Сalculation of the correction factor to the normative values of specific electric loads of multiple residential buildings Moscow and Moscow region

THE PURPOSE. Reducing the cost of external electrical networks in the housing construction of multi-apartment residential buildings (MKD) in Moscow and the Moscow Region by substantiating the value of the correction factor to the standard values of specific electrical loads and developing appropriate amendments to SP 256.1325800.2016 “Electrical installations of residential and public buildings. Rules for design and installation. METHODS. The half-hour graphs of electrical loads installed directly at the objects under study were experimentally obtained from intelligent electricity metering devices. To process the measurement results, statistical methods for processing a large amount of data were applied. RESULTS. The article substantiates the relevance of the topic, analyzes the electrical loads of residential buildings in Moscow and the Moscow region, which confirmed the need to develop a correction factor, the value of which characterizes the difference between real and calculated values. Accepted for statistical processing are the results of measurements of electricity consumption in apartments per day with the maximum total electricity consumption of MKD. Based on the calculations performed, amendments were prepared to section 7 of SP 256.1325800.2016 “Electrical installations of residential and public buildings. Design and installation rules”, including clause 7.1.10. is set out in a new edition, and table 7.5a is formed. CONCLUSION. Based on the analysis of the calculated specific electrical loads of apartment buildings in Moscow and the Moscow region, the value of the correction factor for the city of Moscow and the Moscow region in relation to the MKD of standard projects, which amounted to 0.81, was justified, taking into account the margin. The use of a correction factor to determine the design load of a residential building will reduce costs in the construction of external electrical networks of residential buildings with a simultaneous increase in the efficiency of power transformers in Moscow and the Moscow Region.

1. Opoleva GN. Ehlektrosnabzhenie promyshlennykh predpriyatii i gorodov. Moscow: Publishing House FORUM INFRA-M; 2017;416.

2. Soluyanov YI, Fedotov AI, Galitskiy YY, et al. Updating the normative values of the specific electrical load of apartment buildings in the Republic of Tatarstan. Electricity. 2021; 6:62-71.

3. Soluyanov YI, Fedotov AI, Akhmetshin AR et al. Analysis of the actual values of the assessment of apartment buildings in the Moscow region. Industrial Energy. 2022; 4:20-28.

4. James G, Witten D, Hatie T, et al. An introduction to statistical learning with Applications in R. 2nd ed. Cham. Springer. 2021;612.

5. Zhilkina YV. Processes of reforming the electric power industry in Russia. Energetik. 2020; 1:29-32.

6. Fedotov AI, Abdrakhmanov RS, Akhmetshin AR. Ensuring the standard voltage level in distribution networks of 0.4-10 kV using booster transformers. Proceedings of the higher educational institutions. Energy sector problems. 2011; 09-10:40-45.

7. Gofman AV, Vedernikov AS, Vedernikova ES. Improving the accuracy of short -term and operational forecasting of the power consumption of the energy system using an artificial neural network. Electric Stations. 2012; 7:36-41.

8. Ilyushin PV. Features of the account of load parameters in the analysis of transient processes in networks with distributed generation facilities. Elektroenergiya. Transmission and distribution. 2018; 51(6):54-61.

9. Soluyanov YI, Akhmetshin AR, Soluyanov VI. Energy-resource-saving effect in the power supply systems of residential complexes from the actualization of standards for electrical loads. Proceedings of the higher educational institutions. Energy sector problems. 2021; 23(1):156-166.

10. Latifi M, Sabzehgar R, Rasouli M. Reactive power compensation using plugged-in electric vehicles for an AC power grid. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. 2018;4986-4991.

11. Gracheva EI, Naumov OV, Fedotov EA. Influence of the load capacity of power transformers on their operational characteristics. Proceedings of the higher educational institutions. Energy sector problems. 2017; 7-8:71-77.

12. Gracheva EI, Naumov OV, Sadykov RR. Accounting for idle waste of transformers during operation when calculating electricity losses in distribution networks. Proceedings of the higher educational institutions. Energy sector problems. 2016; 1-2:53-63.

13. Soluyanov YI, Akhmetshin AR, Soluyanov VI. Actualization of specific electrical loads of public premises built into residential buildings. Proceedings of the higher educational institutions. Energy sector problems. 2021; 3:47-57.

14. Nadtoka II, Pavlov AV. Increasing the accuracy of calculating the electrical loads of apartment buildings with electric stoves. Izvestiya vuzov. North Caucasian region. Technical science. 2015; 2:45-48.

15. Nadtoka II, Pavlov AV. Calculations of electrical loads in the residential part of apartment buildings with electric stoves based on the average loads of apartments. Izvestiya vuzov. Electromechanics. 2014; 3:36-39.

16. Carroll P, Murphy T, Hanley M, et al. Household classification using smart meter data. Journal of official statistics. 2018; 34(1):1-25.

17. Proedrou A. Comprehensive review of residential electricity load profile models. IEEE Access. 2021; 9:12114-12133.

18. Cembranel SS, Lezama F, Soares J, et al. A short review on data mining techniques for electricity customers characterization. 2019 IEEE PES GTD Grand International Conference and Exposition Asia. 2019;194-199.

19. Chung H-M, Maharjan S, Zhang Y, Eliassen F. Distributed deep reinforcement learning for intelligent load scheduling in residential smart grids. IEEE Transactions on Industrial Informatics. 2021; 17(4):2752-2763.

20. Mayorov AV. Development of the system of operational and technological management of the power grid complex within the framework of the concept of digital transformation 2030. Electricity. Transmission and distribution. 2019; 13(2):2-7.

21. Goreeva NM, Demidova LN. Statistics. Moscow: Publ.: Prometheus. 2019;496.

22. Loskutov AB, Sosnina EN, Loskutov AA, Zyrin DV. Intelligent distribution networks 10–20 kV with a hexagonal configuration. Industrial Energy. 2013; 12:3–7.

23. Loskutov AB, Loskutov AA, Zyrin DV. Development and research of a flexible intelligent medium voltage electrical network based on a hexagonal structure. Proceedings of NSTU im. R.E. Alekseeva. 2016; 114(3):85–94.