This paper presents a method for refining the forecast schedule of renewable energy sources (RES) generation by its intraday adjustment and investigates the measures for reserving RES with unstable generation in electric power systems (EPSs). Owing to the dependence of electricity generation by solar and wind power plants (PV and WPPs, respectively) on natural conditions, problems arise with their contribution to the process of balancing the power system. Therefore, the EPS is obliged to keep a power reserve to compensate for deviations in RES from the planned generation amount. A system-wide reserve (mainly the shunting capacity of thermal and hydroelectric power plants) is used first, followed by other means of power reserve: electrochemical, hydrogen, or biogas plants. To analyze the technical and economic efficiency of certain backup means, mathematical models based on the theory of similarity and the criterion method were developed. This method is preferred because it provides the ability to compare different methods of backing up RES generation with each other, assess their proportionality, and determine the sensitivity of costs to the capacity of backup methods with minimal available initial information. Criterion models have been formed that allow us to build dependencies of the costs of backup means for unstable RES generation on the capacity of the backup means. It is shown that, according to the results of the analysis of various methods and means of RES backup, hydrogen technologies are relatively the most effective. The results of the analysis in relative units can be clarified if the current and near-term price indicators are known.

The quality of training is a central problem in terms of demand of specialists by the national economy. Therefore the need to improve management efficiency by automation of decision support in the management of the quality of education increases. At the same time the problem of developing the intellectual system of decision support in the management of the quality of education is not sufficiently investigated, which leads to the relevance of the selected areas of research. The present article offers the system of indexes and indicators of educational statistics recommended by the Ministry of Education, describing various aspects of educational activities on different levels of education and operation, was used in the research.

In this paper, we propose a methodology for using static Bayesian networks (BN) to simulate the processes of educational institutions financing and their impact on Gross Domestic Product (GDP). The methods for constructing the BN structure, their parametric learning, validation, sensitivity analysis and scenario analysis «What-if» are considered.