Abstract

The formation of the concept of Smart Grid is associated with a number of issues, including theoretical and methodological. One of the main problems in forming such theory is to build its basis, the starting point for the development of which is the definition of Smart Grid as a systems of views concept on the future of power engineering, the principles of operation and technological basis of which undergoes significant changes compared to modern energy. The paper is aimed at reviewing and developing directions and approaches to the definition of Smart Grid in combination with machine learning mechanisms, highlighting their diverse and common nature to develop a holistic innovative energy development. In this paper, a study of the energy sector in Ukraine was conducted. Its efficiency and innovative development are considered. Problems with the implementation of Smart Grid technology, which arise when using alternative sources and monitoring and administration systems, were highlighted. The ways of mathematical formulation of the Smart Grid optimization problem are determined using the Data Science approach based on the machine learning system and neural networks. Big Data processing methods, Data Mining, statistical methods, artificial intelligence methods, and Machine Learning are analyzed. The design and development of databases and application software will be done using the Data Science method. Smart-technologies will take over the processes of control, accounting and diagnostics of assets, which will provide promising opportunities for self-recovery of the power system, as well as efficient operation of fixed assets. With the introduction of Smart Grid technologies for the Ukrainian power industry, significant fundamental changes will take place. This is the transition from centralized methods of generation and transmission of electricity to distributed networks with the ability to control energy production facilities and network topology at any point, including at the consumer level. Replacement of centralized demand forecasting according to the methodology of active consumer influence becomes an element and subject of the management system. A high-performance information and computing infrastructure will be built as the core of the energy system. This approach creates the preconditions for the widespread introduction of new devices that increase the maneuverability and controllability of the equipment. The creation of next-generation operational applications (SCADA/EMS/NMS) allows the use of innovative algorithms and methods of power system management, including its new active power elements.

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