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СпутникиРНSpaceXРоскосмосNASA Researchers from South Ural State University have proposed a method that uses the data on the monthly average consumption of electricity to quickly and easily calculate the real peak load on grids. This development is especially relevant for regions where people have no gas supply or central hot-water supply systems – there, electricity becomes the only source of energy, and the power supply networks are pushed to their limits.
During the grids designing and retrofitting, power-generation industry specialists are guided by the standard indicators: how much electricity is on average consumed by one apartment or private house. And reality often diverges from these standards. This is an especially pressing issue for the regions off the gas grid: people install electric heaters, boilers and electric cookers in large numbers. The load on the grids increases, transformers overheat, and accidents occur.
“We have analysed the data from electric power meters in 226 apartments in the city of Chelyabinsk and in two cities in Tajikistan – Dushanbe and Khorog,” shares Associate Professor of the SUSU Department of Health and Safety Saidzhon Tavarov. “In Chelyabinsk, people have gas and hot-water supply, and people in Tajikistan don’t. The figures speak for themselves: in the peak hours, the actual load on the grids in Dushanbe and Khorog exceeds the standards up to 2.5 times. Meanwhile, the daily average load remains within the standard indicators. That is, the grids give under not permanently, but namely during the morning and evening hours, when people switch on most of their heaters and boilers.”
The researchers have come up with a unique consolidating “coefficient A”, which takes into account the air temperature, terrain elevation above the sea level, structure of buildings, and even the welfare of their residents. Using this coefficient in a formula together with the readings from common electric power meters (recorded by the facility managing companies once a month), specialists will be able to calculate the real maximum load on grids without the need to conduct expensive hourly measurements.
According to the scientists, the method’s error equals no more than 5%. This was confirmed by comparing with the experiment data and with the calculations using neural networks.
“For power-generation industry specialists this means that now they don’t have to conduct measurements on each transformer every two or three years. It will be sufficient to set the new model in their computer and use it for years to come,” explains Saidzhon Tavarov. “Our approach allows to build in adequate capacity of the grids already at the stage of their designing or retrofitting. This reduces the risk of accidents, extends the life of equipment, and as a result, improves the reliability of power supply for consumers. This is especially important for remote areas and for countries, where people depend on electricity and where its outages are unacceptable.”
In the nearest future, the development researchers are planning on adapting this method for regions with harsh environments, where the accuracy of such predictions remains low.
Read more in the SUSU channel on MAX
Author: Ekaterina Bolnykh
Researchers from South Ural State University have proposed a method that uses the data on the monthly average consumption of electricity to quickly and easily calculate the real peak load on grids. This development is especially relevant for regions where people have no gas supply or central hot-water supply systems – there, electricity becomes the only source of energy, and the power supply networks are pushed to their limits.
During the grids designing and retrofitting, power-generation industry specialists are guided by the standard indicators: how much electricity is on average consumed by one apartment or private house. And reality often diverges from these standards. This is an especially pressing issue for the regions off the gas grid: people install electric heaters, boilers and electric cookers in large numbers. The load on the grids increases, transformers overheat, and accidents occur.
“We have analysed the data from electric power meters in 226 apartments in the city of Chelyabinsk and in two cities in Tajikistan – Dushanbe and Khorog,” shares Associate Professor of the SUSU Department of Health and Safety Saidzhon Tavarov. “In Chelyabinsk, people have gas and hot-water supply, and people in Tajikistan don’t. The figures speak for themselves: in the peak hours, the actual load on the grids in Dushanbe and Khorog exceeds the standards up to 2.5 times. Meanwhile, the daily average load remains within the standard indicators. That is, the grids give under not permanently, but namely during the morning and evening hours, when people switch on most of their heaters and boilers.”
The researchers have come up with a unique consolidating “coefficient A”, which takes into account the air temperature, terrain elevation above the sea level, structure of buildings, and even the welfare of their residents. Using this coefficient in a formula together with the readings from common electric power meters (recorded by the facility managing companies once a month), specialists will be able to calculate the real maximum load on grids without the need to conduct expensive hourly measurements.
According to the scientists, the method’s error equals no more than 5%. This was confirmed by comparing with the experiment data and with the calculations using neural networks.
“For power-generation industry specialists this means that now they don’t have to conduct measurements on each transformer every two or three years. It will be sufficient to set the new model in their computer and use it for years to come,” explains Saidzhon Tavarov. “Our approach allows to build in adequate capacity of the grids already at the stage of their designing or retrofitting. This reduces the risk of accidents, extends the life of equipment, and as a result, improves the reliability of power supply for consumers. This is especially important for remote areas and for countries, where people depend on electricity and where its outages are unacceptable.”
In the nearest future, the development researchers are planning on adapting this method for regions with harsh environments, where the accuracy of such predictions remains low.