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Risk constrained short-term scheduling with dynamic line ratings for increased penetration of wind power

Author

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  • Banerjee, Binayak
  • Jayaweera, Dilan
  • Islam, Syed

Abstract

Limited transmission capacity may lead to network congestion which results in wind curtailment during periods of high availability of wind. Conventional congestion management techniques usually involve generation management which may not always benefit large wind farms. This paper investigates the problem in detail and presents an improved methodology to quantify the latent scheduling capacity of a power system taking into account stochastic variation in line-thermal rating, intermittency of wind, and mitigating the risk of network congestion associated with high penetration of wind. The mathematical model converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation risk. The uniqueness of the approach is that it can limit the generation to be curtailed or re-dispatch by dynamically enhancing the network latent capacity as per the need. The approach is aimed at strategic planning of power systems in the context of power systems with short to medium length lines with a priori known unit commitment decisions and uses stochastic optimization with a two stage recourse action. Results suggest that a considerable level of wind penetration is possible with dynamic line ratings, without adversely affecting the risk of network congestion.

Suggested Citation

  • Banerjee, Binayak & Jayaweera, Dilan & Islam, Syed, 2015. "Risk constrained short-term scheduling with dynamic line ratings for increased penetration of wind power," Renewable Energy, Elsevier, vol. 83(C), pages 1139-1146.
  • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:1139-1146
    DOI: 10.1016/j.renene.2015.05.053
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    Cited by:

    1. Phillips, Tyler & DeLeon, Rey & Senocak, Inanc, 2017. "Dynamic rating of overhead transmission lines over complex terrain using a large-eddy simulation paradigm," Renewable Energy, Elsevier, vol. 108(C), pages 380-389.
    2. Romain Dupin & Laura Cavalcante & Ricardo J. Bessa & Georges Kariniotakis & Andrea Michiorri, 2020. "Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation," Energies, MDPI, vol. 13(12), pages 1-21, June.
    3. F. Gülşen Erdinç & Ozan Erdinç & Recep Yumurtacı & João P. S. Catalão, 2020. "A Comprehensive Overview of Dynamic Line Rating Combined with Other Flexibility Options from an Operational Point of View," Energies, MDPI, vol. 13(24), pages 1-30, December.
    4. Pinto, Mauro S.S. & Miranda, Vladimiro & Saavedra, Osvaldo R., 2016. "Risk and unit commitment decisions in scenarios of wind power uncertainty," Renewable Energy, Elsevier, vol. 97(C), pages 550-558.
    5. Karimi, Soheila & Musilek, Petr & Knight, Andrew M., 2018. "Dynamic thermal rating of transmission lines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 600-612.

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