A Stochastic Incentive-based Demand Response Program for Virtual Power Plant with Solar, Battery, Electric Vehicles, and Controllable Loads

TL;DR

This paper introduces a stochastic incentive-based demand response model for virtual power plants integrating solar, batteries, electric vehicles, and controllable loads, optimizing scheduling under uncertainty to enhance grid coordination.

Contribution

It presents a novel multi-objective stochastic optimization framework for VPP scheduling that incorporates individual preferences and uncertainties in energy demand.

Findings

Effective scheduling of VPP components under uncertainty.

Improved coordination of DERs for grid stability.

Validated approach using MATLAB and real-time simulation.

Abstract

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.