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The Determination of Concession Period for Build-Operate-Transfer Solar Photovoltaic Power Project under Policy Incentives: A Case Study of China
摘要： Since the concession period is one of the most crucial variables influencing the success of a photovoltaic (PV) power project under build-operate-transfer (BOT) mode, this paper presents a real option game model—which integrates the real option and cooperative game theory—to determine the optimal concession period of the BOT solar PV power project under policy incentives by considering the value of the option to defer. In the proposed model, an effective interval of concession period for the BOT project was identified by using a real option, and the equilibrium value of the concession period was obtained by applying classical optimization theory. To evaluate our model, we empirically determined the optimal concession period for the BOT solar PV power project in China, and a sensitivity analysis was conducted to demonstrate how relevant influential factors, which are presented in the model, affect the equilibrium value of the concession period and its effective interval. The results indicate that the current investment environment in China could successfully implement the BOT solar PV power project under policy incentives, including initial cost subsidy and the feed-in tariffs mechanism. While the high volatility of electricity demand, investment cost, and land-use rent fee can lead the failure of the BOT solar PV project implementation, promoting the technological development of PV power generation, maintaining the market stability, and increasing the policy incentives can help the BOT power project to be arranged with an optimal concession period. In addition, the feed-in tariffs mechanism is more helpful than the initial cost subsidy for winning the BOT contract.
关键词： build operate transfer,concession period,China,photovoltaic power generation
更新于2025-09-23 15:21:01
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Understanding the effect of solvent additive in polymeric thin film: turning a bilayer in a bulk heterojunction like photovoltaic device
摘要： Here we report the effect of an additive solvent, the 1,8-diiodooctane (DIO) on the performance of a bilayer organic photovoltaic (OPV) device which active layer comprises the poly[2,7-(9,9-bis(2 ethylhexyl)-dibenzosilole)-alt-4,7-bis(thiophen-2-yl)benzo-2,1,3-thiadiazole] (PSiF-DBT) as the electron donor material and C60 as the electron acceptor material. We observed that when the donor layer was treated with 1% of DIO the power conversion efficiency (PCE) of the device increase 138.4% in relation to the device with a non-treated donor layer and 21.3% in relation to the device containing a donor layer submitted to a thermal annealed. The main effects that lead to this increase in PCE are the large interfacial area between donor and acceptor materials and the improved conductivity at low voltages. The increase in polymer surface roughness leads to a more effective PSiF-DBT/C60 interface for exciton dissociation. This effect, as well as the increase in the conductivity, raised the short circuit current density (JSC) to 13.89 mA/cm2 and PCE to 4.84%. Our conclusions are supported by morphological analysis, chemical cross-sectional evaluations with advanced microscopy techniques, charge mobility measurements as well as by theoretical simulations of the devices in which the changes on the donor/acceptor interfacial area were considered. The outcomes suggest that, solvent additives could be an alternative treatment to replace the thermal annealing which imposes further difficulties to perform the lab-to-manufacturing upscaling.
关键词： solvent additive,PSiF-DBT,exciton dissociation,1,8-diiodooctane,C60,organic photovoltaic,power conversion efficiency
更新于2025-09-23 15:21:01
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Beijing, China (2019.11.21-2019.11.23)] 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Analysis of the Impact of MW-level Grid-connected Photovoltaic Power Station on Backup Automatic Switch in Distribution Network
摘要： With the continuous improvement of grid-connected photovoltaic power station capacity, the influence on the reliable operation of backup automatic switch equipment in the distribution network is also increasing. The impact of PV on the backup automatic switch is mainly due to the fact that after the bus line is de-energized, the PV will continue to supply power to the busbar, which will affect the judgment of the busbar without pressure. In this paper, according to the setting value of backup automatic switch protection commonly used in distribution network, under different load-to-source capacity ratio, the influence of PV on the reliability of the backup automatic switch action and the change of the bus voltage of the PV-connected busbar after the main supply line is de-energized are quantitatively analyzed. Through simulation verification, it is proposed that the most fundamental method to avoid the influence of PV on backup automatic switch is to disconnect the PV power station after failure or loss of system power supply, so as to prevent the occurrence of isolated islands.
关键词： MW-level photovoltaic power station,load-to-source capacity ratio,loss of busbar voltage,the backup automatic switch
更新于2025-09-23 15:21:01
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Structure-Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach
摘要： A study workflow that utilizes several data science methods to apply on polymer materials databases is introduced to reveal correlations among their properties, structural information, or molecular descriptors. The data science methods used in this pipeline include unsupervised machine learning (ML) method self-organizing mapping (SOM) and polymer molecular descriptor generator, both of which have been tailored to fit the polymer materials study. To demonstrate how this pipeline can be applied in this context, we used it on an organic photovoltaic (OPV) donor polymer database to investigate which properties or structural factors positively correlate with the power conversion efficiency (PCE) of OPV materials. This led us to discover that among the studied 8 properties and 11 molecular descriptors, only the photon energy loss (Eloss) and the number of fluorine atoms (nF) show strong positive correlations with PCE values, which is consistent with other verified studies. We also discovered that research trends can also be statistically visualized using our method. In our case study, we found that most of the studied OPV donor materials in the database have branched side chains and typically 7 to 12 non-Hydrogen atoms, and high PCE materials usually have 6 to 9 aromatics rings as well. These results proved that the data science pipeline proposed in this study provides a fast and effective way to obtain research insights for polymer materials.
关键词： self-organizing mapping,data science,molecular descriptors,polymer materials,organic photovoltaic,power conversion efficiency
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network
摘要： Data-driven modeling is one of the research hotspots of photovoltaic (PV) power prediction. However, for some newly built PV power plants, there are not enough historical data to train an accurate model. Therefore, constructing a forecasting model for the PV plants lacking historical data is an urgent problem to be solved. In this paper, we propose a method to transfer the knowledge obtained from historical solar irradiance data to the output prediction. Firstly, the based on hyperparameters of the long short-term memory neural network (LSTM) are optimized and the weights in the neurons are pre-trained, then fine-tuning the deep transfer model with PV output data. In this way, knowledge can be transferred to PV output data. The from solar experimental results show that the proposed method can significantly reduce the prediction error.
关键词： Long short-term memory,Transfer learning,Photovoltaic power forecasting,Hyperparameter optimization,Data mining
更新于2025-09-23 15:21:01
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Conceptual study of photovoltaic power plant connected to the urban electrical network in northern Algeria
摘要： This present research paper aims at showing the active role of the building equipped with photovoltaic (PV) plant in terms of energy, economy, and ecology and its impact on the development of society. Since the installation of PV plants on buildings is a rarely used technology in Algeria, we intend to conceive 30 kWp medium power plants on pilot administrative buildings, which will be generalized to two other buildings of the same type, connected to urban electricity network so as to reduce the consumption of electricity which costs much money to the local authorities, the pilot site is located in the town of EL-ATTAF, in the northwestern of Algeria. It is Mediterranean climate with average radiation of 5.14 kWh/m2/day and average yearly temperature of 19.4°C. The study highlights the technical and financial feasibility of the system based on a simulation with Homer which estimates a load of 41610kWh/year, energy production of 47872 kWh/year, an optimal angle of inclination of 32°, saving of 454 m3/year of natural gas and avoiding huge quantities of pollutants (CO2: 549 kg/year; SO2: 9.6 g/year; NOx: 0.5 kg/year).
关键词： Photovoltaic power plant,BIPV,economic,grid-connected,environmental
更新于2025-09-23 15:21:01
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[IEEE 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Ghaziabad (2018.2.9-2018.2.10)] 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Development of a Decision-Based Neural Network for a Day-Ahead Prediction of Solar PV Plant Power Output
摘要： Day-ahead photovoltaic power prediction is vital for policy making and providing necessary backup capacities. Previous researchers include the implementation of time series, auto-regression and Soft computing techniques like Artificial Neural Networks and Fuzzy Logic. Artificial Neural Networks provides a better fit to complex, non-linear and error-prone data. The paper shows a comparative study of a Radial Basis Neural Network Schema (exact fit), a ‘k-means’ Radial Neural Network, and a Feed Forward Neural Network with Levenberg-Marquardt error backpropagation designed for the prediction of power output at an hourly resolution. The ability of the Neural Network to be trained to adapt to a previous set of data and then interpolate or extrapolate to the new data set has been exploited. The proposed model uses five meteorological variables and uses recorded data collected from the SN Mohanty PV Power Plant. Training of neural network is done on a monthly basis so that normalization constants of variables can be lower and better mapping can be produced. An improved decision-based schematic using Neural Networks is proposed which combines the advantages of both Radial Basis Function (exact fit) and FFNN.
关键词： solar photovoltaic power plant,Radial Basis,Artificial Neural Network,Decision-based,ANN
更新于2025-09-23 15:21:01
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A new approach for photovoltaic module cooling technique evaluation and comparison using the temperature dependent photovoltaic power ratio
摘要： Many photovoltaic module cooling techniques are available to reduce the solar cell temperature, resulting in enhanced e?ciency. Although the power of the photovoltaic module is usually reported as a measure for the performance of the cooling technique, the performance assessment and comparison among di?erent coolers become di?cult if di?erent photovoltaic module’s reference power is being utilized. The existing method requires calculations to be done repeatedly to obtain the photovoltaic module’s power, for any given value of the reference power. In order to compare the performance of the coolers, the use of the same reference power is needed, resulting in a lengthy process. Hence, a new assessment method is proposed, based on the temperature dependent photovoltaic module’s power ratio that is de?ned and derived. The new method identi?es the relevant parameters that are essential for measuring the performance of the cooler such as the power of a photovoltaic module with a cooler and the reference power at photovoltaic module’s standard test conditions. The outcome is that the calculation of the unknown power for di?erent reference power can be instantly obtained and the performance comparison among di?erent coolers become simple without going through the lengthy process as it is in the case of the existing method. It is shown that the proposed method has the same results as the existing method which is experimentally validated. This is evidence to support the new method which may have potential to be applied by photovoltaic module cooling techniques designers.
关键词： Photovoltaic power ratio,Comparison,Cooling techniques,Photovoltaic module,Evaluation,Photovoltaic e?ciency
更新于2025-09-23 15:19:57
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Modeling the forecasted power of a photovoltaic generator using numerical weather prediction and radiative transfer models coupled with a behavioral electrical model
摘要： The intermittency of the solar radiation is the big challenger for the management of electrical SMART GRID network. So the forecasting of the solar radiation remains the crucial objective to anticipate the injection of the electrical power produced by a photovoltaic generator. In this study, we use the Numerical Weather Prediction model WRF coupled to a radiative transfer model libRadtran to forecast the global, direct and diffuse solar radiations. The NMM core of WRF model was used to predict the vertical profiles of the atmospheric meteorological parameters such as liquid and ice water concentration, cloud cover, relative humidity, temperature, etc. These data are used as input in libRadtran to calculate the hourly solar radiations components (Direct, Diffuse and Global) in real meteorological situations. The radiation values are obtained on regular geographical grid points. A behavioral electrical power model is used to compute the electrical power produced by a photovoltaic generator located at the Center of Development of Renewable Energy in the city of Algiers in Algeria. The comparative study shows well that the results are very encouraging.
关键词： Solar radiation,Photovoltaic power,libRadtran,WRF
更新于2025-09-23 15:19:57