Abstract

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.

Highlights

  • One of the main strategic goals of the development of energy systems in the world today is the course towards energy conservation and the introduction of information technologies [1,2,3] that increase the interaction between the consumer and the energy system [4]

  • The issues of applying demand response today have a number of unsolved problems associated with stimulating the involvement of consumers in the creation of mechanisms for the flexibility of power systems [6]

  • The purpose of this study is to develop an algorithm for optimizing the university’s power consumption schedule, taking into account the forecasting of power system loads in order to participate in the demand response program

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Summary

Introduction

One of the main strategic goals of the development of energy systems in the world today is the course towards energy conservation and the introduction of information technologies [1,2,3] that increase the interaction between the consumer and the energy system [4]. The introduction of information technologies such as big data and the Internet of Things (IoT) will optimize the demand response process and increase the overall level of energy efficiency [5], improve the quality and availability of electricity for users, and popularize energy efficient thinking among energy consumers. The need to create and develop demand response technology is due to the changes that are taking place in the power systems of the whole world; there are local reasons. Global challenges include: Accelerated integration of cyber-physical systems into factory processes [7,8]

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