Pratik Harsh pratik.harsh@durham.ac.uk
Postdoctoral Research Associate
Pratik Harsh pratik.harsh@durham.ac.uk
Postdoctoral Research Associate
Professor Hongjian Sun hongjian.sun@durham.ac.uk
Professor
Debapriya Das
Awagan Goyal
Jing Jiang
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.
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 |
Accepted Journal Article
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