Модель для прогнозування потужності ОЕС України під час нічного провалу графіків електричних навантажень

In this work, we give the description of developed model for the operational prediction of power of the UES of Ukraine during the night depression of its electric-load schedules and the developed algorithm of its functioning. As the function of night depression approximation, a second-degree polynom...

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Bibliographic Details
Date:2019
Main Author: Derii V.O.
Format: Article
Language:Ukrainian
Published: General Energy Institute of the National Academy of Sciences of Ukraine 2019
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Online Access:https://systemre.org/index.php/journal/article/view/703
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Journal Title:System Research in Energy

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System Research in Energy
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Summary:In this work, we give the description of developed model for the operational prediction of power of the UES of Ukraine during the night depression of its electric-load schedules and the developed algorithm of its functioning. As the function of night depression approximation, a second-degree polynomial was chosen. As a result of the investigations carried out and test calculations by the developed model, the factors influencing arguments of the function were found, namely, the temperature of outside air and the duration of light day. These factors cause errors in prediction. The main causes of these errors are determined.Analysis of the influence factors allowed us to determine their analytical dependences. It was established that the maximal prediction errors arise at the ends of interval of the night depression of electric-load schedules. To reduce errors in power prediction, it is proposed to use derivatives of approximation functions at the ends of interval of night depression and a second-degree polynomial at other times. The performed testing modeling of power of the UES of Ukraine during its night depression of electric-load schedules for the period from 30.11.2015 to April 30, 2017 showed that the maximal relative error of prediction does not exceed 9.4%, and its relative average value is 3.9%.