The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

Jiyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • A Two-Step Load Disaggregation Algorithm for Quasi-static Time-series Analysis on Actual Distribution Feeders
    2018 IEEE Power & Energy Society General Meeting (PESGM), 2018
    Co-Authors: Jiyu Wang, Nader Samaan, Brant Werts, David Lubkeman, Ning Lu, David Mulcahy, Catie Mcentee, Andrew Kling
    Abstract:

    This paper focuses on developing a two-step Load disaggregation method for conducting quasi-static time-series analysis using actual distribution feeder data. This can help utilities conduct power flow studies using smart meter measurements to assess the impact of high penetration of distributed energy resources. In the first step, Load profiles of residential and commercial buildings obtained from smart meter data are used to match the Load profile at the feeder head. This step will determine the number of residential and commercial Loads on the feeder. The second step is to allocate the selected Load profiles to each Load Node based on its transformer rating. This allows each Load Node to have its own Load profile and the aggregation of those nodal Load profiles matches closely to the metered feeder Load shape at the substation. This algorithm is validated using smart meter data and the SCADA data of a real feeder. We compared the performance of the proposed method with the traditional Load allocation method (i.e. use the feeder Load shape for all subsequent Load Nodes scaling by the transformer capacities) when conducting quasi-static power flow studies. Results show that the proposed algorithm matches the utility data well and the obtained voltage profiles reveal more voltage dynamics than using the conventional Load allocation method.

  • ISGT - Continuation power flow analysis for PV integration studies at distribution feeders
    2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017
    Co-Authors: Jiyu Wang, Xiangqi Zhu, David Lubkeman, Nader Samaan
    Abstract:

    This paper presents a method for conducting continuation power flow simulation on high-solar penetration distribution feeders. A Load disaggregation method is developed to disaggregate the daily feeder Load profiles collected in substations down to each Load Node, where the electricity consumption of residential houses and commercial buildings are modeled using actual data collected from single family houses and commercial buildings. This allows the modeling of power flow and voltage profile along a distribution feeder on a continuing fashion for a 24-hour period at minute-by-minute resolution. By separating the feeder into Load zones based on the distance between the Load Node and the feeder head, we studied the impact of PV penetration on distribution grid operation in different seasons and under different weather conditions for different PV placements.

  • A Data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method
    IEEE Transactions on Smart Grid, 1
    Co-Authors: Jiyu Wang, David Lubkeman, Ming Liang, Yao Meng, Andrew Kling, Ning Lu
    Abstract:

    The Load profile at a feeder-head is usually known to utility engineers while the nodal Load profiles are not. However, the nodal Load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder Load disaggregation algorithm using smart meter data. The two stages are Load profile selection (LPS) and Load profile allocation (LPA). In the LPS stage, a random Load profile selection process is first executed to meet the Load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one Load profile at a time for matching the reference Load profile at the pivot points. In the LPA stage, the LPS selected Load profiles are allocated to each Load Node on the feeder considering distribution transformer Loading limits, Load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Simulation results show that the proposed method can generate a unique Load shape for each Load Node while match the shape of their aggregated profile with the actual feeder head Load profile.

Hao Zhu - One of the best experts on this subject based on the ideXlab platform.

  • Enhancing the Spatio-temporal Observability of Grid-Edge Resources in Distribution Grids.
    arXiv: Signal Processing, 2021
    Co-Authors: Shanny Lin, Hao Zhu
    Abstract:

    Enhancing the spatio-temporal observability of distributed energy resources (DERs) is crucial for achieving secure and efficient operations in distribution grids. This paper puts forth a joint recovery framework for residential Loads by leveraging the complimentary strengths of heterogeneous types of measurements. The proposed approaches integrate the low-resolution smart meter data collected for every Load Node with the fast-sampled feeder-level measurements provided by limited number of phasor measurement units. To address the lack of data, we exploit two key characteristics for the Loads and DERs, namely the sparse changes due to infrequent activities of appliances and electric vehicles (EVs) and the locational dependence of solar photovoltaic (PV) generation. Accordingly, meaningful regularization terms are introduced to cast a convex Load recovery problem, which will be further simplified to reduce computational complexity. The Load recovery solutions can be utilized to identify the EV charging events at each Load Node and to infer the total behind-the-meter PV output. Numerical tests using real-world data have demonstrated the effectiveness of the proposed approaches in enhancing the visibility of these grid-edge DERs.

Wenyao Sun - One of the best experts on this subject based on the ideXlab platform.

  • Study on influence of inserted photovoltaic power station to voltage distributing of distribution network
    IEEE PES Innovative Smart Grid Technologies, 2012
    Co-Authors: Tieyan Zhang, Wenyao Sun
    Abstract:

    Grid-connected photovoltaic power station has changed the distribution of transmission power, thus influences the voltage distribution of Load Node. In accordance with the relevant provisions, grid-connected photovoltaic power stations should not be active participation of voltage regulation, but it grid-connected will have a supporting role for each Node voltage. This paper proposes a maximum admission capacity calculation method which could meet voltage constraint conditions, by simulation example result in the law of influence on voltage distribution which a single and multiple photovoltaic power station grid-connected locations, grid-connected capacity, and Node type. For distribution network with photovoltaic power plant plan and design to provide a reference.

David Lubkeman - One of the best experts on this subject based on the ideXlab platform.

  • A Two-Step Load Disaggregation Algorithm for Quasi-static Time-series Analysis on Actual Distribution Feeders
    2018 IEEE Power & Energy Society General Meeting (PESGM), 2018
    Co-Authors: Jiyu Wang, Nader Samaan, Brant Werts, David Lubkeman, Ning Lu, David Mulcahy, Catie Mcentee, Andrew Kling
    Abstract:

    This paper focuses on developing a two-step Load disaggregation method for conducting quasi-static time-series analysis using actual distribution feeder data. This can help utilities conduct power flow studies using smart meter measurements to assess the impact of high penetration of distributed energy resources. In the first step, Load profiles of residential and commercial buildings obtained from smart meter data are used to match the Load profile at the feeder head. This step will determine the number of residential and commercial Loads on the feeder. The second step is to allocate the selected Load profiles to each Load Node based on its transformer rating. This allows each Load Node to have its own Load profile and the aggregation of those nodal Load profiles matches closely to the metered feeder Load shape at the substation. This algorithm is validated using smart meter data and the SCADA data of a real feeder. We compared the performance of the proposed method with the traditional Load allocation method (i.e. use the feeder Load shape for all subsequent Load Nodes scaling by the transformer capacities) when conducting quasi-static power flow studies. Results show that the proposed algorithm matches the utility data well and the obtained voltage profiles reveal more voltage dynamics than using the conventional Load allocation method.

  • ISGT - Continuation power flow analysis for PV integration studies at distribution feeders
    2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017
    Co-Authors: Jiyu Wang, Xiangqi Zhu, David Lubkeman, Nader Samaan
    Abstract:

    This paper presents a method for conducting continuation power flow simulation on high-solar penetration distribution feeders. A Load disaggregation method is developed to disaggregate the daily feeder Load profiles collected in substations down to each Load Node, where the electricity consumption of residential houses and commercial buildings are modeled using actual data collected from single family houses and commercial buildings. This allows the modeling of power flow and voltage profile along a distribution feeder on a continuing fashion for a 24-hour period at minute-by-minute resolution. By separating the feeder into Load zones based on the distance between the Load Node and the feeder head, we studied the impact of PV penetration on distribution grid operation in different seasons and under different weather conditions for different PV placements.

  • A Data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method
    IEEE Transactions on Smart Grid, 1
    Co-Authors: Jiyu Wang, David Lubkeman, Ming Liang, Yao Meng, Andrew Kling, Ning Lu
    Abstract:

    The Load profile at a feeder-head is usually known to utility engineers while the nodal Load profiles are not. However, the nodal Load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder Load disaggregation algorithm using smart meter data. The two stages are Load profile selection (LPS) and Load profile allocation (LPA). In the LPS stage, a random Load profile selection process is first executed to meet the Load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one Load profile at a time for matching the reference Load profile at the pivot points. In the LPA stage, the LPS selected Load profiles are allocated to each Load Node on the feeder considering distribution transformer Loading limits, Load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Simulation results show that the proposed method can generate a unique Load shape for each Load Node while match the shape of their aggregated profile with the actual feeder head Load profile.

Ning Lu - One of the best experts on this subject based on the ideXlab platform.

  • A Two-Step Load Disaggregation Algorithm for Quasi-static Time-series Analysis on Actual Distribution Feeders
    2018 IEEE Power & Energy Society General Meeting (PESGM), 2018
    Co-Authors: Jiyu Wang, Nader Samaan, Brant Werts, David Lubkeman, Ning Lu, David Mulcahy, Catie Mcentee, Andrew Kling
    Abstract:

    This paper focuses on developing a two-step Load disaggregation method for conducting quasi-static time-series analysis using actual distribution feeder data. This can help utilities conduct power flow studies using smart meter measurements to assess the impact of high penetration of distributed energy resources. In the first step, Load profiles of residential and commercial buildings obtained from smart meter data are used to match the Load profile at the feeder head. This step will determine the number of residential and commercial Loads on the feeder. The second step is to allocate the selected Load profiles to each Load Node based on its transformer rating. This allows each Load Node to have its own Load profile and the aggregation of those nodal Load profiles matches closely to the metered feeder Load shape at the substation. This algorithm is validated using smart meter data and the SCADA data of a real feeder. We compared the performance of the proposed method with the traditional Load allocation method (i.e. use the feeder Load shape for all subsequent Load Nodes scaling by the transformer capacities) when conducting quasi-static power flow studies. Results show that the proposed algorithm matches the utility data well and the obtained voltage profiles reveal more voltage dynamics than using the conventional Load allocation method.

  • A Data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method
    IEEE Transactions on Smart Grid, 1
    Co-Authors: Jiyu Wang, David Lubkeman, Ming Liang, Yao Meng, Andrew Kling, Ning Lu
    Abstract:

    The Load profile at a feeder-head is usually known to utility engineers while the nodal Load profiles are not. However, the nodal Load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder Load disaggregation algorithm using smart meter data. The two stages are Load profile selection (LPS) and Load profile allocation (LPA). In the LPS stage, a random Load profile selection process is first executed to meet the Load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one Load profile at a time for matching the reference Load profile at the pivot points. In the LPA stage, the LPS selected Load profiles are allocated to each Load Node on the feeder considering distribution transformer Loading limits, Load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Simulation results show that the proposed method can generate a unique Load shape for each Load Node while match the shape of their aggregated profile with the actual feeder head Load profile.