Photovoltaic Electricity Generation Research Articles

Optimizing photovoltaic electric generation and roof insulation in existing residential buildings

Refurbishing existing buildings to reduce energy use is a priority worldwide to reduce greenhouse gas emissions. Millions of buildings around the globe have old roofs that are poorly insulated, but with large roof surface areas that could potentially provide significant renewable energy generation. Often, photovoltaic panels are simply added onto existing buildings regardless of thermal integrity. However, due to weathering, roofs frequently need repairs over their useful life. We evaluate a comprehensive methodology using EnergyPlus and TRNSYS simulation tools to evaluate how best to combine solar electric generation and improved insulation to achieve cost reductions, improve efficiency and renewable energy utilization at the time roofs are accessed. Advantages of combining efforts into a single intervention has not been fully explored in building energy research. We further incorporate important impacts that PV stand-off arrays have on roof thermal performance from shading and long-wave irradiance to skies. These influences have important comfort implications for poorly insulated structures with increasing summer heat waves associated with climate-related warming. They also have strong interaction with installed roof insulation levels. With an example analysis in Milan, Italy, we considered three typical residential building types in wide-use around the world (single-family, multi-family, apartment complex) with different geometries, insulation levels and roof constructions. We evaluated two options: re-roofing (roof in need of repair/replacement) and refurbishment (energy intervention for roof improvement). We optimized roof insulation levels via state-of-the-art building energy simulations considering both energy and documented costs. PV electric generation was quantified in detail, also exploring how PV array roof shading affects roof thermal performance, an influence not previously considered. This is particularly important for uninsulated buildings where upper floors can experience excessive summertime heating. Both heating and cooling needs are considered to determine the optimal roof intervention and what savings- and related costs- can be obtained depending on differing parameters. We found combining appropriate insulation with PV can provide a cost-effective option to reduce net primary energy use in residential buildings. Savings from insulation alone varied from 3% (apartment complex) to 17% (single-family). When adding PV systems with the roof initially uninsulated, net savings range from 55% (apartment complex) to 80% (single-family). Shading from stand-off PV arrays reduced summer cooling loads by 17% in uninsulated apartment complexes and provided large predicted improvements in comfort to upper floor dwellings next to exposed roofs.

An Improved H-Bridge Multilevel Inverter-Based Multiobjective Photovoltaic Power Conversion System

A rapid increase in solar photovoltaic (PV) technology-based electrical power generation is being witnessed in recent years to reduce greenhouse emissions and to meet the global energy demand. Power electronic converters are also improving in terms of power capacity, output power quality, and efficiency to interface the PV array and load. In this article, an improved H-bridge multilevel inverter (IHBMLI)-based PV power conversion system (PPCS) is proposed which integrates solar PV array with the existing distribution network. The five-level output voltage reduces the total harmonic distortion and the low switching frequency reduces overall losses. In addition to the real power injection, the proposed PPCS enhances the grid power quality in the distribution system. A single-input–multiple-output single-ended primary inductance converter is designed to track the maximum power from the solar PV array and realizes isolated dc sources for the MLI. The performance of the IHBMLI-based PPCS is verified using extensive simulation studies in the MATLAB/Simulink. The PPCS is demonstrating the power factor correction by reactive power support and harmonic mitigation of the grid. Moreover, the performances are validated with experimental results from a hardware prototype.

Model of Choice Photovoltaic Panels Considering Customers’ Expectations

Photovoltaic electricity generation is key to achieving deep decarbonization with a high degree of electrification. It is predicted that the energy sector will reduce carbon dioxide by producing electricity mainly from photovoltaic (PV) power. Although dynamic development of the implementation of photovoltaic panels has been observed, their choice considering customer specificity is still a problem. Therefore, the purpose of this study is to propose the model of choice photovoltaic panels considering customers’ expectations. It can support the choice of a photovoltaic panel of a certain quality (satisfaction of concrete customer) in combination with the cost of its purchase. The proposed model includes acquiring and then processing customers’ expectations into technical criteria, while simultaneously considering the weighting of these criteria. It is realized in a standardized way, i.e., the zero-unitarization method (MUZ), after which normalized values of the quality of the photovoltaic panels’ criteria are obtained. In turn, the quality of these products is estimated by the weighted sum model (WSM) and then integrated with purchase cost in qualitative cost analysis (AKJ). As a result, using the scale of relative states, it is possible to categorize customer satisfaction from indicating qualitative cost and selecting the photovoltaic panel expected by customers (the most satisfactory). The effectiveness of the model was demonstrated by a sensitivity analysis, after which the key PV criteria were indicated. The proposed model is intended for any entity who selects a photovoltaic panel for customers. The computerization of calculations may contribute to its utilitarian dissemination.

Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location.

Application of Mathematical Model using Artificial Neural Network in Photovoltaic Electrical Power Generation Building

By counting the stock data of PV building integration-related companies in Shanghai and Shenzhen stock markets, a sector index model of PV building integration was established, moving averages were calculated and moving averages were plotted, and the prediction results of the model were used to compare with known data to optimize the model, and data from April 1, 2019 to May 6, 2021 were used as the training set and data from May 6, 2021 to May 28, 2021 were used as the test set by recurrent neural network and LSTM long short-term memory artificial neural network to optimize the model. as the training set and the data from May 6, 2021 to May 28, 2021 as the test set to train the optimized model to obtain the prediction results for the next month. Using the data from April 1, 2019 to May 28, 2021, a time period of 2 months is used to analyze the correlation between the SSE index and the PV building integration sector index for each period using Pearson correlation analysis. Finally, the time series forecasting model analysis is combined to assess the investment risk of individual stocks in the PV building integration sector.

Narrow-Bandgap Semiconductors of Perovskite Rare-Earth Orthoferrites (REFeO3)

Background: Great achievements have been made in improving the power conversion efficiencies of solar cells. However, the price of photovoltaic (PV)-generated electricity still cannot compete with that of conventionally generated power. Thus, novel solar cells made from non-toxic, earth-abundant, and chemically stable materials are desirable to decrease the costs of PV electricity generation. Objective: All-oxide solar cells are a promising next-generation PV technology, fulfilling the requirement of low-cost manufacturing under ambient conditions. This work aims to search for nontoxic, earth-abundant, and chemically stable narrow-bandgap semiconductors for energy applications like all-oxide solar cells. Methods: This work examines a family of perovskite rare-earth (i.e., Y, La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, and Yb) orthoferrites with XRD analysis, SEM, photoresponse, absorption spectra, Xray photoelectron spectroscopy, fluorescence spectra measured with the laser Raman spectrometer, valence band spectra, the Kelvin probe, the Hall Effect experiment, and theoretical calculation on band structure and the density of states, to screen for the narrow-bandgap semiconductors. Results: The novel Pb-free perovskite narrow-bandgap absorbers, CeFeO3, PrFeO3, TbFeO3, DyFeO3, HoFeO3, and YbFeO3, which are earth-abundant and non-toxic, were screened out potentially for use in all-oxide solar cells or other photovoltaic and optoelectronic applications. Among them, YbFeO3 is approved, having an indirect bandgap of approximately 1.0 eV with a maximum Shockley-Queisser efficiency of 31% for single p-n junction solar cells. Conclusion: The chemically stable, non-toxic, earth-abundant, and narrow-bandgap semiconductors of rare-earth orthoferrites promisingly serve as absorbers, photocatalysts, photoelectrodes, photodetectors, and photoelectronic materials. This work breaks new ground in the search for narrow bandgap all-oxide semiconductors potential for energy applications.

Photovoltaic-driven microbial protein production can use land and sunlight more efficiently than conventional crops

Population growth and changes in dietary patterns place an ever-growing pressure on the environment. Feeding the world within sustainable boundaries therefore requires revolutionizing the way we harness natural resources. Microbial biomass can be cultivated to yield protein-rich feed and food supplements, collectively termed single-cell protein (SCP). Yet, we still lack a quantitative comparison between traditional agriculture and photovoltaic-driven SCP systems in terms of land use and energetic efficiency. Here, we analyze the energetic efficiency of harnessing solar energy to produce SCP from air and water. Our model includes photovoltaic electricity generation, direct air capture of carbon dioxide, electrosynthesis of an electron donor and/or carbon source for microbial growth (hydrogen, formate, or methanol), microbial cultivation, and the processing of biomass and proteins. We show that, per unit of land, SCP production can reach an over 10-fold higher protein yield and at least twice the caloric yield compared with any staple crop. Altogether, this quantitative analysis offers an assessment of the future potential of photovoltaic-driven microbial foods to supplement conventional agricultural production and support resource-efficient protein supply on a global scale.

Efficient spatial allocation of solar photovoltaic electric energy generation in different regions of Brazil: a portfolio approach

ABSTRACT Several countries face the challenge of reducing the intermittency of solar photovoltaic generation due to changes in climatic conditions. Considering that Brazil has continental-scale dimensions and diverse geography, an alternative that allows the smoothing of the intermittency of solar photovoltaic generation is needed. The novelty of this study is the proposal to efficiently allocate the production of photovoltaic solar energy between the interconnected regions of the country, forming an optimal portfolio with the highest productivity and the least intermittency. Thus, the regional allocation process for solar generation is formulated through an optimization problem with two objective functions: to increase the country’s productivity and simultaneously control the variability of the supply of solar energy. This study concludes that the current models of planning for the expansion of Brazil do not consider the variability of solar irradiation incident in the five regions of Brazil.

Upgraded metallurgical grade silicon and polysilicon for solar electricity production: A comparative life cycle assessment

Solar grade silicon (SoGSi) is a key material for the development of crystalline silicon photovoltaics (PV), which is expected to reach the tera-watt level in the next years and around 50TW in 2050. Upgraded metallurgical grade silicon (UMGSi) has already demonstrated to be a viable alternative to standard polysilicon in terms of cost and quality. This study presents the life cycle assessment (LCA) of UMG obtained by the FerroSolar process. Moreover, it shows the environmental impacts of PV modules and electricity generation based on this material. For this, an exhaustive review of the life cycle inventory (LCI) of PV value chain, from metallurgical grade silicon (MG-Si) down to electricity generation, has been carried out updating inputs for all processes. The Balance of System (BoS) has also been updated with current state of the art data for a fixed open ground large PV site (100 MWpk). Two different electricity mixes, with low and high carbon intensities, have been considered. The results reveal that for PV electricity generation using UMG-Si instead of polysilicon leads to an overall reduction of Climate change (CC) emissions of over 20%, along with an improvement of the Energy Payback Time (EPBT) of 25%, achieving significantly low values, 12 gCO2eq/kWhe and 0.52 years, respectively. Moreover, it is shown that UMG silicon feedstock is not the main contributor to the carbon and energy footprint of the produced electricity, leaving the first place to PV module manufacturing.

Spraying Cooling System for PV Modules: Experimental Measurements for Temperature Trends Assessment and System Design Feasibility

The hallmark of the PhotoVoltaic (PV) electricity generation is its sustainability, while its main weakness is the low conversion efficiency. A drawback to which is added the PV cell sensitivity to temperature variations: the higher the cell operating temperature, the lower the efficiency. Considering that in-operation modules reach a conversion efficiency in the range of 10 to 15%, there is an urgent need to control their temperature to enhance the electricity generation. To this purpose, the authors developed a PV spraying cooling system able to drastically knockdown modules operating temperature. Using experimental measurements acquired through a dedicated test rig and after an in-depth literature review, the authors analyze the nozzles number, geometry, and position, as well as water and module’s temperature distribution, limestones formation, degradation of front glass properties, water consumption, and module power production with and without the cooling system. The experimental campaign shows that a cooling system equipped with three nozzles with a spraying angle of 90°, powered by water at 1.5 bar and managed in ON/OFF mode (30 s on to 180 s off), can improve the module’s efficiency from 11.18% to 13.27% thanks to a temperature reduction of up to 24 °C. Despite the improvement in electricity production (from 178.88 W to 212.31 W per single module), at the time of writing, the equipment and installation costs as well as the plant arrangement complexity make the investment not eligible for financing also in the case of a 1 MW floating PV facilities.

How PV systems can be cost-competitive for mobile BTS in remote areas of Algeria

Solar electricity for telecom infrastructure has became one of the main contributors of electrical energy to the energy system in the future, but solar technology needs support to find its way to the market. Since photovoltaic (PV) electricity always involves very high costs compared to electricity from other sources, a striking question is: how can PVs achieve competitiveness? There are different strategies for promoting applied PV electricity generation around the world. Here, we simulated and designed a 40 KWp injected PV power, to evaluate the energy of the incident photon flux, that produced by PV panels and the energy injected into the electricity grid, and judge whether the installation is profitable, and if the technology is economical. The results reveal that the site of Tamanrasset (in the extreme south of Algeria) could produce an annual energy production 73879 kWh, which justify the reason the German Academy has chosen such site for the famous desert project, lastly launched between the two countries.

How PV systems can be cost-competitive for mobile BTS in remote areas of Algeria

Solar electricity for telecom infrastructure has became one of the main contributors of electrical energy to the energy system in the future, but solar technology needs support to find its way to the market. Since photovoltaic (PV) electricity always involves very high costs compared to electricity from other sources, a striking question is: how can PVs achieve competitiveness? There are different strategies for promoting applied PV electricity generation around the world. Here, we simulated and designed a 40 KWp injected PV power, to evaluate the energy of the incident photon flux, that produced by PV panels and the energy injected into the electricity grid, and judge whether the installation is profitable, and if the technology is economical. The results reveal that the site of Tamanrasset (in the extreme south of Algeria) could produce an annual energy production 73879 kWh, which justify the reason the German Academy has chosen such site for the famous desert project, lastly launched between the two countries.

A European Assessment of the Solar Energy Cost: Key Factors and Optimal Technology

Solar energy has become one of the most important sources of energy all around the world. Only in the European Union, between 2010 and 2019, solar photovoltaic (PV) electricity generation capacity increased from 1.9 to over 133 GW. Throughout this work, an economic analysis of the production of photovoltaic solar energy utility scale facilities is performed, previously defining some theoretical concepts relating to electricity generation by means of photovoltaic modules, as well as commenting on studies that have inspired the project. In order to carry out this economic analysis, the locations of twenty capital cities within European Union countries are selected, in order to estimate their yearly solar PV energy produced under specific conditions. The Levelized Costs of Energy (LCOE) is calculated with the goal of comparing the profitability of each photovoltaic tracking technology: fixed, one-axis tracking systems (vertical or inclined) and two-axis tracking systems; including LCOE maps country-wise for each technology. A sensitivity analysis is also presented, in order to evaluate the significance and impact of the main variables involved in the analysis. The results show that one-axis tracking systems are the best option in all countries, reducing LCOE by more than 20% when compared to two-axis tracking system. The impact of wages is also significant. In higher latitudes, in most cases, wages also increase, hence the LCOE is higher and consequently less interesting for a potential investor.

Water and energy management in a sustainable site: A case study of a living environment for street children in Tehran, Iran

Urban development of major cities in Iran, especially in metropolises like Tehran, triggered an ever-increasing water and energy demand leading to water crisis deterioration and electricity generation deficiency. Building energy demand is either provided by fossil fuels or fossil-fuel-generated electricity in Iran, so the energy resources are mainly non-renewable. The current study addressed such an unbalanced pattern by proposing an applicable cross-disciplinary methodology for a sustainable urban design. A case study was conducted in a site located in northern Tehran, and multiple sustainable design alternatives were examined. Water, electricity, and energy demands were compared between a conventional design and a sustainable one. Water and energy management scenarios were defined, encompassing rainwater harvesting, greywater recycling, efficient landscape plantation and irrigation, water treatment, reducing construction materials' heat transfer, and photovoltaic electricity generation on site. Evaluations showed promising results indicating: 69.73 % less water demand from city water supply; 68.60 % less electricity demand from national grid; 43 % reduction in cooling and 56 % reduction in heating loads. The decentralized water management system reduced the risk of over flooding in the city wastewater network. Furthermore, a shift to renewable energy resources was achieved, and fossil fuel consumption consequences were alleviated by applying a grid-connected photovoltaic system.

Short-term forecast of photovoltaic power generation output based on improved PSO-Elman neural network

An improved particle swarm optimization (PSO) algorithm is proposed to solve the problems of low power prediction accuracy and high randomness of PV power generation. In the first place, by changing the inertia weight and learning factor and concept of the hybrid genetic algorithm is introduced into the particle swarm optimization algorithm, determine the initial weight of the Elman neural network, establish Elman neural network, then using day feature vector selection and forecasting meteorological features high similarity of date, determine the main meteorological factors affecting the photovoltaic output, meteorological data and electricity generation are used as training sets of Elman neural network, prediction model is established. Finally, the simulation results show that the model is superior to the Without optimization Elman neural network, has higher prediction accuracy, and shows good stability and generalization ability.

A configuration optimization framework for renewable energy systems integrating with electric‐heating energy storage in an isolated tourist area

AbstractThe renewable energy system integrates various power generating units, heating units, energy conversion devices, and energy storage devices, which is an effective way to improve energy efficiency and realize clean energy utilization. In this paper, a renewable energy system integrating with photovoltaic electric power generation systems, wind‐driven generators, gas‐driven generators, gas‐fired boilers, electric chillers, electric and heating energy storage devices is constructed in an isolated tourist area. Considering the complex multi‐energy flows in renewable energy systems, an energy hub with power and heat is established to concentrate and distribute electric and heating energy flow for balancing energy supply and demand in a CCHP system. Introducing the constraints of renewable energy penetration index and energy supply reliability index, a collaborative optimization framework for system configuration is constructed to minimize annual capital cost, fuel cost, and maintenance cost. The simulation results show that the model and algorithm in this paper are feasible and applicable, and the optimization scheme has good economic benefits and optimal performance, which can promote the consumption of renewable energy and reduce the surplus of electric energy and heat energy.

Snapshot of photovoltaics − March 2021

For the past 10 years, photovoltaic electricity generation has been the fastest-growing power generation source worldwide. It took almost six decades to achieve 100 GW of solar energy capacity in 2012, but the 1 TW barrier is likely to be broken during 2022. Despite the ongoing COVID-19 pandemic, the overall investments in solar energy have increased by 12% to USD 148.6 billion (EUR 125 billion). In 2020, more than 135 GW of new solar photovoltaic electricity generation capacity was installed. The recovery of China, the continuous growths in Europe and the USA as well as new emerging markets were the main drivers. The number of countries installing more than 1 GW annually has increased to 18 in 2020. The continuation of price reductions in the battery storage sector has again resulted in a growing market for local battery storage systems in solar farms as well as decentralised photovoltaic electricity generation systems. Apart from classic electricity use, renewable electricity for the generation of green hydrogen will become more and more important in the future.

A novel policy to tackle life span reassessment of existing bulk and thin-film photovoltaic materials

With the growing planetary demands of clean energy, solar photovoltaic system electricity generation has become mandatory in today's world. At the same time, when we look towards photovoltaic waste volume in India, it is estimated to burst forth to 200,000 by 2030 and around 1.8 million tons by 2050. Therefore, there is a dire need to scale up the PV recycle method from lab to full-fledged industry. Most of the developed countries such as China, UK, Japan, etc. have ensued their PV scarp management policies but in context to Indian prospective, it is yet to be established. The paper focuses on the effective remedy of environmental and socio-economic impact arising during the life span fulfilment of c-Si, CdTe, CdS and CIS. Over here, a sustainable novel policy framework is put forward to potentially tackle the above-mentioned problem which is still to be adopted by the Indian Government for further implementation.

A novel policy to tackle life span reassessment of existing bulk and thin-film photovoltaic materials

With the growing planetary demands of clean energy, solar photovoltaic system electricity generation has become mandatory in today's world. At the same time, when we look towards photovoltaic waste volume in India, it is estimated to burst forth to 200,000 by 2030 and around 1.8 million tons by 2050. Therefore, there is a dire need to scale up the PV recycle method from lab to full-fledged industry. Most of the developed countries such as China, UK, Japan, etc. have ensued their PV scarp management policies but in context to Indian prospective, it is yet to be established. The paper focuses on the effective remedy of environmental and socio-economic impact arising during the life span fulfilment of c-Si, CdTe, CdS and CIS. Over here, a sustainable novel policy framework is put forward to potentially tackle the above-mentioned problem which is still to be adopted by the Indian Government for further implementation.

Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study

A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26–32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity generation potential. The outcomes are proof that the model is economically viable and could be employed by local governments to plan solar harvesting strategies. The results are generated from an open source model that allows calculating the available solar radiation over specific land areas, and the application potential for future planning of solar energy projects is evident.