Global assessment for reduction of solar photovoltaic potential due to meteorological and geomorphological limiting factors

Author(s):  
Mustajab Ali ◽  
Hyungjun Kim

<p>Solar Photovoltaic (PV) has the potential to fulfill a considerable amount of growing electricity demands worldwide.  In addition, being neat and clean, it can help to keep the greenhouse gases emission within safe limits. This resource needs a substantial amount of area for its sitting to supply the required amount of electricity. Such an area mainly depends on the available solar resource which is mainly the function of the local environment where PV is installed. Although some previous studies exist at the global scale, however, they have not comprehensively considered environmental (e.g., temperature, dust deposition, and snow) limiting factors that affect the actual solar PV yield. This study addresses such shortcomings and deals with all limiting factors simultaneously to provide a reliable assessment of potential PV performance at a global scale. PV cell efficiency is reduced due to an increase in resistance between cells at a temperature above a certain limit. Meanwhile, the accumulation of soil (dust) and snow on PV modules are also proven to limit the solar PV resources as it tends to block the incoming solar radiation. Lastly, the geomorphological parameter, which is an arrangement of a PV module to face the sun, is also shown to change its power output.</p><p>PV cell efficiency corrections for temperature changes, soil, and snow covers are applied using the biased corrected data from Global Soil Wetness Project 3 (GWSP3), CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2 from National Snow and Ice Data Center (nsidc), and TERRA/MODIS Aerosol Optical Thickness data available from NASA Earth Observations (NEO). The daily mean solar climatological values near the Earth’s surface for the last 14 years (2001–2014) with global coverage of 0.5º x0.5º are used in the analysis. The results have demonstrated that PV performance is affected by temperature increase, soil, snow, and varying tilt-angles. An annual maximum reduction of 5.7% in the total solar PV resource is seen in the Middle East due to the temperature changes. Likewise, a maximum loss of 6.45% in the total solar PV resource is witnessed for soil deposition for Sub-Saharan Africa. A higher reduction (~20%) is shown by snow covers for Russia and Canada in the upper Northern Hemisphere. In addition, a decline of 5–7% is observed for variation in the solar PV tilt-angles in comparison to optimum ones. As a whole, a maximum reduction of 19.45% in the total solar PV resource is found, which leads to a higher coefficient of determination (R<sup>2</sup>= 0.78) than uncorrected estimation (R<sup>2</sup>=0.67). This study will be helpful for household as well as large scale solar schemes and may contribute particularly to achieving the UN SDG No. 07 — Affordable and Clean Energy — and No. 13 — Climate Action — quantitatively.</p>

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6418
Author(s):  
Ruxu Sheng ◽  
Juntian Du ◽  
Songqi Liu ◽  
Changan Wang ◽  
Zidi Wang ◽  
...  

Solar photovoltaic (PV) has become the fastest-growing new energy in China and one of the main contributors to China’s clean energy transition. From 2013 to 2019, China’s solar PV installed capacity grew from 15,890 MW to 204,180 MW, increasing by 11.85 times. To explore solar PV investment changes across China regions, we use spatial shift-share analysis model to decompose solar PV investment changes from 2013 to 2019 into four components: national energy investment growth effect (NEG), national energy investment structure effect (NES), neighbor–nation solar PV investment competitive effect (NNC), and region–neighbor solar PV investment competitive effect (RNC). Based on the decomposition results, we find that the value of NNC of most western provinces is negative for the entire period, while the NNC of most central and eastern provinces is in the middle and lower range. There is little difference in RNC among these regions. While comparing the influence caused by the four effects, NNC and RNC play dominant roles in solar PV investment changes in eastern and central provinces, which means NEG and NES have relatively small impacts. By contrast, NEG and NES affect the solar PV investment changes at a larger scale in most western provinces. Comparing the NNC and RNC, we find that RNC played a prominent role in the eastern and central regions, while NNC played a dominant role in the west.


Author(s):  
Bambang Purwahyudi ◽  
Kuspijani Kuspijani ◽  
Ahmadi Ahmadi

Solar photovoltaic (PV) cell is one of the renewable energy sources and a main component of PV power systems. The design of PV power systems requires accurately its electrical output characteristics. The electrical characteristics of solar PV cell consist of I-V and P-V characteristics. They depend on the parameters of PV cell such as short circuit current, open circuit voltage and maximum power. Solar PV cell model can be described through an equivalent circuit including a current source, a diode, a series resistor and a shunt resistor. In this paper, the development solar PV cell model is built by using self constructing neural network (SCNN) methods. This SCNN technique is used to improve the accuracy of the electrical characteristic of solar PV cell model. SCNN solar PV cell model have three inputs and two outputs. They are respectively solar radiation, temperature, series resistance, current and power. The effectiveness of SCNN technique is verified using simulation results based on different physical and environmental conditions. Simulations are conducted by the change of the solar irradiation, temperature and series resistance. Simulation results show SCNN model can yield the I-V and P-V characteristics according to the characteristics of solar PV cell.


Author(s):  
Jesse Dean ◽  
Alicen Kandt ◽  
Kari Burman ◽  
Lars Lisell ◽  
Christopher Helm

As the demand for renewable energy has grown, so too has the need to quantify the potential for these resources. Understanding the potential for a particular energy source can help inform policy decisions, educate consumers, drive technological development, increase manufacturing capacity, and improve marketing methods. In response to the desire to better understand the potential of clean energy technologies, several approaches have been developed to help inform decisions. One technology-specific example is the use of solar photovoltaic (PV) maps. A solar PV mapping tool visually represents a specific site and calculates PV system size and projected electricity production. This paper identifies the commercially available solar mapping tools and provides a thorough summary of the source data type and resolution, the visualization software program being used, user inputs, calculation methodology and algorithms, map outputs, and development costs for each map.


Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2094
Author(s):  
Shoeb Ahmad Khan ◽  
Shafiq Ahmad ◽  
Adil Sarwar ◽  
Mohd Tariq ◽  
Javed Ahmad ◽  
...  

The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.


Author(s):  
Kadhim H. Hassan ◽  
Abdulmuttalib T. Rashid ◽  
Basil H. Jasim

Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.


2020 ◽  
Vol 9 (1) ◽  
pp. 7-22 ◽  
Author(s):  
Manoharan Premkumar ◽  
Chandrasekaran Kumar ◽  
Ravichandran Sowmya

This paper discusses a modified V-I relationship for the solar photovoltaic (PV) single diode based equivalent model. The model is derived from an equivalent circuit of the PV cell. A PV cell is used to convert the solar incident light to electrical energy. The PV module is derived from the group of series connected PV cells and PV array, or PV string is formed by connecting the group of series and parallel connected PV panels. The model proposed in this paper is applicable for both series and parallel connected PV string/array systems. Initially, the V-I characteristics are derived for a single PV cell, and finally, it is extended to the PV panel and, to string/array. The solar PV cell model is derived based on five parameters model which requires the data’s from the manufacturer’s data sheet. The derived PV model is precisely forecasting the P-V characteristics, V-I characteristics, open circuit voltage, short circuit current and maximum power point (MPP) for the various temperature and solar irradiation conditions. The model in this paper forecasts the required data for both polycrystalline silicon and monocrystalline silicon panels. This PV model is suitable for the PV system of any capacity. The proposed model is simulated using Matlab/Simulink for various PV array configurations, and finally, the derived model is examined in partial shading condition under the various environmental conditions to find the optimal configuration. The PV model proposed in this paper can achieve 99.5% accuracy in producing maximum output power as similar to manufacturers datasheet.©2020. CBIORE-IJRED. All rights reserved


2019 ◽  
Vol 8 (2S11) ◽  
pp. 2136-2138

Solar photovoltaic systems are most commonly used renewable resources nowadays. These pv cells are eco-friendly, pollution free, easy in construction and are compact in size. The solar pv cell generates electricity by simply tracking the sun rays. The main advantage of solar pv cell is that it is dependent on sun light and the sun is available everywhere. The power generated by photovoltaic cell has less efficiency. Thus, this paper proposes the hybrid maximum power point tracker for improving the efficiency of generated energy of grid connected solar pv system.\


2021 ◽  
Vol 43 ◽  
pp. 2843-2849 ◽  
Author(s):  
Bhuwan Pratap Singh ◽  
Sunil Kumar Goyal ◽  
Prakash Kumar
Keyword(s):  
Solar Pv ◽  
Pv Cell ◽  

Author(s):  
Rahul Bisht ◽  
Afzal Sikander

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.


Sign in / Sign up

Export Citation Format

Share Document