The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform

Andrew M. Cross - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew M. Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets-transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

J Yang - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic network Rating for low carbon distribution network operation:a U.K. application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets - transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

  • Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew M. Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets-transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

  • Thermal modelling for dynamic transformer Rating in low carbon distribution network operation
    7th IET International Conference on Power Electronics Machines and Drives (PEMD 2014), 2014
    Co-Authors: J Yang, D Strickland
    Abstract:

    Dynamic asset Rating is one of a number of techniques that could be used to facilitate low carbon electricity network operation. This paper focusses on distribution level transformer dynamic Rating under this context. The models available for use with dynamic asset Rating are discussed and compared using measured load and weather conditions from a trial Network area within Milton Keynes. The paper then uses the most appropriate model to investigate, through simulation, the potential gains in dynamic Rating compared to Static Rating under two transformer cooling methods to understand the potential gain to the Network Operator.

D Strickland - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic network Rating for low carbon distribution network operation:a U.K. application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets - transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

  • Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew M. Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets-transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

  • Thermal modelling for dynamic transformer Rating in low carbon distribution network operation
    7th IET International Conference on Power Electronics Machines and Drives (PEMD 2014), 2014
    Co-Authors: J Yang, D Strickland
    Abstract:

    Dynamic asset Rating is one of a number of techniques that could be used to facilitate low carbon electricity network operation. This paper focusses on distribution level transformer dynamic Rating under this context. The models available for use with dynamic asset Rating are discussed and compared using measured load and weather conditions from a trial Network area within Milton Keynes. The paper then uses the most appropriate model to investigate, through simulation, the potential gains in dynamic Rating compared to Static Rating under two transformer cooling methods to understand the potential gain to the Network Operator.

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

  • Improvement of Transmission Line Ampacity Utilization by Weather-Based Dynamic Line Rating
    IEEE Transactions on Power Delivery, 2018
    Co-Authors: Bishnu P. Bhattarai, Jake P. Gentle, Timothy Mcjunkin, Porter J. Hill, Kurt S. Myers, Alexander W. Abboud, Rodger Renwick, David Hengst
    Abstract:

    Most of the existing overhead transmission lines (TLs) are assigned a Static Rating by considering the conservative environmental conditions (e.g., high ambient temperature and low wind speed). Such a conservative approach often results in underutilization of line ampacity because the worst conditions prevail only for a short period of time during the year. Dynamic line Rating (DLR) utilizes local meteorological conditions and grid loadings to adaptively compute additional line ampacity headroom that may be available due to favorable local environmental conditions. This paper details Idaho National Laboratory-developed weather-based DLR, which utilizes a state-of-the-art general line ampacity state solver for real-time computation of thermal Ratings of TLs. Performance of the proposed DLR solution is demonstrated in existing TL segments at AltaLink, Canada, and the potential benefits of the proposed DLR for enhanced transmission ampacity utilization are quantified. Moreover, we investigated a hypothetical case for emulating the impact of an additional wind plant near the test grid. The results for the given system and data configurations demonstrated that real-time Ratings were above the seasonal Static Ratings for at least 76.6% of the time, with a mean increase of 22% over the Static Rating, thereby demonstRating huge potential for improvement on ampacity utilization.

  • Transmission line ampacity improvements of altalink wind plant overhead tie-lines using weather-based dynamic line Rating
    2017 IEEE Power & Energy Society General Meeting, 2017
    Co-Authors: Bishnu P. Bhattarai, Jake P. Gentle, Timothy Mcjunkin, Kurt S. Myers, Rodger Renwick, Porter Hill, Alex Abbound, David Hengst
    Abstract:

    Overhead transmission lines (TLs) are conventionally given seasonal Ratings based on conservative environmental assumptions. Such an approach often results in the underutilization of the overhead TL capacity as the most conservative environmental conditions occur only for a short period over an year/season. We present computational fluid dynamics (CFD) enhanced weather-based dynamic line Rating (DLR) as an enabling smart grid technology that adaptively computes Ratings of TLs based on local weather conditions to utilize the additional headroom of line ampacity due to concurrent cooling of existing lines. In particular, a general line ampacity state solver is proposed to utilize measured weather data for computing the real-time thermal Rating of the TLs. The performance of the proposed CFD enhanced weather-based DLR is demonstrated from a field study of DLR technology implementation on four TL segments at AltaLink, Canada. The performance is evaluated by comparing the existing Static and the proposed dynamic line Ratings, and the potential benefits of DLR for enhanced transmission assets utilization are quantified. For the given line segments, the proposed DLR results in real-time Ratings above the seasonal Static Ratings for most of the time (up to 95.1%) with a mean increase of 72% over Static Rating.

Lee Jenkins - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic network Rating for low carbon distribution network operation:a U.K. application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets - transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.

  • Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: J Yang, D Strickland, Lee Jenkins, Andrew M. Cross
    Abstract:

    Dynamic asset Rating (DAR) is one of the number of techniques that could be used to facilitate low carbon electricity network operation. Previous work has looked at this technique from an asset perspective. This paper focuses, instead, from a network perspective by proposing a dynamic network Rating (DNR) approach. The models available for use with DAR are discussed and compared using measured load and weather data from a trial network area within Milton Keynes in the central area of the U.K. This paper then uses the most appropriate model to investigate, through a network case study, the potential gains in dynamic Rating compared to Static Rating for the different network assets-transformers, overhead lines, and cables. This will inform the network operator of the potential DNR gains on an 11-kV network with all assets present and highlight the limiting assets within each season.