How do prosumers benefit the power market? Bilevel optimization for demand response programs

Many distribution system operators and regulators recognize that demand response (DR) programs and demand aggregators can help manage the uncertainties of renewable energy generation. Considering the grid stability, this paper models the DR market with and without renewable energy sources to determine the optimal purchase prices and financial incentives. We apply the Karush-Kuhn-Tucker conditions to convert our two proposed bilevel optimization models into mixed-integer nonlinear programming and single level mixed-integer linear programming problems, using the Big-M method and the strong duality theorem. The proposed models examine consumer comfort, total generation cost savings for the distribution system operator (DSO), and the profits of regular/ renewable energy aggregators to determine dynamic pricing over time. We discuss a case study of real-time wind and solar data from one summer and one winter day of low-voltage and high-voltage consumers with advanced metering infrastructure enrolled in the DR program. The results and a sensitivity analysis validate the cost advantages of a DR program with aggregators against the uncertainty of power generation.