A two-stage wind power integrated demand response framework under constrained transmission network for social welfare maximization

The fusion of renewable energy sources (RESs) in the existing electricity network has been steadily gathering momentum day by day. Though the inclusion of renewables promises a sustainable environment by providing clean energy, it poses multiple challenges for the reliable and safe operation of power systems. Some of these issues include line congestion, different nodal prices, and increased network losses. To resolve these issues, a two-level framework is developed in this work. The first level intends to maximize the system's social welfare (SW), while at the second level, time-of-use (ToU) based tariffs are used to shift the load demand during peak hours, thereby reducing congestion, if present. A k-medoid clustering technique is utilized to differentiate between peak, valley, and flat periods. Further, risk control is incorporated into the proposed framework using the conditional value-at-risk (CVaR) metric, presenting an excellent solution to obtain a trade-off between the expected SW and associated risks. Finally, the proposed model has been tested, and the simulation outcomes are displayed for the IEEE 30-bus test system to validate the efficacy and practicality of the suggested approach.