scholarly journals ANALYSIS OF FACTORS FOR FORECASTING ELECTRIC POWER GENERATION BY SOLAR POWER PLANTS

2020 ◽  
Author(s):  
D. Matushkin ◽  
А. Bosak ◽  
L. Kulakovskyi
Keyword(s):  
Mathematical Model ◽  
Power Generation ◽  
Power Plant ◽  
Electric Power ◽  
Electricity Generation ◽  
Solar Power ◽  
Target Function ◽  
Solar Power Plant ◽  
Electric Power Generation ◽  
The Mathematical Model

The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Operation and Test of a New 37 MW Gas Turbine Package Power Plant

1980 ◽  
Author(s):  
J. Jermanok ◽  
R. E. Keith ◽  
E. F. Backhaus
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Electric Power ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Simple Cycle ◽  
Initial Operation ◽  
Design Features ◽  
Electric Power Generation ◽  
Gas Turbine Power Plant

A new 37-MW, single-shaft gas turbine power plant has been designed for electric power generation, for use in either simple-cycle or combined-cycle applications. This paper describes the design features, instrumentation, installation, test, and initial operation.

Performance Analysis of Integrated Solar Tower With a Conventional Heat and Power Co-Generation Plant

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Esmail M. A. Mokheimer ◽  
Yousef N. Dabwan
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Electric Power ◽  
Gas Turbine ◽  
Economic Feasibility ◽  
Field Size ◽  
Electric Power Generation ◽  
Electricity Cost ◽  
Generation Capacity ◽  
Solar Field

This paper presents the results of a thermo-economic analysis of integrating solar tower (ST) with heat and power cogeneration plants that is progressively being installed to produce heat and electricity to operate absorption refrigeration systems or steam for industrial processes. The annual performance of an integrated solar-tower gas-turbine-cogeneration power plant (ISTGCPP) with different sizes of gas turbine and solar collector's area have been examined and presented. Thermoflex + PEACE software's were used to thermodynamically and economically assess different integration configurations of the ISTGCPP. The optimal integrated solar field size has been identified and the pertinent reduction in CO2 emissions due to integrating the ST system is estimated. For the considered cogeneration plant (that is required to produce 81.44 kg/s of steam at 394 °C and 45.88 bars), the study revealed that (ISTGCPP) with gas turbine of electric power generation capacity less than 50 MWe capacities have more economic feasibility for integrating solar energy. The levelized electricity cost (LEC) for the (ISTGCPP) varied between $0.067 and $0.069/kWh for gas turbine of electric power generation capacity less than 50 MWe. Moreover, the study demonstrated that (ISTGCPP) has more economic feasibility than a stand-alone ST power plant; the LEC for ISTGCPP is reduced by 50–60% relative to the stand-alone ST power plant. Moreover, a conceptual procedure to identify the optimal configuration of the ISTGCPP has been developed and presented in this paper.

scholarly journals Performance Evaluation of Solar Power Plants: A Review and a Case Study

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2253
Author(s):  
Mahmoud Makkiabadi ◽  
Siamak Hoseinzadeh ◽  
Ali Taghavirashidizadeh ◽  
Mohsen Soleimaninezhad ◽  
Mohammadmahdi Kamyabi ◽  
...  
Keyword(s):  
Power Plant ◽  
Solar Energy ◽  
Electricity Generation ◽  
Power Plants ◽  
Solar Power ◽  
Ocean Waves ◽  
Solar Power Plant ◽  
Large Area ◽  
Energy Technologies ◽  
Solar Power Plants

The world’s electricity generation has increased with renewable energy technologies such as solar (solar power plant), wind energy (wind turbines), heat energy, and even ocean waves. Iran is in the best condition to receive solar radiation due to its proximity to the equator (25.2969° N). In 2020, Iran was able to supply only 900 MW (about 480 solar power plants and 420 MW home solar power plants) of its electricity demand from solar energy, which is very low compared to the global average. Yazd, Fars, and Kerman provinces are in the top ranks of Iran, with the production of approximately 68, 58, and 47 MW using solar energy, respectively. Iran also has a large area of vacant land for the construction of solar power plants. In this article, the amount of electricity generation using solar energy in Iran is studied. In addition, the construction of a 10 MW power plant in the city of Sirjan is economically and technically analyzed. The results show that with US$16.14 million, a solar power plant can be built in the Sirjan region, and the initial capital will be returned in about four years. The results obtained using Homer software show that the highest maximum power generation is in July.

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