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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1479347
This article is part of the Research Topic Advanced Modeling and Methods for Renewable-dominated Power Systems Operations under Multiple Uncertainties View all 5 articles

An ADMM Approach for Unit Commitment with Considering Dynamic Line Rating

Provisionally accepted
Jiang Dai Jiang Dai 1Nianjie Tian Nianjie Tian 1Qian Zhao Qian Zhao 1Chong Tang Chong Tang 2*Peizheng Xuan Peizheng Xuan 2Lanfen Cheng Lanfen Cheng 2
  • 1 Power Dispatching and Control Center of Guizhou Power Grid Co., Ltd, Guiyang, Guizhou Province, China
  • 2 Electric Power Research Institute of China South Power Grid, Guangzhou, China

The final, formatted version of the article will be published soon.

    In an effort to augment the transmission capabilities within power system scheduling computations, this paper introduces a multi-scenario unit commitment model that incorporates dynamic transmission line capacities, alongside a dedicated Lagrangian relaxation algorithm designed to solve this model. Initially, leveraging the concept of quantile regression, we construct a data-driven model for dynamic transmission line capacity augmentation, grounded in historical environmental parameter datasets. Subsequently, this dynamic transmission line model is integrated into the multi-scenario unit commitment framework, thereby developing a comprehensive unit commitment model that explicitly acknowledges the dynamic nature of transmission line capacities. Ultimately, the system constraints within our model are relaxed, and dynamic transmission line constraints are effectively decoupled through the application of the Alternating Direction Method of Multipliers (ADMM). This allows the dual problem to be decomposed into a series of sub-problems, which are tackled through an iterative algorithm until convergence is achieved. The effectiveness of the proposed model and algorithm is validated using the IEEE-118 and IEEE-300 cases.Indices for units t Indices for time periods s Indices for scenarios b Indices for buses ui,t, di,t Startup and shutdown variable of thermal unit i in time period t xi,t Status variable of thermal unit i in time period t Pi,t,s Power output variable of thermal unit i in time period t under scenario s f Running cost function of thermal unit i π Scenario probability function 1

    Keywords: Unit commitment, Dynamic line rating, Lagrangian relaxation, Alternating Direction Method of Multipliers, Sub-gradient algorithms

    Received: 12 Aug 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Dai, Tian, Zhao, Tang, Xuan and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chong Tang, Electric Power Research Institute of China South Power Grid, Guangzhou, 510631, China

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