Delhi's winter power demand increases to record 5,526 MW

New Delhi, January 6

Delhi’s peak winter power demand rose to a record 5,526 MW on Friday as a numbing cold wave swept the national capital, officials said. An official of the BSES said its power distribution companies are geared to ensure adequate availability for its around 47 lakh consumers (around two crore residents) during the winter months.

“According to data, Delhi’s peak power demand on Friday clocked 5,526 MW at 10.58 am, highest-ever recorded in the national capital during winter months. This is the third day in a row that Delhi’s peak power demand has crossed the 5,000 MW mark,” the BSES official said.

The power demand had peaked at 5,104 MW last year and 5,021 MW in 2020. The peak winter power demand in BRPL and BYPL areas had reached 2,140 MW and 1,122 MW, respectively, during the last winter.

Delhi recorded a cold wave for a second day on the trot on Friday, with the minimum temperature at Ayanagar in southwest Delhi plunging to a numbing 1.8 degrees Celsius, according to the India Meteorological Department (IMD) data.

The Safdarjung Observatory, Delhi’s primary weather station, logged a minimum temperature of four degrees Celsius, which was lower than that of Dalhousie (8.7 degrees Celsius), Dharamshala (5.4 degrees), Shimla (6.2 degrees), Dehradun (4.4 degrees), Mussoorie (6.4 degrees) and Nainital (6.5 degrees) The backbone of BSES’ power-supply arrangements during the winter months includes long-term agreements from power-plants; including Hydro and Delhi-based gas-fuelled generating stations.

Additionally, the BSES is also receiving 840 MW of solar power from Solar Energy Corporation of India, 439 MW of wind power, around 25 MW from waste-to-energy plants. It is also being helped by more than 127 MW of solar power installed on roof-tops in south, west, east and central Delhi.

In case of any contingency, BSES discoms will buy short-term power from the exchange which is available at economical rates. Advanced load-forecasting statistical and modelling techniques will help them accurately forecast the power demand.