A Comprehensive and Preferential Analysis of Demand Response Programs Considering Demand Uncertainty

Demand response programs (DRPs) provide an opportunity for customers to play a significant role in the operation of the electric grid by reducing or shifting their electricity consumption during peak periods in response to dynamic electricity rates or other forms of financial incentives. This article proposes a comprehensive and preferential analysis of different demand response (DR) programs such as price-based DR (PBDR), incentive-based DR (IBDR), and a combination of both programs when applied to the Iranian power grid with the aim of maximizing customer profit. Moreover, a large number of scenarios are generated for accurate modeling of demand uncertainty, and to avoid computational complexity, these scenarios are reduced using the probability distance-based backward reduction method. In this paper, IBDR programs are evaluated by considering two different structures based on the setting of incentive and penalty values offered to customers. These DR programs are prioritized using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, whereas the Analytic Hierarchy Process (AHP) method is used for determining final priority. Independent System Operator (ISO), utility, and customer's perspective are considered as decision variables for determining final priority in the AHP method, and these decision variables are given due weight by the entropy method. It is observed that based on ISO, utility, and the customer's perspective, the PBDR program is given the highest priority, followed by combinations of both PBDR and IBDR programs.