A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

Wang, Jiyu; Zhu, Xiangqi; Liang, Ming; Meng, Yao; Kling, Andrew; Lubkeman, David; Liu, Ning

doi:10.1109/tsg.2020.3008603

Cited by 13 publications

(2 citation statements)

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“…The first data set is synthetic data and uses a modified IEEE 123-bus system to generate one year of 15-min data for 1,100 loads. The feeder load disaggregation algorithm presented in [14] has been used to allocate 1-min resolution residential load profiles from Pecan Street [15] to every load node on a test feeder. Each load node in 123 bus system is assigned a minimum of 4, a maximum of 26, and an average of 11 loads.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Novel Data Segmentation Method for Data-driven Phase Identification

Lee¹,

Zhang²,

Baran³

et al. 2021

Preprint

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This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triplebased Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.

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Section: Simulation Resultsmentioning

confidence: 99%

A Novel Data Segmentation Method for Data-driven Phase Identification

Lee¹,

Zhang²,

Baran³

et al. 2021

Preprint

Self Cite

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“…The parameters and connection points of the DERs are listed in Table I. Each load node in the 123-bus system is assigned a unique 5-minute load profile using the method introduced in [26]. Note that the load profiles are derived from the 1-minute Pecan Street Dataset [27].…”

Section: Data-driven Distributional Robust Reformulationmentioning

confidence: 99%

A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources

Zhang,

Zhu,

2021

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This paper presents a data-driven, distributionally robust chance-constrained optimization method for estimating the real and reactive power controllability of aggregated distributed energy resources (DER). At the DER-level, a twodimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty. At the feeder-level, an aggregated flexibility region is computed via a multi-directional search method. In each search direction, extend the real and reactive power of each controllable DER towards its operational limits until: i) all DERs' maximum operational limits are reached or, ii) one or more of the distribution network operational limits are violated. The method enables three key features for operating aggregated DER resources: controllability estimation, visualization, and risk quantification. Simulation results demonstrated the effectiveness of the algorithm and quantified the impact of different parameter settings on the flexibility region boundary changes. The proposed algorithm is robust and computationally efficient and can meet real-time computing needs.

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