title: ÐÐµÑÐ¾Ð´Ð¸ÐºÐ° ÑÑÐµÑÐ° ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÑ Ð¸ Ð²ÐµÑÑÑÐ½ÑÑ ÑÐ»ÐµÐºÑÑÐ¾ÑÑÐ°Ð½ÑÐ¸Ð¹ Ð¿ÑÐ¸ ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ð¼ Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐµÐ¶Ð¸Ð¼Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: The methodology of considering for solar and wind power plants in the short-term planning of electric power regimes
Creators: name: ÐÐ°Ð½ÑÐºÐ¾Ð², ÐÐ¸ÑÐ¸Ð»Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð²Ð¾Ð·Ð¾Ð±Ð½Ð¾Ð²Ð»ÑÐµÐ¼ÑÐµ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ð¸
ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÐµ ÑÐ»ÐµÐºÑÑÐ¾ÑÑÐ°Ð½ÑÐ¸Ð¸
Ð²ÐµÑÑÑÐ½ÑÐµ ÑÐ»ÐµÐºÑÑÐ¾ÑÑÐ°Ð½ÑÐ¸Ð¸
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Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¿Ð»Ð°Ð½Ð¾Ð²ÑÐ¹ Ð´Ð¸ÑÐ¿ÐµÑÑÐµÑÑÐºÐ¸Ð¹ Ð³ÑÐ°ÑÐ¸Ðº
renewable energy sources
solar power plants
wind power plants
forecasting
gradient boosting
neural network
scheduled dispatch schedule

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description: The subject of the graduate qualification work is "The methodology of considering for solar and wind power plants in the short-term planning of electric power regimes." This work is devoted to the development of a model for predicting the magnitude of the active generation capacity of wind and solar power plants, as well as the subsequent application of the obtained forecast when considering the relevant generation facilities in the short-term planning of the electric power regime. The research set the following goals: 1. Analysis of existing approaches to forecasting the active generation capacity of renewable energy facilities. 2. Processing and analysis of initial data on generation and weather conditions. 3. Development of a prediction model based on gradient boosting. 4. Development of a prediction model based on a neural network. 5. Development of a methodology for considering the received forecast values when forming a planned dispatch schedule. The archive of data on the generation capacity of the Akhtubinskaya solar power plant and the Adygeyskaya wind power plant, as well as an archive of weather data from the nearest weather stations for the period from 2019 to 2020, inclusive, were used as initial data. Data analysis and model development were carried out in the Python 3 programming language. As a result, the initial data were processed, forecasting models were trained on their basis, among which the gradient boosting model turned out to be the most accurate. It was proposed to use the received forecast of active power when forming a planned dispatching schedule. In order to account for the inaccuracy of the model, a 90% confidence interval of forecast values was calculated, which can be used to estimate the required reserve value.