Peak power demand in the national Capital this winter is expected to go up to 6,300 MW and the discoms in the city are gearing up to ensure that adequate electricity is available in this period.

Tata Power Delhi Distribution (Tata Power- DDL) expects a peak demand of approximately 1500 MW this season. A total of 1650 MW of power is available from long term sources to manage winter power demand, stated a release issued by the discom.

Sanjay Banga, CEO of Tata Power-DDL said that the discom has entered into the power banking arrangements with different states including Madhya Pradesh and Andhra Pradesh. Under the arrangement, Tata Power -DDL exports power to these states during the off peak winter months and the same is imported back by it during the summer months of May to September. The arrangement ensures non-stop supply to consumers, he said.

Meanwhile, BSES official said that the peak power demand in BSES Rajdhani Power Ltd. (BRPL) and BSES Yamuna Power Ltd (BYPL) areas had reached 1,888 MW and 1,1136 MW, respectively, during last winter. "This year, it is expected to reach 1950 MW and 1225 MW for BRPL and BYPL respectively. Besides long-term arrangements, BSES discoms are also using advanced techniques like Banking and Backdown to dispose of surplus power and making arrangements to get power during summer months," the statement said.

"In case of any unforeseeable contingency, BSES discoms will buy short term power from the exchange which is available at economical rates," it added.

The company said that for taking care of the summer demand and to optimally manage the winter surplus, BSES discoms export and bank surplus electricity with other states.

"BRPL will bank between 300 MW and 400 MW with states like Himachal Pradesh, Meghalaya, Manipur and Jammu and Kashmir. On its part, BYPL will return around 200 MW to states like Himachal Pradesh, from whom it had taken the quantum during the summer-months," a BSES official said.

To better meet present day demand, BSES said it uses a mix of in-house advanced statistical forecasting models and state-of-the-art weather forecasting solutions, including artificial intelligence (AI) and machine learning.