Bi-Level Coordination of Demand Response and Multiple Virtual Power Plants in a Distribution Network for Flexibility Assessment

Technological advancements, environmental concerns, and improved electricity management drive the transition to smart grids. With the increasing integration of distributed energy resources (DERs), efficient management is crucial for optimizing both renewable and non-renewable energy sources. This paper presents a bi-level stochastic optimization framework to enhance virtual power plant (VPP) operations and improve grid flexibility. At the lower level, VPPs maximize profits by optimizing resource scheduling, managing demand response (DR) programs, and determining power exchanges with the distribution system operator (DSO). The upper level minimizes DSO costs while ensuring network security and reliability. A key feature is the provision of upward and downward flexibility by VPPs at both the individual resource level and in day-ahead scheduling, enabling the DSO to enhance grid stability and renewable integration. The model, formulated as a stochastic mixed-integer linear programming (MILP) problem, effectively addresses uncertainties in load demand, renewable generation, and energy prices. Simulation results demonstrate significant improvements in flexibility and economic efficiency, achieving an 89.91% reduction in excess renewable generation through targeted penalty mechanisms. These findings highlight the practical benefits of the proposed framework for VPP operators and DSOs, offering a robust strategy for managing DERs while ensuring stable and secure grid operation.