Forecasting for dynamic line rating

Andrea Michiorri (Corresponding Author), Huu-Minh Nguyen, Stefano Alessandrini, John Bjørnar Bremnes, Silke Dierer, Enrico Ferrero, Bjørn-Egil Nygaard, Pierre Pinson, Nikolaos Thomaidis, Sanna Uski

    Research output: Contribution to journalArticleScientificpeer-review

    56 Citations (Scopus)

    Abstract

    This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits.
    Original languageEnglish
    Pages (from-to)1713-1730
    JournalRenewable and Sustainable Energy Reviews
    Volume52
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Overhead lines
    Operations research
    Solar wind
    Solar radiation
    Temperature
    Electric lines
    Statistical methods
    Reinforcement
    Sensors
    Air
    Hot Temperature

    Keywords

    • rating
    • overhead lines
    • forecast
    • smart grid

    Cite this

    Michiorri, A., Nguyen, H-M., Alessandrini, S., Bremnes, J. B., Dierer, S., Ferrero, E., ... Uski, S. (2015). Forecasting for dynamic line rating. Renewable and Sustainable Energy Reviews, 52, 1713-1730. https://doi.org/10.1016/j.rser.2015.07.134
    Michiorri, Andrea ; Nguyen, Huu-Minh ; Alessandrini, Stefano ; Bremnes, John Bjørnar ; Dierer, Silke ; Ferrero, Enrico ; Nygaard, Bjørn-Egil ; Pinson, Pierre ; Thomaidis, Nikolaos ; Uski, Sanna. / Forecasting for dynamic line rating. In: Renewable and Sustainable Energy Reviews. 2015 ; Vol. 52. pp. 1713-1730.
    @article{4b62fa8a134743f6ace1a63a622e67f7,
    title = "Forecasting for dynamic line rating",
    abstract = "This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits.",
    keywords = "rating, overhead lines, forecast, smart grid",
    author = "Andrea Michiorri and Huu-Minh Nguyen and Stefano Alessandrini and Bremnes, {John Bj{\o}rnar} and Silke Dierer and Enrico Ferrero and Bj{\o}rn-Egil Nygaard and Pierre Pinson and Nikolaos Thomaidis and Sanna Uski",
    year = "2015",
    doi = "10.1016/j.rser.2015.07.134",
    language = "English",
    volume = "52",
    pages = "1713--1730",
    journal = "Renewable and Sustainable Energy Reviews",
    issn = "1364-0321",
    publisher = "Elsevier",

    }

    Michiorri, A, Nguyen, H-M, Alessandrini, S, Bremnes, JB, Dierer, S, Ferrero, E, Nygaard, B-E, Pinson, P, Thomaidis, N & Uski, S 2015, 'Forecasting for dynamic line rating', Renewable and Sustainable Energy Reviews, vol. 52, pp. 1713-1730. https://doi.org/10.1016/j.rser.2015.07.134

    Forecasting for dynamic line rating. / Michiorri, Andrea (Corresponding Author); Nguyen, Huu-Minh; Alessandrini, Stefano; Bremnes, John Bjørnar; Dierer, Silke; Ferrero, Enrico; Nygaard, Bjørn-Egil; Pinson, Pierre; Thomaidis, Nikolaos; Uski, Sanna.

    In: Renewable and Sustainable Energy Reviews, Vol. 52, 2015, p. 1713-1730.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Forecasting for dynamic line rating

    AU - Michiorri, Andrea

    AU - Nguyen, Huu-Minh

    AU - Alessandrini, Stefano

    AU - Bremnes, John Bjørnar

    AU - Dierer, Silke

    AU - Ferrero, Enrico

    AU - Nygaard, Bjørn-Egil

    AU - Pinson, Pierre

    AU - Thomaidis, Nikolaos

    AU - Uski, Sanna

    PY - 2015

    Y1 - 2015

    N2 - This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits.

    AB - This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits.

    KW - rating

    KW - overhead lines

    KW - forecast

    KW - smart grid

    U2 - 10.1016/j.rser.2015.07.134

    DO - 10.1016/j.rser.2015.07.134

    M3 - Article

    VL - 52

    SP - 1713

    EP - 1730

    JO - Renewable and Sustainable Energy Reviews

    JF - Renewable and Sustainable Energy Reviews

    SN - 1364-0321

    ER -

    Michiorri A, Nguyen H-M, Alessandrini S, Bremnes JB, Dierer S, Ferrero E et al. Forecasting for dynamic line rating. Renewable and Sustainable Energy Reviews. 2015;52:1713-1730. https://doi.org/10.1016/j.rser.2015.07.134