Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Optimal Aggregation Approach for Virtual Power Plant Considering Network Reconfiguration
Author:
Affiliation:

1.Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua Shenzhen International Graduate School (Tsinghua SIGS), Tsinghua University, Shenzhen, China;2.State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing, China;3.Electric Power Control Center of Guangdong Power Grid Co., Ltd., China Southern Power Grid, Guangzhou, China

Fund Project:

This work was supported in part by the National Science Foundation of China (No. U2066601) and the Technical Projects of China Southern Power Grid(No. GDKJXM20180018).

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    Abstract:

    As an aggregator of distributed energy resources (DERs) such as distributed generator, energy storage, and load, the virtual power plant (VPP) enables these small DERs participating in system operation. One of the critical issues is how to aggregate DERs to form VPPs appropriately. To improve the controllability and reduce the operation cost of VPP, the complementary DERs with close electrical distances should be aggregated in the same VPP. In this paper, it is formulated as an optimal network partition model for minimizing the voltage deviation inside VPPs and the fluctuation of injection power at the point of common coupling (PCC). A new convex formulation of network reconfiguration strategy is incorporated in this approach which can guarantee the components of the same VPP connected and further improve the performance of VPPs. The proposed approach is cast as an instance of mixed-integer linear programming (MILP) and can be effectively solved. Moreover, a scenario reduction method is developed to reduce the computation burden based on the k-shape algorithm. Numerical tests on the 13-bus and 70-bus distribution networks justify the effectiveness of the proposed approach.

    表 2 Table 2
    表 3 Table 3
    图1 芦苇茎秆SEM图Fig.1 SEM images of reed stem
    图2 碱浸渍处理后的芦苇茎秆SEM图Fig.2 SEM images of reed stem after alkali impregnation treatment
    图3 不同温度碱浸渍处理后芦苇茎秆SEM图Fig.3 SEM images of reed stem after alkali impregnation treatment at different temperatures
    图4 不同温度碱浸渍处理后的芦苇茎秆显微CT图Fig.4 Micro-CT images of reed stem after alkali impregnation treatment under different temperature
    图1 Illustrative 13-bus distribution network.Fig.1
    图2 Variation of distortion with different numbers of aggregations.Fig.2
    图3 Optimal aggregation result without network reconfiguration in 13-bus distribution network.Fig.3
    图4 Optimal aggregation result with network reconfiguration in 13-bus distribution network.Fig.4
    图5 Voltage deviation with and without network reconfiguration in scenarios 1-27 in 13-bus distribution network.Fig.5
    图6 Injection power fluctuation with and without network reconfiguration in scenarios 1-27 in 13-bus distribution network.Fig.6
    图7 Optimal aggregation result without network reconfiguration in 70-bus distribution network.Fig.7
    图8 Optimal aggregation result with network reconfiguration in 70-bus distribution network.Fig.8
    图9 Voltage deviation with and without network reconfiguration in scenarios 1-27 in 70-bus distribution network.Fig.9
    图10 Injection power fluctuation with and without network reconfiguration in scenarios 1-27 in 70-bus distribution network.Fig.10
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History
  • Received:August 20,2020
  • Online: May 19,2021