Reducing Weighted Average Cost Of Generation In Pakistan Through Time Of Use (Tou) Pricing Models Of Flexible Electric Loads

“In the last few years, Pakistan has surplus generation capacity. This surplus generation capacity has resulted in an accumulation of a large circular debt and a huge sum of capacity payments is paid to compensate the surplus generation capacity. In addition to this, Pakistan has significant daily and season variations in the demand which further accounts for vast surplus generation capacity. As a consequence of this, the Weighted Average Cost of Generation (WACG) has increased drastically over the last few years. To consume the extra capacity, there is a dire need to find a suitable load that is non-seasonal as well as flexible. Moreover, such load should not be the one that increases the daily load peaks.

To this end, we propose development of a dynamic artificial intelligence (AI) based Time-of-Use (ToU) pricing tool for off-peak utilization of generation capacity by flexible loads. Real-time electricity demand data will be used in the tool to develop short-term demand forecasts. Similarly, data pertaining to the available real-time generation will be used to estimate the generation capacity corresponding to the developed short-term forecasts. The difference between generation capacity and the demand will dictate tariff rates. During peak hours when the gap between the demand and the available generation capacity is smaller, higher tariff rates will incentivize electricity usage during off-peak hours and vice versa. As the tool determines the TOU pricing, it will be ensured that at all the time the tariff rates remain higher than the basket price of electricity in order to prevent any burden on the national exchequer in the form of subsides. The proposed tool will be dynamic in nature implying that it will function under different sets of demand and generation data.”

CGP 01-055
Associate Professor
Computer Science, Lahore University of Management Sciences (LUMS), Lahore
12 months
Rs. 3,939,758/-