Svetlana Aleksandrovna Rozhkova, Ogarev Mordovia State University, graduated from the Faculty of Mathematics and Information Technologies at Ogarev Mordovia State University; Postgraduate Student, Lecturer of the Department of ComputerAided Design at Ogarev Mordovia State University; an author of articles in the field of mathematical modeling of home energy management systems. [email: rozhkova_sa@mail.ru]S. Rozhkova, Vladimir Fedorovich Belov, Ogarev Mordovia State University, Doctor of Engineering, Professor; graduated from the Faculty of Electronics at Ogarev Mordovia State University; Head of the Department of ComputerAided Design at Ogarev Mordovia State University; an author of articles, monographs, and inventions in the field of the design of autonomous electric power systems, in which power quality parameters are controllable. [email: belovvf@mail.ru]V. Belov


Optimal Scheduling of Local Battery Storage
Nowadays energetics is characterized by consistent trend of the development of distributed power generation systems (microgrids) and alternative energy sources implementation as its components. In these systems using local electric batteries became actual and possible for automatic energy consumption management. These devices significantly increase the reliability and electronic characteristics of electricity supply through optimal choice of energy sources using rapid connection of the power sources to the power consumers. The authors analyze the algorithm for optimal control of the power system consisting of two energy sources  one of them is battery storage and another one is external centralized grid where timevarying electricity pricing is released. The problem of finding the algorithm for optimal control generation as a task of the creating working timetable of electric storage, which has to minimize expenses for power supply is considered. Within a given time interval, a microprocessor controls a power storage battery releasing process. Particle Swarm Optimization (PSO) is selected as a numerical method for solving this problem. Based on experiment numerical results that were performed, conclusions about effectiveness of developed battery schedule optimization algorithm are given; also recommendations were given for further improvements of microprocessor programming. Control, microgrid, electric energy consumption, battery energy storage, optimization, nonlinear programming, particle swarm optimization.

