The article considers the information and technical aspects of an innovative autonomous integrated power plant management, including alternative energy sources and a diesel generator set, as well as controlled inverters that provide energy supply to consumers of various priority categories, which can be used in the Arctic regions of the Russian Federation. The main aspects of creating innovative systems are considered and it is determined that the creation of integrated energy systems requires a significant deepening of cooperation between national producers in order to ensure the scalability of integrated energy systems by ensuring the unity of information means of data exchange between individual modules and the control system. It is shown that a specific requirement for control systems of complex power plants is the requirement of high autonomy, including the ability to provide consumers with electricity under variable environmental conditions without direct intervention of operational personnel. The article substantiates the division of the information and algorithmic support of the control system of a complex power plant into two modules – analytical and control. For the analytical module, an algorithm is proposed that ensures the development of control solutions in a complex energy system, ensuring the stability of energy supply to the most important consumers. At the same time, the algorithm provides an increase in the reliability of the energy storage device based on Li-Ion batteries used in the system not only by eliminating excessive charge and deep discharge, but also by reducing the number of charge/discharge cycles. The solution of system autonomy problem is provided by a multivariate algorithm for predicting weather conditions using statistical data and methods for analyzing fuzzy time series. The intelligent control algorithm was implemented in C++, the weather forecasting algorithms were implemented in Python using the ANFIS library.
alternative energy, energy effi control algorithms in the energy sector, fuzzy time series, forecasting