Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic.
Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India.
These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy.
The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff.
The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage.
Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage.
To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.
The authors are thankful to MAHADISCOM Sangli, for providing the data. MAHADISCOM (Maharashtra State Electricity Distribution Company) is a public sector undertaking company controlled by the Government of Maharashtra, India, which is the second largest electricity distribution utility in the world.
Rathod, R.R. and Garg, R.D. (2017), "Design of electricity tariff plans using gap statistic for K-means clustering based on consumers monthly electricity consumption data", International Journal of Energy Sector Management, Vol. 11 No. 2, pp. 295-310. https://doi.org/10.1108/IJESM-02-2016-0005Download as .RIS
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