4.4 Article

Enhanced Lagrange relaxation for the optimal unit commitment of identical generating units

Journal

IET GENERATION TRANSMISSION & DISTRIBUTION
Volume 14, Issue 18, Pages 3920-3928

Publisher

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2020.0410

Keywords

power system planning; power generation dispatch; power generation economics; renewable energy sources; power generation scheduling; optimisation; power generation planning; enhanced Lagrange relaxation; optimal unit commitment; identical generating units; intelligent generation scheduling; seamless integration; uncertain sources; volatile renewable sources; future low carbon energy; system operators; effective flexibility adequacy plans; power systems; power balance; feasible operation; economical operation; different time horizons; different generational constraints; environmental constraints; technical constraints; unit commitment problem; Lagrange relaxation framework; optimal path; identical heat-rate units; conventional Lagrange relaxation; system size

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Intelligent generation scheduling for seamless integration of uncertain and volatile renewable sources constitutes a crucial solution in delivering future low carbon energy. System operators need to devise effective flexibility adequacy plans for their power systems so as to guarantee power balance and ensure feasible and economical operation over different time horizons and under different generational, environmental and technical constraints. In this work, the authors propose a novel approach for addressing the unit commitment problem of identical generating units, based on Lagrange relaxation framework. The proposed method is characterised by a double decomposition formulation to determine the optimal path for the commitment of identical heat-rate units. This approach is tested and compared to conventional Lagrange relaxation on systems with a number of generating units in the range of 20-100. The proposed approach completely outperforms the conventional alternative in terms of total fuel cost savings, as well as the number of required iterations. In addition, the required iterations are found to increase linearly with system size, which is favourable for large-scale implementations.

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