Delhi’s peak winter power demand touched an all time high of 5,343 MW on New Year’s Day. Heating load (heaters and geysers) is the main reason behind the increase in city’s power load in winter as it constitutes more than 40 per cent of the total power demand, a senior BSES official said.
Delhi’s peak power demand continues its upwards climb, be it summers or winter. After clocking an all-time high of 7,409 MW during summers of 2019, it continues to break records in winters also, touching 5,343 MW which is highest ever in the winter season so far.
Last year, highest peak power demand in winter was recorded 5,298 MW on December 30, 2019. In fact, on January 1, 2020 the peak power demand in BRPL, BYPL and TPDDL also reached the highest of 2,256 MW, 1,148 MW and 1,656 MW respectively.
The official also said that in November, peak power demand this year was higher than the peak power demand of November 2018 on 20 occasions and it was higher in the month of December on 30 occasions.
The back-bone of BSES’ power-supply arrangements during the winter-months includes long-term agreements from power-plants like Dadri (stage 1 and 2), Hydro Stations, Singrauli, Rihand, Sasan, DVC Mejia and Delhi based gas fueled generating stations. Additionally, BSES is also receiving 50 MW of wind power and 20 MW of solar power from SECI.
Apart from these, BSES discoms are using advanced techniques and avenues like “banking”, “reserve shutdown”, “power exchange” and ensuring sufficient “spinning reserves” to dispose of surplus power as well as ensuring reliable power supply, as also making arrangements to get power during summer months, he added.
In case of any unforeseeable contingency, BSES discoms will buy short-term power from the exchange which is available at economical rates and can range between (around) Rs 1 to over Rs 4 per unit - during winter months, depending on the time-slot, he said.
“To meet power demand challenges and to get a grip on so many varied and dynamic variables, BSES uses a mix of advanced statistical forecasting models, combined with state-of-the-art weather forecasting solutions, including Artificial Intelligence (AI) and machine learning,” he said.
