CONSTRUCTION OF A MATHEMATICAL MODEL OF ELECTRICITY CONSUMPTION MODE

• Дата: 2017
• Автор: Kuznietsov, M. P.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: Institute of Renewable Energy National Academy of Sciences of Ukraine 2017
• Теми: electricity consumption; mathematical model; random process; correlation function; autoregression
• Онлайн доступ: https://ve.org.ua/index.php/journal/article/view/19
• Назва журналу: Vidnovluvana energetika

The large-scale implementation of renewable energy requires alignment with the real conditions of electricity consumption. The balance of generation and energy consumption is a dynamic process. Since the work of renewable energy depends on weather factors and is modeled using stochastic processes, the same approach is proposed for the consumption process. This will allow us to estimate the variability of the energy balance and determine the possibilities of balance reliability ensuring. To determine the parameters of the mathematical model, the actual modes of electricity consumption are used, described in terms of time series. An important step in this case is to ensure the steady state of the investigated process. An acceptable method is the decomposition of the process, as a random function, within the framework of the general linear model. In this case, the general process is divided into a deterministic averaged component, a discrete random component, and a continuous stationary process. Analysis of actual data on consumers of different levels, as particular individual settlements, regional and general energy systems, obtained for different time intervals, indicates a qualitative similarity of random components of the consumption process. Identification of the mathematical model indicates the possibility of applying of two- or one-parameter autoregression. A com-parative evaluation of the accuracy of different models is performed. Determining the parameters of the model as solutions of the stochastic differential equation Ornstein-Uhlenbeek provides a high similarity of the mathematical model with real processes.