoltaic power generation technology is developing rapidly around the world to achieve the objective of carbon neutrality. The deposition of dust on solar photovoltaic modules is one of the main reasons for the decline in power generation efficiency. To identify the factors affecting dust deposition, scholars have conducted numerous outdoor experiments or numerical simulations. However, there are still few studies conducted in an indoor setting. Also, there is a lack of investigation into the effect of wind direction on dust deposition. In this paper, three indoor wind tunnel experiments are designed. Considering the characteristics of wind speed, tilt angle and wind direction, the performance of super-hydrophobic coatings is evaluated. The results show that the density of dust deposition on the photovoltaic module decreases with the increase of tile angle when it faces the wind, with the dust-proof efficiency of the super-hydrophobic coating reaching a maximum of 94.43% at 75°. At the same time, the density of dust deposition increases with wind speed, and the dust-proof efficiency is 28.81% at maximum when the wind speed is 1.2 m/s. In addition, the density of dust deposition gradually declines with wind direction, and the dust-proof efficiency reaches 65.85% at maximum when the photovoltaic module faces the wind. Therefore, the use of super-hydrophobic coatings can significantly reduce the dust deposition on photovoltaic modules and improve the efficiency of photoelectric conversion. [ABSTRACT FROM AUTHOR]

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations. • ASI-based GHI nowcasts outperform persistence model under cloudy conditions. • Under clear skies, three ASIs reveal more accurate nowcasts than persistence. • ASIs forecasts are more accurate from persistence at increasing forecast horizons. [ABSTRACT FROM AUTHOR]

• The solar potential in different residential blocks is simulated. • The influences of block morphological parameters on solar potential are varified. • Four parameters that impact on the block solar potential more than 70% are verified. • The facade PV capacity in high-rise and hybrid residential blocks is more than 66%. • The average PV installation rate of roof, south and west facade is 96%, 32% and 39%. Block-scale application of photovoltaic technology in city is becoming a viable solution for renewable energy utilization. Rapid urbanization process has made urban buildings have huge development potential for solar energy in China, especially in residential areas that occupy large parts of the city. However, the residential blocks in China show differences in the morphological layout and parameters, which means they have diverse potential for solar energy development. This paper studies the effect of quantified morphological parameters on solar potential for residential blocks in Wuhan, China. The distribution characteristics of solar potential in different block environments are discussed. Various strategies are evaluated in terms of the installation position and height of PV modules. The results showed that floor area ratio, building density, average building height and building interval have significant impact on solar potential for residential block, and their correlation can reach 75%, 71%, 78% and 72% respectively. The average PV installation ratio of roof, south facade and west facade can reach 96%, 32% and 39% respectively. The results of this paper not only provide a reference to the block-scale application of PV technology in urban environment, but also stimulate the implementation of photovoltaic policies on various residential blocks in China. [ABSTRACT FROM AUTHOR]

The distribution of solar radiation on the sky is a research difficulty in the field of solar energy. In this paper, based on the CIE standard and the principle of photothermal integration, a new anisotropic solar radiation model is established, which can estimate the solar radiation received by the horizontal surface and the inclined surface at different inclination angles. First, a system of 15 sky types describing the radiation distribution is proposed according to the CIE standard, and the results show that the proportions of overcast days, cloudy days and sunny days for 28.14%, 34.24% and 37.62%, respectively. Second, the relative solar radiation and relative sky luminance are categorized according to the classified sky types, and the polynomial function relationship between them is established. Combined with the CIE sky luminance model, a new anisotropic solar radiation model (CIE-NASR model) under different sky types was established. Finally, the CIE-NASR model and the existing models were compared. The results show that the CIE-NASR model outperforms the existing model in terms of model accuracy at 30°, 45° and 60°. The results of this research provide a more theoretical for the method application of solar photovoltaics. • 3 weather types were divided into 15 sky types based on the clearness index. • The functional relationship between solar radiation and sky luminance is proposed. • A new anisotropic solar radiation model was proposed based on the CIE standard sky luminance distribution. [ABSTRACT FROM AUTHOR]

• Propose a regional pv residual electricity heat conversion and heat storage system. • Establish a mathematical analysis model and analyze the thermal-electric balance. • Established a model based on TRNSYS for capacity design and dynamic simulations. • Conduct a evaluation of the system's operational performance and investment costs. The Tibetan Plateau is characterized by abundant solar energy resources, providing excellent conditions for centralized solar photovoltaic power generation applications. Traditionally, the electricity generated by photovoltaic systems is mainly utilized through grid connections or battery storage. However, constrained by grid standards and regional dispatching, power surplus waste is common in high-altitude areas. In this regard, integrating storage systems are generally accompanied by increased costs. To address this, an innovative regional photovoltaic residual electricity thermal energy conversion and storage system is proposed and evaluated in this paper. A system simulation model is developed in Trnsys, and the load characteristics of surplus electricity are simulated and evaluated under different photovoltaic capacities. On this basis, the heat storage system capacity is obtained. The simulation results showed that the energy utilization rate of the system increased relatively by 6% to 54%, with the exergy efficiency in the range of 12.55%–23.56%, when implementing the proposed solution. The levelized cost of energy exhibited a trend of initially decreasing and then increasing. Furthermore, a performance comparison was conducted against traditional photovoltaic systems and solar centralized heating systems. The proposed system's operational benefits and investment costs are evaluated and analyzed. The results highlighted that the system not only effectively utilizes the abundant solar energy resources in the Tibetan Plateau, realizing efficient energy conversion and storage, but also demonstrates significant economic and environmental benefits. And the findings of this study contribute to the efficient utilization of solar energy resources in the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Solar energy has been rapidly utilized in urban environments owing to its significant potential to fulfill the energy demand. The precise forecasting of solar energy, including solar radiation and photovoltaic power forecasting, is crucial for effective energy utilization in cities. Currently, artificial intelligence algorithms, including machine learning (ML) and deep learning (DL) algorithms, have been widely utilized in the research of solar energy forecasting and have shown excellent performance. The different research scales lead to variations in the influencing factors and forecasting approaches used in solar energy prediction. Therefore, this paper provides a comprehensive review of solar radiation and photovoltaic power forecasting research using ML and DL algorithms from a multi-scale perspective, including the meso-scale, micro-scale, and building-scale forecasting in urban environments. The aim is to summarize the state-of-the-art progress and evaluate the solar forecasting effectiveness in diverse research scenarios. The characteristic analyses of ML- and DL-based solar forecasting approaches, prediction models, forecasting horizons, inputs and outputs were conducted for different research scales. This paper discusses the existing issues and future research directions for multiscale solar energy forecasting in cities. The analyses can help researchers and engineers in identifying suitable prediction algorithms and approaches. Consequently, this exploration can contribute to the efficient utilization of solar radiation and photovoltaic power generation in multi-scale urban environments. [Display omitted] • Machine learning and deep learning-based solar radiation and photovoltaic power forecasting research in urban environment are classified from a multi-scale perspective. • The characteristics and key points of solar radiation and photovoltaic power forecasting at the meso-scale, micro-scale, and building-scale in cities are analyzed. • Solar radiation and photovoltaic power forecasting approaches, prediction models, input and output values, and forecasting horizons are discussed. • Future research directions of solar radiation and photovoltaic power forecasting in urban environment are presented. [ABSTRACT FROM AUTHOR]

Wave Glider is an unmanned autonomous vehicle fully powered by renewable energy, which converts wave energy into thrust and converts solar energy into power for electronic devices. It is extensively applied in large-scale, long-term ocean observation. However, photovoltaic power generation capacity of Wave Glider is relatively weak (continuous power available to payloads: 10 Watts), which limits the sensing and communication ability of Wave Glider. This paper presents a Wave Glider with a WEC called WEC-Glider scheme, i.e., the fluctuating tension of umbilical drives the slider to do a reciprocating movement, and then the slider drives the ball screw to spin the generator. The WEC-Glider is capable of converting wave energy into electricity as well as propulsion. Based on Fossen's equation, the 6-DOF dynamic models are established. A numerical model is established via Matlab/Simulink, to simulate the dynamic response with different parameters. The numerical results show that the proposed WEC-Glider can achieve 34 Watts power generation under the given regular waves. In the future, the WEC-glider could be used in the oceanographic observation to cope with insufficient photovoltaic power generation of the traditional Wave Glider, especially on continuous cloudy days or in high latitude region. [ABSTRACT FROM AUTHOR]

Poverty alleviation is an important work of the Chinese government in recent years. According to the international poverty standards of the world bank, China's poverty reduction population accounts for more than 70% of the world. Photovoltaic poverty alleviation is a significant way for regions rich in solar energy resources to transform the advantages of renewable energy resources into the driving force of social and economic development. It is also an effective means for China to implement power poverty alleviation. While promoting emission reduction in these regions, it accelerates economic development and implements targeted poverty alleviation. This paper analyzes the comprehensive benefits of typical market entities of photovoltaic poverty alleviation projects, and establishes the environment, economic and social benefit evaluation models for poor households, the state and government, photovoltaic enterprises and grid corporation. In the model, we creatively put forward an index system to characterize the poverty alleviation effect, and consider the additional industrial benefits brought by photovoltaic poverty alleviation projects. Finally, a typical photovoltaic poverty alleviation project in Haiyuan County, Northwest China is selected to verify the feasibility of this model. [ABSTRACT FROM AUTHOR]

Silicon-based solar photovoltaics cells are an important way to utilize solar energy. Diamond wire slicing technology is the main method for producing solar photovoltaics cell substrates. In order to reduce production costs and improve the production efficiency, the solar photovoltaics cell substrates silicon wafers are developing in the direction of large size and ultra-thin, and the diamond wire slicing technology is developing in the direction of high wire speed and fine wire diameter. These aspects cause an increase in the fracture probability of silicon wafer during the processing and increase costs. In this paper, a comprehensive review has been conducted on silicon wafer fracture with the latest research. Firstly, the strength characteristics of ideal crystalline silicon are summarized and discussed. The ideal crystalline silicon has a large mechanical strength, and the tensile strength in the non-dissociation direction is more than 10 GPa, while the fracture strength of silicon wafers is only 100 MPa–500 MPa. This is because there is subsurface damage on the wafers during slicing processing. Then the testing methods and statistical methods of silicon wafer fracture strength are introduced. The testing methods mainly include 3-point bending test, 4-point bending test, and biaxial bending test. Collecting load-displacement data during bending test can further calculate the fracture stress of silicon wafers through linear stress analytical formulas and finite element methods. Then, the Weibull function is used for statistical analysis to obtain the fracture strength of the silicon wafer. Finally, the research literatures on the theoretical modeling of silicon wafer fracture strength and the calculation model of silicon wafer fracture probability under different load conditions are introduced. This review contributes to a comprehensive understanding of the mechanical strength degradation and fracture mechanism of silicon wafers, and provides critical insights for future research interests. [ABSTRACT FROM AUTHOR]

Solar energy has developed rapidly in recent years, and its impacts on climate have been revealed mainly through model simulations and limited field observations. However, the representation of solar plants in these models is highly simplistic and does not account for dynamical effects, which may pose risks under the simulations for tracking photovoltaics (PV). To make a more comprehensive assessment of the PV-induced climatic impacts, it is relevant to further develop a more sophisticated PV module based on the observed PV-induced impacts from specific cases. To remedy the deficiencies in the existing PV energy models, the newly sophisticated PV module established in this paper includes both the land surface radiation balance, sensible heat balance and the surface physical dray process over the locations of PV plants. It is then used to simulate the local climatic impacts of PV plants through three parallel sensitivity experiments. Comparison analysis between control run and two PV installation scenarios showed obvious changes in the 10-m wind speed, land surface temperature, and 2-m specific humidity were found locally over the locations of PV plants during both the warm and cold periods. The magnitudes of these changes are positively related to the installed capacity of the PV plants, but are much smaller than the natural climate variations. It is expected that the observation-based PV module established in this paper is a meaningful attempt for the model simulation on this field. • A newly sophisticated PV model is firstly developed based on observations. • PV-induced reduction of 10-m wind speed is ∼0.7–1.0 m/s. • Land surface temperature decreases by ∼ 1–2 K during daytime. • PV-induced drying change is obvious in January (∼0.1 kg/kg) but not in July. • The local climatic impacts are much smaller than the natural climate variation. [ABSTRACT FROM AUTHOR]

• Development of a generic dynamic PV model including under/over voltage protection. • Analysis of the impact of fault on the performance of single-phase PV systems. • Analysis of the impact of PV systems on the dynamic performance of DN. • Implications of the adoption of different standards on unwanted tripping of PV systems. • Analysis of the impact of PV systems on the protective mechanisms in DN. Faults are a major concern to the dynamic performance of distribution networks. With the predicted increase in PV contribution, it is vital to understand the impact of PV on the distribution network. However, there is very little work published that address the impact of PV systems on the dynamic performance of distribution networks. Most PV systems connected at the distribution level are single-phase in nature. A generic single-phase dynamic PV model, including the protection mechanism, is presented in this paper. This enables the evaluation of the dynamic performance of distribution networks with multiple single-phase PV systems. The paper analyses the impact of faults on the PV inverter output using the models developed. The impact of PV on the network performance during fault is then analysed. The implications of the adoption of different regulations, viz. G-83, IEEE 1547:2018, IEC 61727 and VDE 0126-1-1, are also discussed. The results show that the presence of PV systems does not significantly affect the performance of the distribution network during faults. Also, the usual concern of loss of protection co-ordination does not occur at this voltage level. Faults that are far from the PV terminals would be cleared within 0.2 s. This indicates that the disconnection delay of 0.5 s as stipulated by G-83 is appropriate. However, a lower cut-off voltage for under-voltage disconnection than the current value stipulated in G-83 would enable more PV systems to stay connected during fault and avoid unnecessary loss of generation. The network used in this work is a representative of distribution networks and protection schemes in the UK. The results of this analysis are therefore extendable to any radial distribution network that uses standard protection, e.g. fuses and relays in low voltage distribution networks. [ABSTRACT FROM AUTHOR]

The rapidly growing of population in the developing countries and their lack of access to electricity, especially in the remote or rural areas, is causing huge challenges for on energy production. Energy is an enabler and a reliable energy supply is critical to sustainable socio-economic development for any nation. Most of Chad's people live in villages with no particular power supply system. Exploiting renewable energies is the only means of fostering development and improving people's welfare. This paper attempts at proposing an energy profile and storage model for Chad in vast remote towns. The paper addresses the key energy gap that is hindering on the development of such systems, it models and assess the potential on electricity generation and using hydrogen as surplus power storage system. A techno-econo-environmental survey on a solar-wind hybrid system in 25 towns in Chad is undertaken using NASA data and HOMER Software. Several hybrid scenarios of energy production and storage is analyzed. The results showed that in the electricity generation scenario, the average total NPC for the studied stations was $ 48164 and the average LCOE was $0.573. The lowest LCOE was related to Aouzou station with 0.507 $/kWh and the highest LCOE was obtained for Bol station with 0.604 $/kWh. In the simultaneous electricity and hydrogen generation scenario, the cheapest hydrogen ($4.695/kg) was produced in the "Grid" scenario, which was the same for all of the stations, with a total NPC of $2413770. The most expensive hydrogen ($4.707/kg) was generated in the "Grid-Wind" scenario and Bol stations with a total NPC of $2420186. The paper develops cost effective models for all hybrid systems combination for both electricity and hydrogen generation across Chad. These findings could help policy makers, investors and other developmental agencies make informed choices on energy access for sustainable development for rural communities in Sub Saharan Africa. • The wind turbine alone was the superior choice at no station in Chad. • The lowest LCOE (0.507 $/kWh) was associated with Aouzou station. • The lowest COH (4.695 $/kg) was associated with Grid scenario. • In electricity generation scenario, Abeche has the lowest emission (63.1 kg/year). • In hydrogen generation scenario, Aouzou has the lowest emission (1109 kg/year). [ABSTRACT FROM AUTHOR]

Recent research has focused on the design and sizing of standalone photovoltaic systems. Unfortunately, due to highly fluctuating levels of solar energy and the power supply generated not coinciding with energy demand, high capacity of generation and energy storage systems are required to provide a highly reliable supply. However, a highly reliable system tends to produce high excess energy which needs to be discarded as a dump load, which is proving to be a major concern. For this reason, this paper proposes to use the excess energy for hydrogen production, which will then be sold to local consumers. This paper presents a comparison of optimization design and economic analysis of a standalone photovoltaic with a battery system and a standalone photovoltaic with battery-hydrogen production system using an iterative technique. For the feasibility study of the standalone photovoltaic with a battery and with a hydrogen production system, the proposed minimum hydrogen-selling price is determined by breakeven analysis and is compared to the past references' hydrogen-selling prices and gasoline prices. • Optimization design, modeling, and economic analysis of SAPV without and with hydrogen production system were studied. • Iterative technique was used in optimization process. • Optimization results for SAPVBS and SAPVBHPS were compared. • Breakeven point analysis of the payback period and the return of investment were used to determine hydrogen-selling price. • Theminimum hydrogen-selling price was compared to the past published hydrogen-selling prices and Malaysia's gasoline prices. [ABSTRACT FROM AUTHOR]

• Correlation among peak demand, energy generation and consumption. • Simulation with a statistical model. • Optimization model to compute peak demand contract and PV panels quantity. • The method considers the Expected Value and the Conditional-Value-at-Risk. • Analysis performed for a real commercial consumer in Brazil. In the last few years, the growth of solar power worldwide has been remarkable and, different from other sources, it allows small and big consumers to play an important role in the electric system. In Brazil, for big consumers, apart from the benefit associated with energy cost reduction, the photovoltaic system can also reduce the peak demand as well, making the investment even more attractive. Therefore, one of the main challenges for consumers is to accurately estimate the impact of photovoltaic systems on their costs. To do that, the estimation of their future energy consumption, peak demand and energy generation from photovoltaic systems is important to properly compute the economic advantages of such investments. This paper proposes to solve this problem by simulating future energy scenarios of energy consumption, generation and peak demand and correlating them to compute the optimum quantity of photovoltaic panels to be installed and the peak demand contract with the utility by solving a mixed integer linear stochastic optimization model. In the first part of this work, a Box & Jenkins modelling is used to estimate the parameters of the energy consumption, generation and peak demand in a correlated way. In the second part, a stochastic optimization model is applied using a convex combination of the Expected Value and Conditional Value-at-Risk, which were used as risk metrics to compute the optimum number of panels and the best peak demand contract. To illustrate the proposed approach, a case study of a real big consumer is presented, considering a specific contract applied in Brazil. The results allow us to analyze the investment in photovoltaic systems considering the risk level of the consumer and the correlation of all variables involved. In addition to that, the proposed paper can be used as a reference model to be applied in different modalities in Brazil and other countries. [ABSTRACT FROM AUTHOR]

As subsidies continue to fall, the technology and cost performance of distributed photovoltaic (PV) determines the progress of its grid parity. Based on the discussion of technology and cost, this paper analyzed the economic performance of China's distributed PV industry by utilizing the two indicators of levelized cost of energy (LCOE) and internal rate of return (IRR). The results show that: firstly, the current external environment is generally conducive to the development of distributed PV, but there are still some constraints. Secondly, with the decrease of unit investment cost, distributed PV can achieve the goal of parity before 2025. Thirdly, distributed PV projects in the three types of solar energy resources all have high IRR, and the economic performance is better for the projects with high proportion of spontaneous self-use. At the end of the paper, policy recommendations are offered as references for the government. • We comprehensively analyzed the technology and cost performance of China's distributed PV. • We focused on the trend and decline path of China's distributed PV costs. • The LCOE of distributed PV in China is expected to achieve full parity in 2025. • Except 100% grid-connected mode, the IRR of distributed PV power plants in three areas is higher than 8% which has shown good economic benefits. [ABSTRACT FROM AUTHOR]

The worldwide search for sustainable energy solutions has heightened the concentration on solar energy sources as an essential part of addressing climate change and maintaining long-term energy security. Combining the photovoltaic/thermal systems with thermoelectric generators (PV/T-TEG) stands out as a potential innovation that uses the functionality advantages of multiple energy conversion techniques. In this paper, a novel water-based photovoltaic/thermal-thermoelectric hybrid system (WPV/T-TEG) is proposed and designed to boost the solar energy conversion rate. Thus, the proposed hybrid system is tested under Algerian weather conditions. Through an experimental analysis, the performances of the proposed system have been compared to the standard PV module (SPV). It was found that the proposed WPV/T-TEG hybrid system produces about 29.04 W of electrical energy and about 187.75 W h / d a y of average overall exergy per day, whereas this is higher by 13.67 % , 21.09 % , 24.04 % , and 95.07 % compared to hybrid photovoltaic/thermoelectric (HPV/TEG), hybrid photovoltaic module alone (HPV), SPV, and thermoelectric module alone (TEG), respectively. During the production phase, the proposed WPV/T-TEG hybrid system was found to avoid about 38719 kg CO 2 eq m − 2 y r − 1 of emission. Overall, the proposed WPV/T-TEG hybrid system has the potential for future advancement in the solar harvesting approach. • The environmental and the exergy benefit of WPV/T-TEG systems are examined. • The WPV/T-TEG systems produce about 187.75 Wh/day of exergy. • The WPV/T-TEG system produces about 298.38 W of thermal energy. • WPV/T-TEG configuration can reduce the GWP amount by 1548.76 kg. CO 2. eq.m−2. yr−1. [ABSTRACT FROM AUTHOR]

Spectral beam-splitting represents a potential approach to enhance energy conversion in solar concentrating systems. This study introduces a novel hybrid solar concentrator system, comprising a dish reflector with a two-axis tracking system and an affordable optical linear system that divides the concentrated solar beam into distinct visible spectral bands. Experimental investigations were conducted, encompassing an optical analysis of the splitter system and an assessment of photovoltaic and thermal power generation from the prototype throughout a day with solar tracking. Results indicate that the splitter system achieved a transmissivity of 76 % within the 400–800 nm range with clear separation between spectral bands, demonstrating system reliability. Spectral band dimensions remained relatively constant throughout the day, with widths of 0.460 cm, 0.550 cm, and 0.700 cm for red, blue, and yellow bands, respectively. The photovoltaic cell exposed to the yellow band exhibited the highest power and current intensities, with power densities of 15 mW/cm2 at noon, compared to 12 mW/cm2 for the blue and 5 mW/cm2 for the red bands. The system also recovered thermal power, reaching a maximum of 190 W around noon. The paper discusses suitable photovoltaic cell types for each band color and explores opportunities for improved efficiency and large-scale implementation. [ABSTRACT FROM AUTHOR]

The realization of dc-dc converters performs a vital function in exploiting renewable energy sources such as solar photovoltaic (PV) and fuel cell applications. This paper demonstrates a single-switch unidirectional buck-boost dc-dc converter for continuous power flow control, excluding the hybrid switched-capacitor. The proposed converter utilizes a limited number of passive components, only four diodes and three inductors required, in addition to six capacitors. The converter can operate at a wide input voltage range with continues input current. The converter has experimented under real-time conditions with 660 W PV system. The obtained efficiency ranges from 93% to 98%. Furthermore, the converter has interfaced with 550 W fuel cell operated under different fuel pressure. The realized efficiency ranges from 91% to 97%. The maximum measured inductance current ripple is limited to under 0.70 A in both scenarios, whereas 0.16 V is the maximum output voltage ripple. • A Boost-buck DC-DC converter with a single switch is proposed. • The proposed converter is integrable with fuel cell and photovoltaic systems. • The converter has a minimum inductance current ripple and power losses. • The proposed converter efficiency ranges from 91% to 98%. • The converter performance is compared with classical methodologies. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most promising energy types, but photovoltaic (PV) mismatches have several negative effects. Finding an efficient and low-cost method to reduce or eliminate the power loss caused by the mismatch in PV power generation systems is necessary. In this paper, we propose a PV submodule-distributed maximum power point tracking (DMPPT) scheme based on a multi-port DC converter. In-depth analysis and research are performed on the PV model and mismatch characteristics, the PV module voltage equalization (VE) strategy, and implementation with the multi-port buck–boost converter. We discuss the trade-off between true DMPPT and VE on control overhead and tracking accuracy, and a detailed derivation is created to illustrate the differences between them. The VE can reduce the cost and complexity of the system at the expense of a small PV energy capture. The proposed implementation reduces the number of semiconductor components and current sensors, which is conducive to the integrated design of the hardware. A prototype based on a multi-port buck–boost converter for a 36 V/200 W PV module was designed and built. The feasibility and progress of the proposed scheme were verified through experiments. [ABSTRACT FROM AUTHOR]

This review details the most recent advancement in solar electricity production devices, in order to offer a reference for the decision-makers in the field of solar plant installation worldwide. These technologies can be classified into three main categories, namely Photovoltaics, Thermal, and Hybrid (thermal/photovoltaic). Hence, this paper begins by laying out the methodology that is used for conducting this research. Next, solar electricity production technologies are investigated and their sub-classifications are detailed to determine their resource requirements and characteristics. Subsequently, a thorough discussion is carried out. Followed by an assessment of the environmental and financial performances of each technology. Moreover, a statistical study is undertaken to highlight the efficiency and performances of each solar technology, as well as to determine their rankings in terms of electricity production worldwide. Finally, research trends related to the development of solar electricity plants are provided. • Provides thorough investigation of the solar PV, CSP and hybrid technologies. • Recent advances in hybrid solar power technologies are analyzed. • GIS based maps and summary of the installed solar power plants worldwide are given. • Updated economic and environmental analyses of solar technologies is carried out. • Presents future research trends in solar technologies. [ABSTRACT FROM AUTHOR]

Highlights • A study of the effect of dirt accumulation on the productivity of PV panels is done. • A robot is designed and manufactured to meet the constraints of the power station. • An increase of 32.27% in the power generation was concluded. • This paper gave an insight of the dust effects on the power generation in Lebanon. Abstract In the last two decades, solar energy sources have gained a remarkable place among others and have proven that it is possible to utilize green energy to satisfy a part of the global demands of electrical power. In Lebanon, solar Photovoltaic (PV) systems are being used progressively. However, soiling and dust accumulation, on the surface of these panels, minimizes their efficiency. As a first of its kind in Lebanon, this paper presents the results from a study conducted on the effectiveness of wet cleaning using a robot. This study is carried out on a part of a 1 MWp solar power plant station in Zahrani, a coastal region in Lebanon. The robot was designed, manufactured, implemented, and tested to clean the abovementioned station. The panels were cleaned in a period starting from June 15th to September 30th, 2018. The generated power was collected on a daily basis and compared to the generated power over the same period of 2017 without cleaning. The results showed that the cleaning procedure was able to effectively minimize the effect of dust on the solar panel's power output. Thus, an increase average of 32.27% in the power generation is obtained. [ABSTRACT FROM AUTHOR]

Abstract Under the circumstances of global carbon emissions reduction, it has become a trend to promote the adoption of clean energies, such as solar energy. With the increasing maturity of photovoltaic (PV) technology, household-type distributed solar PV power generation projects are increasingly popular in China. Nevertheless, compared with conventional power generation, the initial cost of a solar PV project remains relatively high. Therefore, to mobilize the incentives of the general public, there is an urgent need for studies on how to share the costs and benefits of a solar PV power generation project between the government and users. By adopting the Shapley Game methodology, this paper has conducted a theoretical analysis of the cost-sharing among the central government, local government, and users and has built an investment cost-sharing model ( C i ′ = P (i) − V s (i) − V r i , i = 1 , 2 , 3) , which is able to coordinate the benefit of all three stakeholders. This is followed by a case study. The results show that, under China's central government subsidy of 0.42 yuan per kWh, the best strategy for the local government to encourage the public to install solar PV facilities is to provide a one-off compensation equal to 30% of the initial investment. Finally, this paper proposes relevant policy recommendations to promote the development of solar PV power generation for emissions reduction in China. Highlights • We made a cost-sharing analysis of a solar PV project using the Shapley value. • We built an investment cost-sharing model (C′i = P(i)- Vs(i)- Vri, i = 1,2,3). • Providing 30% of initial subsidy is the best strategy for China's local government. [ABSTRACT FROM AUTHOR]

Graphical abstract System output power along with solar irradiance and SWP power. Highlights • Isolated continuous power generation system at constant voltage using solar energy. • A novel technique using PHES with solar photovoltaic system for reliable and quality power. • Power generation absolutely unaffected by variations or absence of solar irradiation. • System is suitable for rural as well as other areas having open well or other water sources. • Mathematical modelling and simulation using real solar irradiation for performance evaluation. Abstract A novel off-grid small power generation system using locally available sources is proposed in this paper for the areas, where the open-wells or the permanent sources of water are available. Although, the system can be installed anywhere, but its installation in remote and rural areas, where there is no access of electricity, can be an interesting application. The proposed system comprises of a solar photovoltaic (SPV) system, solar water pump, pico-hydro turbine-generator and pumped-hydro energy storage system. Its operation is quite different from all other existing SPV power generating systems. In this paper, an innovative power generation technique is developed, due to which the output power of the proposed system is absolutely unaffected by the changes that occur in solar irradiation. The proposed system does not require the use of batteries, inverter, transformer, and charging or controlling circuits at all. The attractive features of the system are simplicity, reliability and ability to generate continuous power at constant voltage irrespective of variations or absence of solar irradiation. The mathematical model of the proposed system has been developed in the paper. The simulation is carried out using real solar irradiation for performance evaluation of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes an artificial neural network (ANN) to predict the solar energy generation produced by photovoltaic generators. The intermittent nature of solar power creates two main issues. Firstly, power production and demand have to be balanced to ensure the control of the whole system, and the inherent variability of clean energies makes this difficult. Secondly, energy generation companies need a highly accurate day-ahead or intra-day estimation of the energy to be sold in the electricity pool. For the tool developed in this paper, we address the issue of the complexity of control in systems that are based on solar energies. The tool's ability to predict the parameters that are involved in solar energy production will allow us to estimate the future power production in order to optimise grid control. Our tool uses an ANN which we developed using MATLAB ® software. The results were validated by analysing the root mean square error of the prediction for days outside the database used for training the ANN. The difference between the actually produced and predicted energy is about 0.5–9%, meaning that the accuracy of our tool is sufficient enough to be installed in systems which have integrated solar generators. [ABSTRACT FROM AUTHOR]

Electrical energy becomes necessary for human being. Generation of electrical energy mostly depends on fossils fuel, they are limited in nature and also responsible for environmental pollution. Renewable energy resources provides a better alternative for future,In comparison to conventional energy resources economical aspect is a major issue of renewable energy sources with the feasibility and efficiency. These limitations are tried to overcome by deployment hybrid renewable energy resources. There are certain criteria to analyze and implement the sized, optimized and cost efficient system. This paper focus on hybrid energy systems based on solar photovoltaic (PV) and wind resources. This paper shed lights on various parameters of economic feasibility, sizing strategies with logical advancements to enhance their utilization, future prospects, and their arrangement. Strategies to develop an effective storage system is also presented here.A brief review on developments in optimization techniques, reliability index and cost analyzing techniques for hybrid renewable energy systems are also presented. [ABSTRACT FROM AUTHOR]

Highlights • The SIMULINK models for grid connected PV plants of 100 kW and 1 MW are constructed. • A new GMPPT algorithm based the MFO is proposed as to deal with the problem of PSC. • The GMPPT and DMPPT algorithms combination is used to enhance the PV plant behaviors. • To highlight the MFO dynamic performance, a comparative analysis is carried out. • The obtained results prove that the MFO method gives the best performances under PSC. Abstract The photovoltaic (PV) energy production depends on the conditions surrounding the PV array such as irradiance (G), temperature and the array surface state. These factors directly affect the photonic absorption and the productivity of the PV panels. The phenomenon of partial shading condition (PSC) is one of the problems that disturb the proper operation of PV plants. In the recent literature, several algorithms have been developed to solve this problem. This paper principally aims at extensively presenting the PSC problem that had been considered by numerous articles and cited in this paper. Then, the paper presents combination of two techniques, the first one is the Global Maximum Power Point Tracking (GMPPT) for 100 kW array. The second technique is the Distributed Maximum Power point tracking configuration (DMPPT) for 1 MW PV plant under PSC. This combination aims to overcome the drawbacks related to PSC and enhance the PV system performance. A novel technique GMPPT controller is proposed using Moth-Flame Optimization algorithm (MFO) as solution for PSC shading. A comparative study is performed among different MPPT algorithms such as: Classical Incremental Conductance algorithm (IC), Fuzzy Logic approach based on IC (FL), Particles Swarms Optimization method (PSO) and MFO. Simulation results prove the capability of the proposed approach for seeking the GMPPT of the PV array system. [ABSTRACT FROM AUTHOR]

This paper presents a simple forecasting database infrastructure implemented using the open-source database management system MySQL. This proposal aims at advancing the myriad of solar and wind forecast models present in the literature into a production stage. The paper gives all relevant details necessary to implement a MySQL infra-structure that collects the raw data, filters unrealistic values, classifies the data, and produces forecasts automatically and without the assistance of any other computational tools. The performance of this methodology is demonstrated by creating intra-hour power output forecasts for a 1 MW photovoltaic installation in Southern California and a 10 MW wind power plant in Central California. Several machine learning forecast models are implemented (persistence, auto-regressive and nearest neighbors) and tested. Both point forecasts and prediction intervals are generated with this methodology. Quantitative and qualitative analyses of solar and wind power forecasts were performed for an extended testing period (4 years and 6 years, respectively). Results show an acceptable and robust performance for the proposed forecasts. [ABSTRACT FROM AUTHOR]

Solar energy has expanded rapidly in recent years, and China is the largest market in terms of installed capacity. With the aim of achieving carbon neutrality by 2060, solar power will play an increasingly important role in China. However, like many other countries, the low energy density of solar photovoltaics is one of the major drawbacks of its further development. The emergence of floating photovoltaic systems (FPV) can not only break this threshold but also generate a series of cobenefits from a brand-new energy-land-water nexus perspective. Using a GIS-MCDA model, an evaporation model, combined with a cost-benefit analysis, this paper estimates the development potential of FPV in China, and its energy-land-water cobenefits are further analyzed. Moreover, to reveal the current land constraint for developing solar photovoltaics in China, the potential of traditional terrestrial solar photovoltaics has also been evaluated. The results show that the potential installed capacity of FPV in China can reach 705.2 GW–862.6 GW with an annual 1164.9 TWh to 1423.8 TWh of potential power output, and most potential FPV stations can obtain positive financial returns. The annual water evaporation reduction is approximately 5.8 km3. In the meantime, around 7117.3 km2 of the land could be conserved, which would alleviate the land constraint for terrestrial solar photovoltaic systems, especially in the highly urbanized eastern and southern coastal areas in China. [ABSTRACT FROM AUTHOR]

This study investigates a wind power-photovoltaic-concentrated solar power (WP-PV-CSP) system that incorporates different supercritical CO 2 (S-CO 2) Brayton cycle layouts to address grid-connected safety issues associated with solar and wind energy. Additionally, it aims to enhance the system's techno-economic performance. Notably, prior research has not explored the optimal capacity configuration of the WP-PV-CSP system or identified the most effective S-CO 2 Brayton cycle layout for system optimization. Reasonable capacity allocation can guarantee the system's reliability and feasibility, while an appropriate operation schedule can better handle the problem of mismatch between output and demand, and capacity and operation interact with each other. Consequently, this paper proposes a bi-level capacity-operation collaborative optimization approach to optimize the system's main components' capacity and operation scheduling with the optimization objectives of levelized cost of energy (LCOE) and CO 2 emissions. Moreover, it seeks to pinpoint the most advantageous S-CO 2 Brayton cycle layout. The results demonstrate that employing the S-CO 2 intercooling Brayton cycle as the CSP system's power cycle yields the WP-PV-CSP system's best techno-economic performance. Compared with the WP-PV-CSP system integrating the steam Rankine cycle, the LCOE, CO 2 emissions, and power discard are reduced by 13.81%, 40.36%, and 37.06%, respectively, and the system efficiency experiences a noteworthy 36.36% increase. These findings comprehensively highlight the substantial techno-economic and environmental advantages of adopting the S-CO 2 Brayton cycle. In summary, this research contributes valuable insights into optimizing WP-PV-CSP systems, explicitly addressing capacity and operational challenges, and underscores the potential benefits of selecting the S-CO 2 Brayton cycle in advancing renewable energy solutions. • A renewable energy system integrating S-CO 2 Brayton cycle is studied. • A bi-level model is proposed for simultaneous capacity and operation optimization. • Optimal power cycle layout for WP-PV-CSP system with LCOE and CO 2 as objectives. • Sensitivity analysis of main equipment capacity and meteorological data. [ABSTRACT FROM AUTHOR]

• The cathode's positive (anode's negative) chemical potential boosts solar power. • A high exergy efficiency of 55.43 % is obtained at anode bandgap of 0.1 eV. • Photovoltaic determines thermoradiative power while it enhances secondary power. • The iTR-PETE boosts the combined efficiency by 92.3% as compared to PETE. Photon enhanced thermionic emission (PETE) is a promising full-spectrum solar power technology capable of converting super-bandgap photons via photovoltaic and sub-bandgap photons through thermionic emission. However, PETE cathode with an infrared absorber, is usually accompanied with a high thermal radiation loss to the environment. This paper proposes an isothermal thermoradiative enhanced-PETE (iTR-PETE) configuration, where p-type cathode absorbs high-energy photons and n-p junction anode converts low-energy photons for thermoradiative purposes. A thermodynamic model is developed to assess the respective impacts of photovoltaic, thermionic and thermoradiative on solar iTR-PETE conversion. The positive and negative chemical potentials of the cathode and anode work together to increase thermionic power. The iTR-PETE converter achieves an excellent exergy efficiency of 55.43 % at a concentration ratio of 40 and an anode bandgap of 0.1 eV. Photovoltaic determines thermoradiative power, while thermoradiative increases waste heat to the secondary thermal cycle to improve the overall solar conversion efficiency. Along with an additional 4.24 % of power generated at anode, the thermoradiative effect reduces thermal radiation from the PETE converter by 28.54 %. The iTR-PETE combined system achieves a 92.3 % higher conversion efficiency than that of conventional PETE combined system. [ABSTRACT FROM AUTHOR]

Renewable energy is the cornerstone of our future energy needs. In particular, solar energy is being utilized at a faster pace than ever. Floating Solar Photovoltaics (FSPV) has recently gained traction as a suitable alternative of land-based large scale PV installation. It is a promising technology to utilize water surfaces for placing solar plants. Not only it utilizes the water as real estate but it has several other advantages as well. For example, FSPV can use the existing transmission and distribution infrastructure that is the part of hydroelectric power plants. In this paper, we evaluate an FSPV plant and its integration with the existing hydroelectric power station of a small reservoir in Pakistan. We have investigated the 500 kV, 132 kV and 11 kV voltage levels for the integration of FSPV plant. Moreover, we have devised a hydro-solar optimization model for the efficient utilization of energy. The combined system consisting of hydroelectric and 200 MWp FSPV produces more than 3.5% additional power overall when compared with production of only hydroelectric power. More importantly the FSPV generation coincides with the daily mid-day peak load thus works as a peaker plant for the national grid. • Combination of FSPV and Hydroelectric is utilized for peak load demand hours. • Reservoir area feasibility and elevation study for installation of FSPV on hydroelectric reservoir is carried out. • Evaluation of various grid integration options are carried out. • Optimization model is presented for FSPV and hydroelectric combination. [ABSTRACT FROM AUTHOR]

This paper proposes a new strategy for implementing the Reconfigurable Solar Photovoltaic array or Dynamic Photovoltaic array under repeating shade conditions. The repeating shades are mainly caused by the civil structures, buildings, poles, etc., present in the vicinity of a Dynamic Photovoltaic array. This paper presents a new algorithm to minimize the processing time in case of repeating shades. According to which, the controller does not have to perform the exhaustive calculations at each shade condition. Instead, the algorithm compares the present shade response with the previous shade responses. Simulation of 4-by-4 size Dynamic Photovoltaic array has been carried out. Experimental tests of a Dynamic Photovoltaic array under resistive load conditions have been conducted. A switch-network of double pole double throw switches has been proposed and is used in the Dynamic Photovoltaic array experimental test. Switch-count of the proposed switch-network is one-third and significantly less compared to the conventional semiconductor switch-network. [ABSTRACT FROM AUTHOR]

There is abundant solar potential in the Middle East North Africa region. Yet access to sustainable energy is still a fundamental challenge in many countries of this region. In this paper we seek to understand the success and failure of the development and the diffusion of solar energy technologies by analysing using a Strategic Niche Management framework to compare the niche development of solar thermal energy and solar photovoltaics in Lebanon. This paper has two main questions: (1) How have the solar thermal niche and the solar photovoltaic niche developed in Lebanon, and how do they compare? (2) In which ways does the Strategic Niche Management framework help us to understand the development of solar energy niches in a developing country context? To answer these questions, a cross case analysis of solar thermal and solar photovoltaic systems was conducted. Due to the absence of research using Strategic Niche Management in Middle Eastern developing countries, this study uses an illustrative case from a country in this region to contribute new insights. Moreover, unlike the Strategic Niche Management research that only focuses on single case studies, this paper presents the results of a comparative study of two niches. The main Strategic Niche Management propositions were grouped and compared per item (i.e. on voicing and shaping expectations, social networks, and learning). The results show that the solar thermal niche affected the solar photovoltaic niche to a large extent. This was especially in relation to the learning and coordination processes. This has gradually contributed to establishing a clear vision. However, both niches lacked a niche manager who was able to coordinate, manage and maintain the dynamics of the niche processes. It also lacked horizontal collaboration between key actors involved (i.e. ministries). International donors were found to play a crucial role in initiating and shaping the market with certain constraints of prioritization in the region. The paper ends with conclusions and ideas for future research on solar energy niche development in the context of developing countries. [ABSTRACT FROM AUTHOR]

Many countries have adopted schemes to promote investments into renewable energy sources resulting, among others, in a high penetration of solar PV energy. The system integration of the increasing amount of variable electricity generation is therefore a highly important task. This paper focuses on a residential quarter with PV systems and explores how heat pumps and thermal and electrical storages can help to integrate the PV generation through self-consumption. However, self-consumption and PV integration are not only affected by technologies but also by pricing mechanisms. This paper therefore analyses the impact of different tariffs on the investment and operation decisions in a residential quarter and its interaction with the external grid. The considered tariffs include a standard fixed per-kilowatt-hour price, a dynamic pricing scheme and a capacity pricing scheme. To account for the interdependent uncertainties of energy supply, demand and electricity prices, we use a module-based framework including a Markov process and a two-stage stochastic mixed-integer program. Analysing a residential quarter in Southern Germany as a case study, we find that the integration of a PV system is economically advantageous for all considered tariffs. The self-consumption rate varies between 58 and 75%. The largest PV system is built when dynamic prices are applied. However, the peak load from the external grid increases by a factor of two under this tariff without any incentive for reduction. In contrast, capacity pricing results in a reduction of the peak load by up to 35%. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in bifacial photovoltaics (PV), a worldwide map of their potential performance can help assess and accelerate the global deployment of this emerging technology. However, the existing literature only highlights optimized bifacial PV for a few geographic locations or develops worldwide performance maps for very specific configurations, such as the vertical installation. It is still difficult to translate these location- and configuration-specific conclusions to a general optimized performance of this technology. In this paper, we present a global study and optimization of bifacial solar modules using a rigorous and comprehensive modeling framework. Our results demonstrate that with a low albedo of 0.25, the bifacial gain of ground-mounted bifacial modules is less than 10% worldwide. However, increasing the albedo to 0.5 and elevating modules 1 m above the ground can boost the bifacial gain to 30%. Moreover, we derive a set of empirical design rules, which optimize bifacial solar modules across the world and provide the groundwork for rapid assessment of the location-specific performance. We find that ground-mounted, vertical, east-west-facing bifacial modules will outperform their south-north-facing, optimally tilted counterparts by up to 15% below the latitude of 30°, for an albedo of 0.5. The relative energy output is reversed in latitudes above 30°. A detailed and systematic comparison with data from Asia, Africa, Europe, and North America validates the model presented in this paper. [ABSTRACT FROM AUTHOR]

Photovoltaic system is widely installed to increase the share of renewable energy as well as to reduce the environmental impact of fossil fuel based energy. Photovoltaic (PV) is one of the most potential renewable energy based power generation systems. Monitoring of PV system is very important to send information that allows owners to maintain, operate and control these systems to reduce maintenance costs and to avoid unwanted electric power disruptions. Different monitoring systems have been introduced with the time following different requirements. Circuit complexity, availability of friendly graphical user interface, easy to understand system architecture, maintenance facility and customization ability for end user differ from system to system along with cost issues. This paper provides an overview of architectures and features of various PV monitoring systems based on different methods. There are various technologies for PV monitoring and control, developed as for commercial use or research tasks. It has been seen that a large portion of the work is done on classifications, for example, Internet based Monitoring using Servers, TCP/IP, GPRS and so forth. There are various methodologies for data acquisition, for example, PLC (Power Line Communication), PIC, Reference cell, National Instruments etc. Various requirements are considered while selecting a proper monitoring system for an application. Review of various monitoring technologies with system attributes and working structures have been discussed to get a clear view of merits and demerits of existing PV monitoring systems. All the systems discussed in this paper have pros and cons, and these systems were developed following different requirements. In the end, a particular cost effective monitoring system using Arduino microcontroller has been proposed considering both research and user level requirements from perspectives of cost, availability of parts/modules and features, compatibility with sensors and end-devices etc. [ABSTRACT FROM AUTHOR]

Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand-alone networks, dramatic changes can be anticipated in both power system planning and operating stages. Solar photovoltaic integration requires the capability of handling the uncertainty and fluctuations of power output. In this case, solar photovoltaic power forecasting is a crucial aspect to ensure optimum planning and modelling of the solar photovoltaic plants. Accurate forecasting provides the grid operators and power system designers with significant information to design an optimal solar photovoltaic plant as well as managing the power of demand and supply. This paper presents an extensive review on recent advancements in the field of solar photovoltaic power forecasting. This paper aims to analyze and compare various methods of solar photovoltaic power forecasting in terms of characteristics and performance. This work classifies solar photovoltaic power forecasting methods into three major categories i.e., time-series statistical methods, physical methods, and ensemble methods. To date, Artificial Intelligence approaches are widely used due to their capability in solving the non-linear and complex structure of data. The performance analysis shows that these methods outperform the traditional methods. Recently, the ensemble methods were also developed by researchers to extract the unique features of single models to enhance the forecast model performances. This combination produces accurate results compared to individual models. This paper also elaborates on the metrics assessment which was implemented to evaluate the forecast model performances. This work provides information which is beneficial for researchers and engineers who are involved in the modelling and planning of the solar photovoltaic plant. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems have the greatest potential to meet the scale of sustainable energy demands, yet large surface areas beyond rooftop areas are required. One source of additional surface area that avoids conflict with food production while scaling with population density is noise barriers, which provide dual use of land area both as noise abatement and energy generation. This paper provides a method to quantify the potential of mass scale deployment of photovoltaic noise barrier (PVNB) systems in a country. Based on a feasibility analysis of the irradiation levels and noise barrier locations, the PV power potential and the energy output for the photovoltaic modules is calculated for specific locations using a tool chain of free GIS software. This method, is then demonstrated with a case study for the state of California and the results are then extrapolated for the entire U.S. The U.S. is an ideal candidate country because noise abatement mandates fall well short of World Health Organizations guidelines and PVNB technology has not been implemented on a wide scale. Using conservative assumptions, the results show that the total PVNB power potential for the U.S. ranges from 7 to 9 GW using only existing noise barriers. According to findings of the paper the installed capacity of the large scale photovoltaic system deployed on noise barriers in a single state is comparable to the installed capacities of the largest solar farms in the U.S. and yet due to the unique mounting of PVNB, such systems provide better land utilization ratios for energy production than conventional solar PV farms. [ABSTRACT FROM AUTHOR]

Electricity is the fastest-growing final form of energy. The strong growth of renewable in many countries raises their share in global power generation to one-third by 2040 [1] . Lack of access to electricity is one of the biggest troubles facing the world's poor, with over 1.6 billion left in the dark globally. The substantial majority of human beings live in rural areas of developing countries which is too far away to be reached through the national grid. For their lighting needs they depend on candles, kerosene lanterns, and firewood* This consequence in an each day rate that is high priced in the long run. Long-term, solar energy is the most practical and low-budget way of bringing power to far flung communities. Small-scale, distributed solar home systems provide an effective and affordable way to bring light to people without electricity. Egypt's electricity use has been growing swiftly as a end result of economic growth in the closing decade. However, Egypt has a massive of its populace residing in rural areas access to energy is still a challenge. The power sector is key to Egypt's monetary development and poverty decrease goals. This paper represents support sustainable livelihoods for rural communities. The paper refers to renewable energy for rural populations with no access to centralized electricity power. The focus is on solar electrical energy and its economic applications for village electrification. The Paper goals to compare between electrical energy cost (kWh) using PV panels, diesel generator and the end of result suggests the cost of 1 kWh from the PV system is less than diesel system for rural residential electricity are 0.22$/kWh and 0.5$/kWh respectively. [ABSTRACT FROM AUTHOR]

Solar power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent power output due to the uncertain solar irradiance significantly challenges the economic integrations of solar generation within the existing power system, which calls for effective forecasting methods to improve the solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term solar power generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the solar power generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting. [ABSTRACT FROM AUTHOR]

Energy management is required to enable autonomous photovoltaic-powered membrane (PV-membrane) desalination systems to make the optimal use of solar energy. In this paper, a novel charge controller based on pre-set voltage sensing thresholds was designed to optimise the energy from PV panels and supercapacitors (SCs). The control algorithms were established from the data derivations with high-temporal-resolution (1s) solar irradiance (SI) source, allowing for resilient system operation under variable conditions. The impacts of ramp rates, in both SI and PV output voltage (V PV) on the system, were systematically investigated. Under a worst-case scenario, with a rapid ramp rate of ΔV PV = 2 V/s, the charge controller enabled the SCs to bridge the power gap to 6 min 20 s, permitting an additional 10 L of permeate water produced. The state-of-charge of the SCs varied from 11 to 86%, regardless of the magnitude of the ramp rate. The combination of the voltage thresholds (V pump_on = 160 V and V pump_off = 90 V) was determined to result in optimum system performance, realising a high permeate production at low specific energy consumption. It is concluded that the proposed charge controller is an effective method to enhance system resilience under worst-case solar conditions. Image 1 • Autonomous photovoltaic-powered reverse osmosis membrane desalination system. • Charge controller designed for energy management of PV-membrane system. • Control algorithms test based on high-temporal-resolution (1s) solar data. • Supercapacitors buffering for six minutes during rapid PV voltage ramp rates. • Pre-set voltage thresholds for controller determined to optimise system performance. [ABSTRACT FROM AUTHOR]

This paper investigates the local complementarity of three types of renewable sources (solar, wind and tidal). One of the main drawbacks of non-conventional renewable energy sources is their intermittent nature. Wind energy depends on the wind speed, which varies throughout the day. The generation of photovoltaic solar energy is strongly affected by clouds, that cause an abrupt drop of power to less than 50% of its rated capacity. In contrast, tidal energy is characterized by a high degree of predictability. This article raises important features of local complementarity of these renewable sources. These properties can be exploited to improve the firm power of isolated microgrids, leading to a variety of beneficial operational consequences. The contributions and conclusions presented here are supported by case studies and real data measured in the coastal region of northern Brazil. [ABSTRACT FROM AUTHOR]

This paper presents new results on the assessment of sustainability indicators for renewable energy (RE) systems (solar PV, wind, phosphoric acid fuel cell, and solid oxide fuel cell). Multi-criteria decision-making (MCDM) model and hybrid Step-wise Weight Assessment Ratio Analysis/Additive Ratio Assessment (SWARA/ARAS) method are used in this study. Five sustainability criteria (resource, environmental, economic, social and technology) and fourteen sub-categories (area, material, energy-construction, energy-fuel, CO 2 -construction, CO 2 -fuel, capital-construction, capital-fuel, delivered cost of energy, current installed capacity, growth rate, capacity factor, system efficiency, and lifetime) are included in this analysis. The extended SWARA and ARAS hybrid method in addition to three energy experts are used in this study for the calculation of sustainability indicators. The final result of this process of ranking the four renewable energy technologies using SWARA-ARAS hybrid method based on the five sustainability criteria and the fourteen subcategories is: (1) wind energy systems (land-based), (2) solid oxide fuel cell, (3) phosphoric acid fuel cell, and (4) solar energy systems (poly-silicon). The MCDM model integrated with the hybrid SWARA/ARAS method is found to be a useful methodology for the sustainability assessment of renewable energy systems, sustainable energy development and decision-making for policy makers. • Sustainability indicators for renewable energy systems: solar PV, wind, and fuel cells. • Multi-criteria decision-making (MCDM) model and hybrid extended SWARA/ARAS method. • Social, environmental, economic, technology and resources criteria were used in this study. • Final ranking of renewable energy systems: (1) wind, (2) fuel cell and (3) solar PV. • Proposed methodology is useful for sustainable energy development and for decision-making for the policy makers. [ABSTRACT FROM AUTHOR]

In this paper a new one-diode model, conceived in order to be used to represent the current-voltage curves of both crystalline and thin-film photovoltaic modules, is presented. The model parameters are calculated from the information contained in the datasheets issued by manufactures by means of simple iterative procedures that do not require the assumption of simplifying hypotheses. Some innovative relations describing the dependence of the parameters from the solar irradiance and cell temperature are adopted in order to permit the model to reliably simulate the electrical behaviour of photovoltaic devices operating in real conditions. The ability of the model to calculate the current-voltage characteristics issued by manufacturers for values of the solar irradiance and cell temperature also far from the standard rating conditions was verified for various photovoltaic technologies, such as monocrystalline, polycrystalline, amorphous silicon, CIS, CIGS and tandem junction photovoltaic modules. The percentage ratio of the mean absolute difference of current to the rated current at the maximum power point ranges from 0.071% to 1.807%; the analogous ratio of power varies between 0.056% and 1.720%. A comparison with the results obtained with other recognised two-diode models is also presented. • A procedure to get an improved one-diode model for PV devices is presented. • The model is suited to simulate both crystalline and thin-film PV devices. • The procedure is used to depict the I-V curves of seven different PV panels typologies. • The procedure is checked by comparison with other recognised two-diode models. [ABSTRACT FROM AUTHOR]

The work presented in the paper provides a detailed TRaNsient System Simulation (TRNSYS) model that simulates the performance of a solar photovoltaic – thermal (PV/T) collector and examines its potential contribution for household heating applications in the UK. Based on this, a system is modelled to simulate the hot water demand of a house through connecting the solar – thermal panel with a thermal storage tank, a pump and a controller. The results obtained from the simulation indicated by how much the solar panel is able to convert solar energy into electrical power and heat over different seasons of the year and provide the hot water needs of the household. The model was validated based on the experimental configurations of a hybrid heat pipe based solar PV/T module and through applying cooling cycles, the thermal and electrical outputs and efficiencies of the system were indicated. Through this, it is investigated that the temperature of the solar panel can be reduced on average by almost 25%, which subsequently, will result in an increase of the electrical power output by nearly 15%. The simulation results also assisted in investigating and analysing aspects such as the effectiveness and efficiency of the panel over different times of the year and helped to optimise the performance of the system. For instance, it is simulated that the system can provide hot water for the household throughout all seasons of the year and under different solar radiation conditions. However, it is discovered that in order for the system to meet the required output demand, input from an auxiliary power unit may always be necessary. • The performance of a solar PV/T collector is simulated through TRNSYS software. • The simulation models the hot water and electricity demand of a household in the UK. • The simulation is run to indicate the effect of cooling on the performance of the panel. • The results indicate by how much solar energy is converted into electricity and heating. • The results assist in analysing the efficiency of the PV/T over the course of a year. [ABSTRACT FROM AUTHOR]

Residential consumers in the U.S. have demonstrated a growing interest in rooftop photovoltaic (PV) systems, resulting in increased adoption over the last decade. However, this has diminished utility revenues, and policymakers have expressed concerns about inequitable consumer access to publicly-funded rooftop PV adoption incentives. In response to these concerns, policymakers and utility companies are changing their policies to discourage rooftop PV adoption. Alternative renewable energy models, such as utility-provided community solar programs, offer a potential solution. However, when designing such programs, it is important to consider the potential impacts on different system stakeholders, including utilities, policymakers, and solar installers. This paper describes an agent-based model that predicts the performance of different residential distributed solar models with respect to these stakeholders' objectives. In this model, consumer agents residing in an urban utility territory decide in each time-step whether they will adopt a particular renewable energy model, and the impacts of their adoption decisions on stakeholder performance metrics are captured over time. Simulation results suggest that if community solar program premium prices are set appropriately, all stakeholders can benefit: the utility can recover part of its revenue losses even as rooftop PV adoption increases, solar installers' businesses can thrive, and increased renewable energy adoption can be achieved equitably. The proposed modeling methodology can help to inform design decisions of distributed solar energy models that avoid benefiting some stakeholders at the unnecessary expense of others. • An agent-based model is developed to study solar adoption in the residential sector. • Consumer adoption behavior is studied for multiple solar-based energy models. • System-level approach with key energy stakeholders' objectives is considered. • Results show all stakeholders can benefit within a mutually beneficial arrangement. • Empirical examples are provided in support of the simulation results. [ABSTRACT FROM AUTHOR]