In this thesis, the impact of customer baseline (CBL) accuracy on the Peak Time Rebate (PTR) program is investigated. In a hypothetical case, PTR is offered to the residential customers and its economic performance is evaluated with respect to the CBL accuracy performance. Since this program relies on CBL for payment settlement, its performance hinges on the accuracy of such calculations. The accuracy of CBL calculations are studied for residential customers. Moreover, for the purpose of this investigation, popular CBL methods of High5of10 (NYISO), Low4of5, Mid4of6, exponential moving average (ISONE) and regression methods and their adjusted forms are selected for CBL calculation. Then, this calculated CBL is utilized to examine the performance of a case of PTR program. The case consists of 262 residential customers. According to the results, in this case study, utility pays at least 50 percent of its revenue as a rebate just because of the inaccuracy of CBL methods. This loss increases if the aforementioned CBL methods get adjusted for their morning consumption. At the end, it is discussed that PTR can cause a significant loss to the customers and cause unfair redistribution of the utility’s revenue. It is shown that aforementioned inefficiencies are because of the failure of CBL calculation methods to predict the customer’s load profile on the event day.