Stochastic Incentive-based Demand Response Program for Virtual Power Plant with Distributed Energy Resources

Harsh, Pratik; Sun, Hongjian; Das, Debapriya; Goyal, Awagan; Jiang, Jing

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.

Citation

Harsh, P., Sun, H., Das, D., Goyal, A., & Jiang, J. (online). Stochastic Incentive-based Demand Response Program for Virtual Power Plant with Distributed Energy Resources. IEEE Transactions on Industry Applications, https://doi.org/10.1109/TIA.2025.3535847

• Journal Article Type: Article
• Acceptance Date: Nov 26, 2024
• Online Publication Date: Jan 29, 2025
• Deposit Date: Dec 13, 2024
• Publicly Available Date: Jan 29, 2025
• Journal: IEEE Transactions on Industry Applications
• Print ISSN: 0093-9994
• Electronic ISSN: 1939-9367
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TIA.2025.3535847
• Keywords: Index Terms-Demand response; Electric vehicles; Multi- objective optimization problem; Stochastic model; Virtual power plant
• Public URL: https://durham-repository.worktribe.com/output/3216638
• Publisher URL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=28