Reliable energy prediction method for grid connected photovoltaic power plants situated in hot and dry climatic condition

Chakraborty, Suprava

doi:10.1007/s42452-020-2122-8

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Table 2 lists the energy production of PVs by several parameters processed by database [35]. The peak value of the average daily sum of global irradiation received by the modules, H(i) d , is H(i) d = 7.28 kWh/m 2 /d, which is consistent with the literature's [4] reference peak. Figure 5 shows the results in terms of the air temperature and PV energy production, E m (Table 2), for each month.…”

Section: Analyses and Resultssupporting

confidence: 69%

“…PVPs are treated in the literature at the level of forecasting power generations, where several time series prediction statistical methods and algorithms on artificial intelligence are introduced, investigating the effect of prediction time horizon variation [1] and of the design in extreme conditions, where mathematical models to predict the energy generation of PVPs in hot and humid climatic condition are studied [4]. In [1,4], some parameters were studied that impact the electrical power generation, e.g., the type of solar cells and their conditions, electrical circuits of modules, solar incidence angle, and weather conditions.…”

Section: Introductionmentioning

confidence: 99%

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Direct Method to Design Solar Photovoltaics to Reduce Energy Consumption of Aeration Tanks in Wastewater Treatment Plants

Zacchei

Colacicco²

2022

Infrastructures

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Photovoltaic (PV) energy systems are considered good renewable energy technologies due to their high production of clean energy. This paper combines a PV system with wastewater treatment plants (WWTPs), which are usually designed separately. For this, a recent methodology was adopted, which provides direct steps to estimate the peak powers of PV plants (PVPs) by using the airflow of blowers. The goal was to reduce the energy consumption of aeration tanks in WWTPs. Analytical equations and parameters based on the air temperature, solar irradiation, biological kinetic, dissolved oxygen, and mechanical oxygenation are adopted. The key parameter in this methodology is the air temperature variation that represents an approximated temperature in the WWTP’s oxidation tanks. It is shown, through the analysis of small WWTPs, that since the temperature changes for each season, there is a peak in the function of the quantity of oxidation, which is high in the summer season. Further, the curve trends of temperature for WWRPs are similar to PVPs. Therefore, it could be possible to design the PV system with the WWTPs well. The results show that the air temperature curves increase in a directly proportional way with the consumption of energy from oxidation blowers; this could induce a more conservative PVP design. Furthermore, the results show that the mean trend of the energy consumption of the analyzed aeration systems reaches about 8.0% at a temperature of 20–25 °C, covering a good part of the oxidation tank consumption.

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Section: Analyses and Resultssupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Direct Method to Design Solar Photovoltaics to Reduce Energy Consumption of Aeration Tanks in Wastewater Treatment Plants

Zacchei

Colacicco²

2022

Infrastructures

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“…Cao and Cao [7] use recurrent neural networks integrated with wavelet analysis and they satisfactorily forecast the daily solar irradience using data for Shanhai. Chakraborty [8] suggests a mathematical model for predicting solar energy generated by winter, if a solar power plant has very little power generation whereas the other solar plants in the near locations have considerably higher power generation, this may be an indication of a measurement error. Uncleaned snow above solar panels or break downs may cause such situations.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

A weather-based forecasting system for the solar power plants in the Konya region

YAVUZ¹,

Botsalı

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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Increasing energy demand along with decreasing environmental resources necessitates looking for alternative energy sources. With this respect, solar power has gained considerable importance over recent years. This study analyzes the forecasting problem for the amount of electric power generated by solar power plants. The amount of electric power generated by solar power plants is not constant and changes depending on several variables such as the weather conditions, seasonal effects, t ype of solar panel, etc. On the other hand, to meet the electric power demand and minimize electric transfer cost, forecasting the electric power generated by solar power plants is critical. We test several neural network models with various weather-related input parameters. Among these parameters, we choose the most promising ones (radiation, humidity, hour, month) for further analysis to forecast the electric power generated by solar power plants located in the Konya region. Our test results over the past data show that it is possible to forecast the electric power generated by solar panels in the Konya region with less than 5% error.

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“…The output current of the PV module is a function of the solar irradiance, short-circuit current, temperature coefficient of current, and temperature of PV module, shown by Equation ( 12) [63].…”

Section: Practical Sizing Of Pv Dgmentioning

confidence: 99%

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

et al. 2021

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The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

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Reliable energy prediction method for grid connected photovoltaic power plants situated in hot and dry climatic condition

Cited by 4 publications

References 29 publications

Direct Method to Design Solar Photovoltaics to Reduce Energy Consumption of Aeration Tanks in Wastewater Treatment Plants

Direct Method to Design Solar Photovoltaics to Reduce Energy Consumption of Aeration Tanks in Wastewater Treatment Plants

A weather-based forecasting system for the solar power plants in the Konya region

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

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