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

TL;DR

This work addresses the coordination of distributed energy resources through a stochastic incentive-based DR framework for a direct-controlled VPP that aggregates solar, battery swapping, EV charging, and controllable loads. It introduces a priority-based DR mechanism for EVs and loads, formulates a five-objective optimization, and normalizes to a single objective via utopia-tracking, followed by a stochastic extension using Hong's (2m+1) PEM to handle uncertainties. The approach is validated on a 33-node distribution system with co-simulation in MATLAB, RSCAD, and RTDS, showing improved voltage profiles, reduced grid purchases, and cost savings for EV charging and controllable loads. The results demonstrate the practical viability of the method and its potential to enhance VPP flexibility and grid resilience, with future work focusing on resiliency under adverse conditions.

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.

Figures (8)

• Figure 1: Direct-controlled virtual power plant.
• Figure 2: Hourly CS energy demand and incentivized electricity price for a day.
• Figure 3: Variation in SOC of EVs during their stay at the CS.
• Figure 4: Hourly load curtailments and increments for the consumers with controllable loads.
• Figure 5: Hourly power exchange between solar, EVs, batteries, and DS as a source and load within the VPP framework.