There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost.

The article discusses the requirements for propellers of bottom mining machines for the development of deep-sea deposits of such unique minerals as ferromanganese nodules, deep-sea polymetallic sulfides, cobalt-manganese crusts. The main factor is the minimal impact on the underwater ecosystem. Based on existing prototypes of mining machines and patents, an analysis of the advantages and disadvantages of existing conceptual mechanisms for the movement of mining machines was carried out, as a result of which it was determined that the most promising machines are those that do not contact the surface of the seabed, but such machines have a number of disadvantages. No less promising today are walking and tracked vehicles. The paper presents simplified formulas for determining the required minimum traction force when moving underwater mining machines, and simplified formulas for determining the speed of movement were obtained, which also clearly indicates the advantage of floating mining machines. According to the obtained graphical dependencies, a set of parameters plays an important role in the productivity of mining machines, including the speed of movement, overall dimensions, weight, and shape of the machine. The article also describes a complex for the extraction of minerals dispersed along the seabed, developed by the staff of the Department of Mechanical Engineering of St. Petersburg State University with various configuration options for the complex depending on the type of mineral, as well as the density of its distribution.

Improving the tools and mathematical methods to diversify the portfolio of oil and gas assets in the face of limited investment, high market volatility, increasing risks and uncertainties at the current level of technology development is a very urgent task. In order to form an effective investment portfolio, the authors proposed asset diversification using cluster analysis, which implies grouping sample objects according to a set of specific features. The method under consideration involves five stages of asset valuation in order to consider those assets in a three-dimensional space, taking into account the specifics of the oil and gas business, including determination of individual asset trajectory, performing spatial approximation, calculating the clustering coefficient, ranking the resulting pairs, and directly solving the portfolio formation optimization problem. This paper provides a reasonable set of metrics for diversification of the investment portfolio based on cluster analysis: main criterion - ∆PV, additional criteria - ∆Production, ∆OPEX/toe, ∆CAPEX/toe, characterized geological, environmental, social and economic aspects. Thus, the proposed methodology provides an opportunity to identify the most attractive investment projects, thereby allowing large oil and gas companies to diversify their business with minimal risk and maximum return on invested capital.