Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data

Jurasz, Jakub; Wdowikowski, Marcin; Figurski, Mariusz

doi:10.3390/en13164255

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Moreover, studies were conducted analyzing the impact of meteorological data on electricity production forecasting [37]. Studies were also conducted on more technologically advanced pv systems, with the ability to store generated electricity [38] or the systems based on photovoltaic roof tiles [39].…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of the Impact of the Assumed Moment of Meeting Total Energy Demand on the Profitability of Photovoltaic Installations for Households in Poland

Iwaszczuk

Trela

2021

Energies

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In this article, the authors analyzed two extreme investment variants considering the installed capacity of Poland’s residential pv installation: (a) pv installation meets electricity demand only in the first year of operation, and in each subsequent year it is necessary to purchase electricity at market prices; (b) installation meets electricity demand even in the last year of operation, and in each previous year, the excess of generated electricity needs to be transferred back to the grid, with the limited (by law) right of drawing it from the grid for free in the future. In the article, a sensitivity analysis was performed, and profitability changes were established based on the NPV value, depending on case (a) or (b). The performed analyses showed that the pv installation profitability should not be analyzed, assuming only one moment when it meets 100% of the household’s electricity demand. It was shown that the choice of such a moment, depending on the value of particular technical and financial parameters, can lead to a change in the NPV value, even over 10%. Although the studies were done for Poland, such an approach can be implemented in other countries.

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Section: State Of the Artmentioning

confidence: 99%

Analysis of the Impact of the Assumed Moment of Meeting Total Energy Demand on the Profitability of Photovoltaic Installations for Households in Poland

Iwaszczuk

Trela

2021

Energies

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Machine Learning Models for Electricity Generation Forecasting from a PV Farm

Krechowicz,

Pawelec

2024

IFIP Advances in Information and Communication Technology

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Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Krechowicz

Lichołai

et al. 2022

Energies

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Problems with inaccurate prediction of electricity generation from photovoltaic (PV) farms cause severe operational, technical, and financial risks, which seriously affect both their owners and grid operators. Proper prediction results are required for optimal planning the spinning reserve as well as managing inertia and frequency response in the case of contingency events. In this work, the impact of a number of meteorological parameters on PV electricity generation in Poland was analyzed using the Pearson coefficient. Furthermore, seven machine learning models using Lasso Regression, K–Nearest Neighbours Regression, Support Vector Regression, AdaBoosted Regression Tree, Gradient Boosted Regression Tree, Random Forest Regression, and Artificial Neural Network were developed to predict electricity generation from a 0.7 MW solar PV power plant in Poland. The models were evaluated using determination coefficient (R2), the mean absolute error (MAE), and root mean square error (RMSE). It was found out that horizontal global irradiation and water saturation deficit have a strong proportional relationship with the electricity generation from PV systems. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed PV farm. Random Forest Regression was the most reliable and accurate model, as it received the highest R2 (0.94) and the lowest MAE (15.12 kWh) and RMSE (34.59 kWh).

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Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data

Cited by 3 publications

References 17 publications

Analysis of the Impact of the Assumed Moment of Meeting Total Energy Demand on the Profitability of Photovoltaic Installations for Households in Poland

Analysis of the Impact of the Assumed Moment of Meeting Total Energy Demand on the Profitability of Photovoltaic Installations for Households in Poland

Machine Learning Models for Electricity Generation Forecasting from a PV Farm

Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

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