Optimizing Nodal Demand Response in the Day-Ahead Electricity Market within a Smart Grid Infrastructure

Developments of the smart grid infrastructure can facilitate the upsurge of Demand Response (DR) share in power system resources. This paper models the effects of Demand Response Programs (DRPs) on the behavior of the electricity market in the Day-Ahead (DA) session. Decision makers look for the best DR tariff to employ it as a tool to obtain a flexible and sustainable energy market. Employing the most effective DRP is of crucial importance. An optimized DR model and the optimum rates for each DRP are found to meet the decision makers' requirements. Optimizing the nodal tariff and incentive values of different DRPs are proposed in the electricity market. In such environment, market interactions are considered by means of a security constrained unit commitment problem. Both types of Price-Based Demand Response (PBDR) and Incentive-Based Demand Response (IBDR) are modeled. The numerical results presented indicate the effectiveness of the proposed model.