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Integrating Conductor Health into Dynamic Line Rating and Unit Commitment under Uncertainty

Geon Roh, Jip Kim

Abstract

Dynamic line rating (DLR) enables greater utilization of existing transmission lines by leveraging real-time weather data. However, the elevated temperature operation (ETO) of conductors under DLR is often overlooked, despite its long-term impact on conductor health. This paper addresses this issue by 1) quantifying risk-based depreciation costs associated with ETO and 2) proposing a Conductor Health-Aware Unit Commitment (CHA-UC) that internalizes these costs in operational decisions. CHA-UC incorporates a robust linear approximation of conductor temperature and integration of expected depreciation costs due to hourly ETO into the objective function. Case studies on the Texas 123-bus backbone test system using NOAA weather data demonstrate that the proposed CHA-UC model reduces the total cost by 0.74\% and renewable curtailment by 85\% compared to static line rating (SLR) and outperforms quantile regression forest-based methods, while conventional DLR operation without risk consideration resulted in higher costs due to excessive ETO. Further analysis of the commitment decisions and the line temperature statistics confirms that the CHA-UC achieves safer line flows by shifting generator commitments. Finally, we examine the emergent correlation behaviors arising between wind generation and DLR forecast errors, and show that CHA-UC adaptively manages this effect by relaxing flows for risk-hedging conditions while tightening flows for risk-amplifying ones.

Integrating Conductor Health into Dynamic Line Rating and Unit Commitment under Uncertainty

Abstract

Dynamic line rating (DLR) enables greater utilization of existing transmission lines by leveraging real-time weather data. However, the elevated temperature operation (ETO) of conductors under DLR is often overlooked, despite its long-term impact on conductor health. This paper addresses this issue by 1) quantifying risk-based depreciation costs associated with ETO and 2) proposing a Conductor Health-Aware Unit Commitment (CHA-UC) that internalizes these costs in operational decisions. CHA-UC incorporates a robust linear approximation of conductor temperature and integration of expected depreciation costs due to hourly ETO into the objective function. Case studies on the Texas 123-bus backbone test system using NOAA weather data demonstrate that the proposed CHA-UC model reduces the total cost by 0.74\% and renewable curtailment by 85\% compared to static line rating (SLR) and outperforms quantile regression forest-based methods, while conventional DLR operation without risk consideration resulted in higher costs due to excessive ETO. Further analysis of the commitment decisions and the line temperature statistics confirms that the CHA-UC achieves safer line flows by shifting generator commitments. Finally, we examine the emergent correlation behaviors arising between wind generation and DLR forecast errors, and show that CHA-UC adaptively manages this effect by relaxing flows for risk-hedging conditions while tightening flows for risk-amplifying ones.
Paper Structure (26 sections, 19 equations, 9 figures, 3 tables)

This paper contains 26 sections, 19 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Conductor cost depreciation model
  3. Loss of tensile strength (LoTS) for ACSR lines
  4. Probability of Failure from LoTS
  5. Piecewise depreciation cost for overhead lines
  6. Heat balance equation
  7. Linear temperature approximation
  8. Conductor health-aware unit commitment
  9. Baseline UC model
  10. Conductor health-aware unit commitment
  11. CHA-UC Timeline
  12. Case Study
  13. Preliminaries
  14. Simulation Settings
  15. Data Collection
  16. ...and 11 more sections

Figures (9)

  • Figure 1: An illustrative example of LoTS with a conductor with exposed temperatures: 105° C, 109° C, 112° C each for two hours. The path of total degradation calculation is shadowed on trajectories of different temperatures Musilek2012.
  • Figure 2: Piecewise function of the incremental depreciation cost per one hour of ETO. Functions are drawn for each new and old ACSR Finch. The piecewise functions lay above the exact depreciation cost.
  • Figure 3: Illustration of a linear fit to the current-conductor temperature relationship. The temperature from the IEEE HBE and the linear fit assuming ACSR Finch, a perpendicular wind speed of 2.7 m/s and ambient temperature of 9.4° C is shown. Conductor temperature limit is set as 95 ° C; in practice, it varies depending on the transmission system owner Brown2024.
  • Figure 4: Schematic of the CHA-UC optimization timeline. Processes that require actions outside optimization is colored with gray. Equations and factors are marked with its references.
  • Figure 5: Histograms comparing the distribution of actual and scenario errors. (a) DLR forecast error, (b) WP forecast error
  • ...and 4 more figures