Solar Photovoltaic Generation Research Articles

Design of distributed photovoltaic power station on the roof of 200 kW industrial plant

Abstract With the depletion of fossil energy and the increasing attention of people to environmental pollution, it was urgent to find clean and non-polluting alternative energy. Solar photovoltaic power generation had the advantages of low cost, long life, no pollution and stability. This project designed a photovoltaic power station on the roof of a 200 kW industrial plant. 360 single-crystal 550W photovoltaic modules were selected, the installation inclination was 20°. Through PVsyst simulation, the total power generation in 25 years was 4.8884 million kWh and the average annual power generation was about 195,500 kWh.

Performance evaluation of PV configurations considering degradation rate and hot spots

<p>The rapid emergence and evolution of renewable energy sources such as solar energy has become a vital component of the global effort to meet the energy needs of the future. The major concerns for continuous solar photovoltaic (PV) generation are degradation rate, hot spots. These factors lead to the negative impact on PV mismatch losses, fill factor, maximum power and efficiency. To improve the performance of PV system, the simplest solution is PV panel configuration hence in this paper spider web tie (SWT) based PV Panel configuration in proposed. The proposed configuration is implemented on KC200GT PV Panel of 5×5 size PV panels considering degradation rate, hot spot. The performance of SWT configuration is compared with series-parallel (SP), bridge-link (BL), triple tied (TT), and photovoltaic (PV) panel configurations and performance parameters such as Vmp, Imp, Pmp, Voc, Isc. FF, ∆Pml, and η are calculated in all the cases. In all the cases the proposed SWT configuration exhibited the improved performance.</p>

Investigating green hydrogen production operated by redundant energy on a solar PV mini-grid through matlab simulation and artificial neural network

Green hydrogen is an important part of the transition to a net zero economy, due to its potential to deliver high amounts of energy without pollution. One important opportunity to produce green hydrogen is the use of redundant energy on solar PV mini-grids. Redundant energy is the potential generation of a mini-grid that is never utilized due to low demand and the mismatch of peak solar PV generation and peak demand. In this study, a typical rural community solar PV mini-grid is simulated using Matlab Simulink and incorporated with a water electrolyser for hydrogen production. Having run the simulation for 365 days of the year, it was revealed that the mini-grid can produce a daily average of 1.18–2.16 kg, a monthly average of 36.59–64.84 kg and a yearly total of 609.26 kg of hydrogen. Subsequently, an artificial neuron network was trained with data acquired from the simulation. With varying configurations of the number of neurons and hidden layers, it was found that the model successfully predicts hydrogen production on the mini-grid with the highest performance (RMSE = 39.85 g and R2 = 0.9979) seen when the number of hidden layers is 40 and the number of neurons per hidden layer is 3.

Study of the Management of the Electrical Energy Production and Distribution System Within the National School of Teachers of Mamou, Guinea

With the energy transition, marked essentially by the mass integration of energy production based on renewable resources, the missions and challenges of electrical energy distribution networks are evolving. This study is part of this dynamic, its objective is the study of the management of the production and distribution system of electrical energy within the National School of Teachers of Mamou. It emerges from this study that the supply of electrical energy to the National School of Teachers of Mamou is ensured by a hybrid system of three power sources: photovoltaic solar fields, Generator Group and Electricity of Guinea. The current electrical energy requirements of the Mamou NST are 40 kW. The total power of the installed photovoltaic solar fields is 70 kWp; the Generator used has a power of 10 kVA; the site’s Electricity of Guinea network is made up of transformers, cabin substations and protective equipment. The electricity distribution network is characterized by: Four (4) 250 A circuit breakers; a 32 A circuit breaker for the departure of lamps, sockets and fans; a 10 A circuit breaker for the lamps; a 10 A circuit breaker for the fans; a 16 A circuit breaker for the sockets and an 800 A mechanical inverter. The study shows that the power of photovoltaic solar fields is largely sufficient to cover the current electrical energy needs of the National School of Teachers of Mamou.

Prediction of power generation and maintenance using AOC‐ResNet50 network

AbstractWith the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Research on English Teaching of Solar Energy and Photovoltaic Power Generation Technology

As an emerging energy development industry, solar-photovoltaic power generation technology has the problems of inaccurate translation and translation bias, which affects its long-term development. Therefore, improving the English teaching level of solar-photovoltaic power generation technology majors is an urgent problem to be solved. In order to explore the relationship between solar-photovoltaic power generation technology and the professional practice of English teaching, this paper establishes a vocabulary set of English teaching based on the Clicker calculation model to match the needs of solar-photovoltaic power generation technology majors. Then, combined with teaching theory and intelligent algorithm, the translation constraint feature was used to call and extract data, so as to verify the matching accuracy of solar-photovoltaic power generation technology and English teaching practice. The results show that the matching adjustment coefficients t=-2.822 and df=0.23, and the professional adjustment coefficients t=-3.011 and df=0.85 in the proposed model have significant changes, which are significantly better than those of the manual algorithm. Moreover, the matching accuracy of the Clicker calculation model is more than 95%. The results show that there is a positive correlation between solar-photovoltaic power generation technology and English teaching practice, and the Clicker calculation model can provide support for the matching of solar-photovoltaic power generation technology and English teaching practice.

Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.

Efficiency improvement and application prospects of solar photovoltaic power generation

The background of efficiency improvement and application prospects of solar PV power generation reflects a dynamic and evolving landscape. As technology continues to advance and as society places greater emphasis on sustainability and clean energy, solar PV is expected to play an increasingly significant role in meeting the worlds energy needs while mitigating the environmental impact of energy production. This paper is mainly about how to improve the efficiency of solar photovoltaic power generation and the application of solar power generation. We put together an article mainly by looking up technology and summarizing other peoples opinions. Among them, we have studied the principle of solar power supply systems in the first part. The author analyzed how to choose monocrystalline silicon and polysilicon and how they can improve the efficiency and the use of HJT batteries and PERC solar cells. Both HJT and PERC applications can greatly improve efficiency

Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.

A distributed economic model predictive control-based FPPT scheme for large-scale solar farm

Increasing solar-photovoltaic-power penetration necessitates the implementation of flexible power point tracking (FPPT) in solar farms. However, coordinating multiple solar photovoltaic (PV) power generation systems (SPVPGSs) poses a significant challenge due to their inherent intermittency and varying dynamic characteristics, complicating FPPT implementation. To overcome this challenge, this paper proposes a distributed economic model predictive control (DEMPC) scheme to achieve FPPT, while simultaneously enhancing overall economic performance in solar farms. By integrating solar farm control and local control into one optimal control framework, this scheme eliminates the need for power allocation, PV voltage reference calculation, and pulse width modulation. Leveraging the SPVPGS model and soft power constraint, DEMPC controllers are designed to achieve the economic targets of solar farms, which cooperate through a communication network to realize FPPT and economic optimization. Additionally, the strong nonlinearity of SPVPGS causes a non-convex mixed integer nonlinear programming (MINLP) problem, solved by a MINLP algorithm using finite converter switching states. Simulations under step-changed irradiance and power reference, as well as urgent maintenance conditions, demonstrate that the DEMPC-based FPPT scheme significantly outperforms the traditional hierarchical model predictive control-based FPPT scheme, presenting both superior dynamic response and enhanced economic performance in FPPT implementation.

Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints

The significant increase in solar PV and wind capacity worldwide has raised growing concerns about power system frequency stability in several countries. One reason for this is that these renewable technologies offer almost no inertial response. However, there are some options available to address this issue. Renewables could now offer virtual inertia. Also, power system operators could impose frequency stability constraints as part of the generation Unit Commitment (UC). These options could potentially have a large impact on renewable generation and hence also on energy costs and system emissions. In this paper, we study two key options improving stability, inertia from solar PV generators supplied by virtual synchronous machines, and frequency stability constraints as part of the UC. We assess N-1 generation contingencies for the interconnected power systems of Chile, Argentina, and Peru. We found that the option of virtual inertia reduces the violation of the RoCoF in hours of high solar generation, without affecting renewable generation. On the other hand, frequency stability constraints ensure non-violation of all stability parameters but reduce renewable generation by 12 %. This reduction increases total operating costs by about 35 %, even when the constraints are combined with virtual inertia and battery support.

Solar arrays create novel environments that uniquely alter plant responses

Societal Impact StatementGlobally, the combustion of fossil fuels represents the vast majority of greenhouse gas emissions, and as such, a transition to renewable forms of energy provides the greatest potential for mitigating climate warming. Although solar photovoltaic energy generation is a leading climate solution, these energy facilities have a significant spatial footprint. Naturally, concerns regarding the coexistence of solar development in agriculturally productive and pristine native ecosystems remain. This study offers insight for how plants respond to novel environmental conditions within a solar array and contextualizes results to inform future array siting, design, and management to realize a sustainable solar energy future.Summary Photovoltaic (PV) solar arrays impose dynamic shading regimes and redistribute precipitation to the ecosystems beneath, leading to spatial and temporal heterogeneity in plant growth environments. Although PV are known to alter ecosystem‐level processes in managed and native landscapes, the control of PV‐induced microenvironments on plant ecophysiological responses are largely unexplored. A more robust and mechanistic understanding of how PV microenvironments control plant response will inform management of existing solar arrays and provide insight for future arrays designed to enhance ecosystem services. Here, we evaluated carbon (photosynthetic parameters) and water relations (daily patterns of leaf water potential (ψL) and stomatal conductance (gsw)) in a C3 perennial grass (Bromus inermis) across PV microsites within a 1.6 ha (1.2 MW) array in semiarid Colorado, USA. Light‐saturated photosynthetic rate was surprisingly consistent spatially, not differing between plants growing in near full sun (between PV rows) versus those growing in shadier microsites beneath panels (~28% of full sunlight). Additionally, plants located in microsites receiving only direct sunlight in the morning, when air temperature and vapor pressure deficits (VPD) were low, had greater ψL and gsw than plants receiving direct sunlight primarily in the hotter drier afternoon. Thus, while soil moisture is a primary control of plant productivity in most water‐limited grasslands, we found that VPD was a better predictor of daily patterns of leaf‐level photosynthetic and water relations responses that control aboveground biomass production in a PV array. These findings provide new mechanistic insight for evaluating vegetation management strategies in semiarid PV arrays.

A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.

The Effect of Radiation Intensity on the Performance of Direct-Expansion Solar PVT Heat Pump Systems

The objective of this study was to investigate the impact of solar radiation intensity on the performance of direct-expansion solar PVT heat pump systems. To this end, an experimental setup was constructed for direct-expansion photovoltaic (PVT) solar heat pump water heating systems and photovoltaic (PV) power generation systems. The system performance and main parameters were analyzed and discussed under different solar radiation intensities. The winter experiments in southern China revealed that the exhaust temperature of the heat pump unit varied considerably under clear conditions, while the back temperature remained stable, fluctuating between approximately −13.5 °C and 24 °C. In contrast, the power generation of PVT panels increased with the increase in radiation intensity, from 78.33 W to 122.68 W, for an increase of 56.6%. Furthermore, the total electricity generation of the PVT panels was higher than that of PV panels, with an increase of 8.7–8.3%. Nevertheless, discrepancies between experimental and theoretical data were observed, particularly under overcast conditions, where the back panel temperature error was pronounced. Additionally, the system exhibited enhanced stability at elevated temperatures in comparable environments, accompanied by an improvement in the system’s coefficient of performance (COP) by 5.67%.

Photovoltaic solar energy in Brazil: a review

Brazil has a high energy potential taking into account the region with the lowest solar radiation index in our territory, located in the state of Santa Catarina, it is observed that it is higher than several other regions in the world, especially in comparison with countries that stand out in implementation and investment of the sector. The present study aims to present a brief analysis of the concept of photovoltaic solar energy generation, providing a history of implementation in our country, also presenting a current panorama, government incentives through energy policy and future perspectives. It was not intended to carry out a case study for the implementation of a system, nor to present statistical comparisons with other nations.

Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules

Solar photovoltaic (PV) power generation is a promising clean energy technology, but dust affects its performance. This study, conducted in nine Chinese cities with varying climates and geographies, analyzed the physicochemical properties of dust and its deposition patterns at different inclination angles using microanalysis. Inclination angles were classified into three categories, and a dust density prediction (DDP) model was developed for each. The study also quantified the effect of different dust densities on PV module temperature in a controlled environment and created a PV module output power prediction (POPP) model to assess dust accumulation impacts on PV efficiency. It was found that dust insulation could increase PV module output power by 1.64 %–1.79 % at dust densities of 5–10 g/m2 and radiation levels of 400–800 W/m2. Combining the DDP and POPP models, a PV efficiency loss rate model was proposed to evaluate annual average PV efficiency and power generation losses at different tilting angles. Results showed an annual average PV efficiency loss of over 7 % at a 15° inclination angle and about 4.5 % at a 90° inclination angle. This study provides a framework for evaluating PV module mounting angles and cleaning intervals, optimizing solar energy systems for improved efficiency and returns.

Cross-technology legitimacy feedback: The politics of policy-led innovation for complementarity in concentrating solar power

Solar photovoltaic and wind power generation is expanding fast globally, fuelled by technological progress and rapid cost reductions. Other renewable power technologies fare much worse: deployment stagnates despite substantial technological progress. Here, we explore why these technologies fall off political agendas although they are improving, proposing that negative cross-technology feedback from more dynamic, faster deployed technologies reduce the legitimacy of laggard technologies. This generates political pressure to cancel or adapt support schemes, which in turn may push the laggard technology to change and become more complementary to the dynamic technologies. We illustrate our propositions with a case study of concentrating solar power (CSP) policy and deployment in three countries. We show how negative legitimacy feedback from the dynamic diffusion of photovoltaics and wind power in the 2010s led to both policy termination and technological adaptation towards complementarity, changing CSP from a generation to a storage and balancing technology.

Enhancing Solar Photovoltaic Panel Performance in India's Tropical Climate: An Experimental Study on the Influential Effects of Optical Filters

<p>Due to the increasing global energy demand and consequent climate change from burning fossil fuels, researchers are getting more focused on renewable energies as a potential solution to the world's energy problems. Solar energy is a prominent form of sustainable energy. The solar photovoltaic power generation system’s electrical performance is negatively influenced by several factors, including the impact of solar irradiance, incident angle, temperature, dust accumulation on the top of the solar cell, cracks in the solar panel, shadowing, shunt resistance, solar cell materials, load mismatch, maintenance and cleaning schedule, spectral mismatch loss, and cable loss. The direct environmental factors that have the biggest impact on efficiency are the effects of sun irradiance and temperature. This empirical investigation examined the impact of optical filters on the electrical efficiency of a 40W polycrystalline solar PV panel in India while considering real-time ambient conditions. Through the utilization of an optical filter, the solar PV panel's temperature is diminished, hence enhancing the output power of the solar panel. Based on the experimental findings, the utilization of a transparent plexiglass sheet equipped with a coolant system installed on the solar panels leads to a drop in the average operating temperature of the solar PV module by up to 6°C. Additionally, the output power is enhanced by up to 10W in comparison to the reference solar panel’s output performance.</p>