Delhi's Winter Peak Power Demand Touches All-Time High Of 5,816 MW

Delhi's peak power demand hit a record 5,816 megawatts (MW) on Monday morning which is the highest ever for the winter months, surpassing the previous record of 5,798 MW set on January 19.

Meanwhile, this is the sixth time in January when Delhi's power demand exceeded last year's peak winter demand of 5,526 MW, recorded on January 6, 2023. Barring January 2, the city's peak demand has consistently surpassed 5,000 MW every day this month so far.

The power demand reached 5,816 MG at 10.52 am on Monday, according to the city.

Apart from long-term agreements from power plants, over 2000 MW of green power is playing a significant role in ensuring reliable power supply to BSES consumers during the winter months. This includes 840 MW of solar power from SECI, 540 MW of Hydropower, 500 MW of wind power, 40 MW from Waste-to Energy and 160 MW of roof-top solar power installed on roof-tops in South, West, East and Central Delhi. In fact, up to 60 per cent of the maximum 'winter' power demand of up to 3600 plus MW in the BSES area will be met with green power.

It further mentioned that BSES discoms are using avenues like "banking", and power exchange" and ensuring sufficient "spinning reserves" to dispose of surplus power as well as ensure reliable power supply, as well as making arrangements to get power during summer months.

Moreover, BSES discoms will also bank surplus power with other states, which need additional power during the winter months. This banked power will be available during the summer months. BRPL proposes to bank up to 290 MW with states like Himachal Pradesh and Goa. On its part, BYPL is expected to bank up to 150 MW with states like Uttarakhand, Tamil Nadu, Himachal Pradesh, Rajasthan and Kerala from whom it had taken the quantum during the summer months.

Notably, BSES uses a mix of advanced statistical forecasting models using complex algorithms, combined with state-of-the-art weather forecasting solutions, including Artificial Intelligence and Machine Learning to meet power challenges.

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