Stochastic load modeling in the residential sector

The power demand on the electric grid varies depending on the time of day and the needs of consumers. Demand response is a change in the consumer load curve accompanied by a change of price, used primarily by suppliers to limit consumption peaks. Reducing the short-term mismatch between production and consumption helps to integrate renewable energy sources, various low-carbon technologies, battery storage of electricity and electric vehicles into the electric grid. One of the tools used to maintain a balance between electricity production and consumption is smart meters, which operating in asmart grid. Such devices are widespread in the United States and the European Union, in the residential sector too. At the moment, the introduction of smart grids in the residential sector is just beginning in the Russian Federation. The article considers a stochastic model of electricity consumption by household appliances, based on the convolution theory. The measurement of power consumption by the most common household appliances has been performed. Several examples of consumer profiling based on the obtained data are given.The barriers that arise during the implementation of smart grids in the Russian Federation are identified, as well as the reasons why the interest of electricity suppliers in smart grids is growing.

1. Binyet E., Chiu M.-Ch., Hsu H.-W., Lee M.-Y., Wen Ch.-Yu. Potential of Demand Response for Power Reallocation, a Literature Review. Energies. 2022;15(3). https://doi.org/10.3390/en15030863

2. Dobrego K.V. Model for Electric Load of Community Housing Projects to Investigate «Generator – Accumulator – Consumer» System while Using Monte-Carlo Method. Nauka i tekhnika = Science and Technique. 2017;16(2):160–170. (In Russ.). https://doi.org/10.21122/2227-1031-2017-16-2-160-170

3. Kuznetsov B.F., Klibanova Yu.Yu., Sukyasov S.V., Lugovnina V.V. Building a stochastic model of residential load on example of a water heater. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta = Proceedings of Irkutsk State Technical University. 2019;23(5):958–966. (In Russ.). https://doi.org/10.21285/1814-3520-2019-5-958-966

4. Tarnizhevskii M.V., Mikhailov V.I. Modelirovanie sutochnykh grafikov elektricheskikh nagruzok kommunal'no-bytovykh potrebitelei metodom ortogonal'nykh razlozhenii. Elektrichestvo. 1985;(5):66–68. (In Russ.).

5. Borovsky A.V., Yumenchuk A.A. Stochastic Models of Electricity Consumption. System Analysis & Mathematical Modeling. 2024;6(1):31–46. (In Russ.). https://doi.org/10.17150/2713-1734.2024.6(1).31-46

6. Hussain M.M., Akram R., Memon Z.A., Nazir M.H., Javed W., Siddique M. Demand Side Management Techniques for Home Energy Management Systems for Smart Cities. Sustainability. 2021;13(21). https://doi.org/10.3390/su132111740

7. Mansouri M.R., Simab M., Bahmani Firouzi B. Impact of Demand Response on Reliability Enhancement in Distribution Networks. Sustainability. 2021;13(23). https://doi.org/10.3390/su132313201

8. Cortés-Cediel M.E., Cantador I., Rodríguez Bolívar M.P. Analyzing Citizen Participation and Engagement in European Smart Cities. Social Science Computer Review. 2019;39(4). https://doi.org/10.1177/0894439319877478

9. Rodríguez Bolívar M.P., Alcaide Muñoz C., Alcaide Muñoz L. Identifying Strategic Planning Patterns of Smart Initiatives. An Empirical Research in Spanish Smart Cities. Electronic Government. 2020;12219:374–386. https://doi.org/10.1007/978-3-030-57599-1_28

10. Shkitina N., Akimov D. Analiz vliyaniya stokhasticheskoi nagruzki elektromobilei na raspredelitel'nuyu set'. Elektroenergiya. Peredacha i raspredelenie = Electric Power. Transmission and Distribution. 2021;(1):40–45. (In Russ.).