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Feasible operation region estimation of virtual power plant considering heterogeneity and uncertainty of distributed energy resources

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  • Chen, Lin
  • Tang, Zhiyuan
  • He, Shuaijia
  • Liu, Junyong

Abstract

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation.

Suggested Citation

  • Chen, Lin & Tang, Zhiyuan & He, Shuaijia & Liu, Junyong, 2024. "Feasible operation region estimation of virtual power plant considering heterogeneity and uncertainty of distributed energy resources," Applied Energy, Elsevier, vol. 362(C).
    • Handle: RePEc:eee:appene:v:362:y:2024:i:c:s0306261924003830
    DOI: 10.1016/j.apenergy.2024.123000
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    References listed on IDEAS

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    Cited by:

    1. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).

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