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Flexible operation of virtual power plant enabled integrated electricity-heating system under multiple uncertainties via distributionally robust model predictive control

Lookup NU author(s): Dr Sheng WangORCiD

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Abstract

© 2025 Elsevier LtdVirtual power plant (VPP) can incorporate various electric infrastructures, e.g., data centers (DCs) and electric vehicles (EVs), creating multiple uncertainties and challenges for the operation of integrated electricity–heating system (IEHS). This paper focuses on the flexible operation problem of VPP-enabled IEHS under both static and dynamic uncertainties. First, the temporal shifting flexibility of workloads from DCs is modeled. Second, a novel metric-based distributionally robust model predictive control (DRMPC) framework is introduced to address both static uncertainties from renewable energy and dynamic uncertainties from EV charging behaviors. Third, the dynamic uncertainties are reformulated as ambiguity tubes, and distributionally robust bounds for both dynamic and static uncertainties are determined using DRMPC. Through ambiguity tubes and distributionally robust optimization, the stochastic MPC system is converted into a nominal one. Case studies validate the effectiveness of the proposed approach.

Publication metadata

Author(s): Wang X, Li Q, Zhai J, Jiang Y, Wang S, Wang J

Publication type: Article

Publication status: Published

Journal: Applied Energy

Year: 2026

Volume: 404

Print publication date: 01/02/2026

Online publication date: 06/12/2025

Acceptance date: 25/11/2025

ISSN (print): 0306-2619

ISSN (electronic): 1872-9118

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.apenergy.2025.127177

DOI: 10.1016/j.apenergy.2025.127177

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