  The growing integration of distributed energy resources (DERs) into the power
grid necessitates an effective coordination strategy to maximize their
benefits. Acting as an aggregator of DERs, a virtual power plant (VPP)
facilitates this coordination, thereby amplifying their impact on the
transmission level of the power grid. Further, a demand response program
enhances the scheduling approach by managing the energy demands in parallel
with the uncertain energy outputs of the DERs. This work presents a stochastic
incentive-based demand response model for the scheduling operation of VPP
comprising solar-powered generating stations, battery swapping stations,
electric vehicle charging stations, and consumers with controllable loads. The
work also proposes a priority mechanism to consider the individual preferences
of electric vehicle users and consumers with controllable loads. The scheduling
approach for the VPP is framed as a multi-objective optimization problem,
normalized using the utopia-tracking method. Subsequently, the normalized
optimization problem is transformed into a stochastic formulation to address
uncertainties in energy demand from charging stations and controllable loads.
The proposed VPP scheduling approach is addressed on a 33-node distribution
system simulated using MATLAB software, which is further validated using a
real-time digital simulator.
