Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

Yuriy Leonidovich Zhukovskiy, Margarita Sergeevna Kovalchuk, Daria Evgenievna Batueva and Nikita Dmitrievich Senchilo
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Yuriy Leonidovich Zhukovskiy: Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 Saint Petersburg, Russia
Margarita Sergeevna Kovalchuk: Electric Energy and Electromechanically Department, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia
Daria Evgenievna Batueva: Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 Saint Petersburg, Russia
Nikita Dmitrievich Senchilo: Electric Energy and Electromechanically Department, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia

Sustainability, 2021, vol. 13, issue 24, 1-26

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.

Keywords: demand response; energy efficiency; energy saving; Internet of Things; machine learning; big data; digital technologies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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