Multiobjective Wind-Thermal Generation Scheduling Considering Demand Response Programs Using Augmented Epsilon Constraint Method

This paper focuses on using Demand Response (DR) to cover uncertainty of wind power in Smart Grid (SG) environment. In this paper a multiobjective programming is utilized to minimize total operating cost and air pollutants emission, simultaneously. The proposed multiobjective model schedules energy and reserves provided by both of generating units and responsive loads in power systems with high penetration of wind power. The proposed generation scheduling model is solved using augmented epsilon constraint method. The best solution can be chosen by Entropy and TOPSIS methods.In the proposed model, the discrete retail customer responses to incentive-based DR programs are aggregated by Demand Response Providers (DRPs) and are submitted to ISO. Pricebased DR and random nature of wind power are modeled by price elasticity concept of the demand and normal probability distribution function, respectively. In addition to up and down spinning reserve, DRPs can participate in energy market and submit their offers in the wholesale electricity market. This approach is implemented over a daily time horizon on the IEEE 30-bus test system. The results indicate the benefits of customers’ participation in energy and reserve market that in addition to compensating uncertainty of wind power reduces total operation costs and emission

Keywords:

Demand response , multiobjective programming , reserve , smart grid , wind power