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Electrical Engineering and Systems Science > Systems and Control

arXiv:2101.10800 (eess)
[Submitted on 26 Jan 2021 (v1), last revised 3 Feb 2021 (this version, v2)]

Title:Robust Scheduling of Virtual Power Plant under Exogenous and Endogenous Uncertainties

Authors:Yunfan Zhang, Feng Liu, Zhaojian Wang, Yifan Su, Weisheng Wang, Shuanglei Feng
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Abstract:Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands. This paper proposes a novel model for determining the optimal offering strategy in the day-ahead energy-reserve market and the optimal self-scheduling plan. It considers exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices and available wind power generation, as well as the endogenous uncertainties (or called decision-dependent uncertainties, DDUs) pertaining to real-time reserve deployment requests. A tractable solution method based on strong duality theory, McCormick relaxation, and the Benders' decomposition to solve the proposed stochastic adaptive robust optimization with DDUs formulation is developed. Simulation results demonstrate the applicability of the proposed approach.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2101.10800 [eess.SY]
  (or arXiv:2101.10800v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2101.10800

Submission history

From: Yunfan Zhang [view email]
[v1] Tue, 26 Jan 2021 14:23:52 UTC (4,305 KB)
[v2] Wed, 3 Feb 2021 12:35:38 UTC (4,306 KB)
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