Comparison and Sensitivity Analysis of Methods for Solar PV Power Prediction

2018 ◽  
pp. 333-344 ◽  
Author(s):  
Mashud Rana ◽  
Ashfaqur Rahman ◽  
Liwan Liyanage ◽  
Mohammed Nazim Uddin
Keyword(s):  
Sensitivity Analysis ◽  
Solar Pv ◽  
Power Prediction

Techno-Economic Analysis of Solar e-Cooking Systems for Rural Communities in Nigeria

2021 ◽  
Vol 107 ◽  
pp. 203-208
Author(s):  
Ogheneruona E. Diemuodeke ◽  
Michael Orji ◽  
Clinton Ikechukwu ◽  
Yacob Mulugetta ◽  
Youba Sokona ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Economic Policy ◽  
Rural Communities ◽  
Economic Analysis ◽  
Clean Energy ◽  
Solar Pv ◽  
Battery Storage ◽  
Cost Of Energy ◽  
Induction Cooking ◽  
Design Analyses

This paper presents solar PV electric cooking systems to fill the gap of clean energy stove demand in Africa and in particular in rural communities. The design analyses of four different solar PV electric cooking configurations, based on resistive burner and induction burner, are presented. The levelised cost of energy (LCOE) of the solar PV induction e-cooking, with battery storage, is 0.39 $/kWh. Sensitivity analysis was done to ascertain the affordability range of solar PV e-cooking. It was shown that the combination of the reduced cost of investment and good sunshine would most likely make the solar PV induction e-cooking competitive. However, the acceptability of the solar PV induction cooking will require addressing some important technical, economic, policy and socio-cultural related barriers.

scholarly journals Solar Photovoltaic Power Prediction Using Big Data Tools

2021 ◽  
Vol 13 (24) ◽  
pp. 13685
Author(s):  
Mariz B. Arias ◽  
Sungwoo Bae
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Solar Irradiance ◽  
Weather Data ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Power Prediction ◽  
Pv System ◽  
Generation Planning ◽  
Solar Pv System

Solar photovoltaic (PV) installation has been continually growing to be utilized in a grid-connected or stand-alone network. However, since the generation of solar PV power is highly variable because of different factors, its accurate forecasting is critical for a reliable integration to the grid and for supplying the load in a stand-alone network. This paper presents a prediction model for calculating solar PV power based on historical data, such as solar PV data, solar irradiance, and weather data, which are stored, managed, and processed using big data tools. The considered variables in calculating the solar PV power include solar irradiance, efficiency of the PV system, and characteristics of the PV system. The solar PV power profiles for each day of January, which is a summer season, were presented to show the variability of the solar PV power in numerical examples. The simulation results show relatively accurate forecasting with 17.57 kW and 2.80% as the best root mean square error and mean relative error, respectively. Thus, the proposed solar PV power prediction model can help power system engineers in generation planning for a grid-connected or stand-alone solar PV system.

Sensitivity Analysis Based Identification of Key Parameters in the Dynamic Model of a Utility-Scale Solar PV Plant

2021 ◽  
pp. 1-1
Author(s):  
Balaji Guddanti ◽  
Ramirez Jorge Orrego ◽  
Rajarshi Roychowdhury ◽  
Mahesh S Illindala
Keyword(s):  
Sensitivity Analysis ◽  
Dynamic Model ◽  
Solar Pv ◽  
Utility Scale

scholarly journals Voltage Profile and Sensitivity Analysis for a Grid Connected Solar, Wind and Small Hydro Hybrid System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3555
Author(s):  
Noah Serem ◽  
Lawrence K. Letting ◽  
Josiah Munda
Keyword(s):  
Sensitivity Analysis ◽  
Renewable Energy ◽  
Wind Power ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Test System ◽  
Solar Pv ◽  
Voltage Profile ◽  
Hydro Power ◽  
Power Injection

Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a grid that is highly penetrated by renewable energy sources; solar PV, wind power and small hydro systems. Analysis is done on IEEE 39 bus test system with Wind power injection alone, PV power injection alone, with PV and wind power injection and with PV, wind and micro hydro power injection to the grid. The analysis is used to determine the buses where voltage stability improvement is needed. From the results, it was concluded that injection of the modeled wind power alone helped in stabilizing the voltage levels as determined from voltage profiles and reactive power margins. Replacing some of the conventional sources with PV power led to reduction of voltages for weak buses below the required standards. Injection of power from more than one renewable energy source helped in slightly improving the voltage levels. Distribution Static compensators (D-STATCOMs) were used to improve the voltage levels of the buses that were below the required standards.

Deep learning and wavelet transform integrated approach for short-term solar PV power prediction

Measurement ◽  
2020 ◽  
Vol 166 ◽  
pp. 108250 ◽  
Author(s):  
Manohar Mishra ◽  
Pandit Byomakesha Dash ◽  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Subrat Kumar Swain
Keyword(s):  
Deep Learning ◽  
Wavelet Transform ◽  
Integrated Approach ◽  
Solar Pv ◽  
Power Prediction ◽  
Short Term
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