Reducing the difference between on peak and off peak demand has long been recognized by utilities as an effective way of cutting the cost of producing electricity. Moreover, having prior knowledge about dynamic electricity rates gives the consumer an opportunity to optimize the consumption. Hence, an efficient demand response program promises the advantage on both sides. Owing to high flexibility, real-time pricing based demand response are considered to possess the highest potential among all the other programs. But, the current practice of same suffers from a lack of consumer level behavioral understanding and hence, making it difficult to predict and map the response. As a result, demand response programs are inducing the uncertainty in terms of real time demand. This uncertainty poses difficulty for power generation entities as well as load serving entities in predicting the consumer’s behavior in response to advance price signals. Current research focuses on the development of consumer psychology model for predicting and imitating the consumer’s response scenario to advance price signals and mapping the same in the form of elasticity matrix. Elasticity matrix is further integrated in the model to modify the price signals. These modified price signals based on elasticity matrix reduces the uncertainty in the system.