In the next 20 years, the fossil energy must become a guarantor of the sustainable development of the energy sector for future generations. Significant threats represent hurdles in this transition. This study identified current global trends in the energy sector and the prospects for the development of energy until 2035. The importance of risk assessment in scenario forecasting based on expert judgments was proven. Three contrasting scenarios, #StayHome, #StayAlone, and #StayEffective, for the development of fossil energy, all based on comprehensive analysis of global risks by expert survey and factor analysis, were developed. It was concluded that fossil energy is mandatory with integration of advanced technologies at every stage of the production of traditional energy and of renewable energy as an integral part of the modern energy sector. Based on the results of the study, nine ambitious programs for the development of sustainable energy are presented. They require the creation and the utilization of a single interactive digital platform adapted to this purpose. It is a passport mandatory for the flexible interaction of energy production, its transmission, and its consumption in the perspective of having a future sustainable, reliable, and secured energy sector.
The article presents a study of implementations of information technologies for energy saving. The study provides an analysis of cloud platforms used in the energy sector. Most platforms are used to increase equipment load factors, diagnose it, or integrate the energy infrastructure of individual systems into large systems. The article discusses the role of man in the process of improving energy efficiency. As a result of the research, it is found that the user’s connection to energy saving will act as a significant motivation. The proposed cloud platform focused on the personalization of energy actions will create the prerequisites for the formation of a smart consumer. The article describes the structure and functionality of the system. As a result of the platform implementation, the user will be able not only to reduce his bills for energy, but to see the real effect of his actions in the overall balance.
The power supply system is affected by external disturbances, so it should be stable and operate normally in compliance with power quality standards. The power supply system goes into abnormal modes operation when, after a short-term failure or disturbance, it does not restore normal mode. The electrical complex, which includes a wind power plant, as well as a battery and a diesel generator connected in parallel, is able to provide reliable power supply to consumers which meets the power quality indicators. The article develops an algorithm that is implemented by an automatic control system to select the operating mode depending on climatic factors (wind) and the forecast of energy consumption for the day ahead. Forecast data is selected based on the choice of the methods, which will have the smallest forecast error. It is concluded that if the energy consumption forecast data is added to the automatic control system, then it will be possible to increase the efficiency of the power supply complex. In the developed algorithm the verification of normal and abnormal modes of operation is considered based on the stability theory. The criteria for assessing the normal mode of operation are identified, as well as the indicators of the object’s load schedules for assessing the load of power supply sources and the quality standards for power supply to consumers for ranking the load by priority under critical operating conditions and restoring normal operation are considered.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.