Estimation of elasticity of electricity demand in Iran: New empirical evidence using aggregate data

Abstract

as electricity markets are liberalized, consumers become exposed to more volatile electricity prices and may decide to modify the profile of their demand to reduce their electricity costs. Therefore, accurate prediction of consumers' reaction to the electricity price changes can be effective in order to increase the profits of producers and consumers. This paper classifies the variety of consumers' reaction to the electricity price changes. Then, the price elasticity is estimated for a real network in Iran based on collected data from statistical analysis. To achieve this, the consumption period (1 day) is divided into three groups (Peak, Off-peak, Low) and mathematical definition model of elasticity is applied to estimate price elasticity. Results revealed that the highest elasticity in peak period belongs to residential customers due to their highest demand-level in the peak period.

Analysis and Accurate Prediction of User’s Response Behavior in Incentive-Based Demand Response

“…As described in Section 2, a user's response behavior can be approximated by a quadratic function. The response elasticity of the same user at different times of the day may be different [40], therefore, the user may have different values of α and β at different time periods. Generate historical response data of users according to [5], [26], and [41].…”