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Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability

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  • Lai, Ching-Ming
  • Teh, Jiashen

Abstract

The nonflexible operations of transmission networks with high load demand and wind power increase the likelihood of power congestions and deteriorate grid reliability. This condition causes higher load curtailments and inhibits the penetrations of wind power, subsequently leading to higher dispatch cost because more expensive generators compensate for the loss of wind integrations. These unfavourable factors contribute to a higher total system operating cost, which should be reduced. This paper applies a network topology optimisation technique to optimise line and busbar switching for relieving network congestions and improving network flexibility. A dynamic thermal rating system is used to enhance overhead line ratings. A battery storage system is utilised to time shift wind power usage and avoid wind spillage. These methods are effective but they have been studied only in isolation. This paper presents an assessment framework that combines all the three methods in a single model to evaluate their synergistic effects on wind integration and network reliability. The proposed framework is generic and can be applied on any networks with changes only to the numerical results. The battery energy and power ratings required to maintain the prevailing security of supply standards in a large scale wind-integrated network are determined probabilistically. Case studies performed on a modified IEEE 24-bus reliability test system show that the proposed combination of methods reduces system dispatch, load curtailment and wind curtailment costs the most when compared to any combinations with fewer methods or using each method in isolation.

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  • Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
  • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011624
    DOI: 10.1016/j.apenergy.2021.117837
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    References listed on IDEAS

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    1. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
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    12. Yasir Yaqoob & Arjuna Marzuki & Ching-Ming Lai & Jiashen Teh, 2022. "Fuzzy Dynamic Thermal Rating System-Based Thermal Aging Model for Transmission Lines," Energies, MDPI, vol. 15(12), pages 1-23, June.
    13. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
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