Open AccessEnhanced Lagrange relaxation for the optimal unit commitment of identical generating units
Author(s) -
Nikolaidis Pavlos,
Poullikkas Andreas
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0410
Subject(s) - power system simulation , mathematical optimization , flexibility (engineering) , lagrange multiplier , computer science , scheduling (production processes) , lagrangian relaxation , relaxation (psychology) , electric power system , renewable energy , range (aeronautics) , implementation , power (physics) , mathematics , engineering , electrical engineering , psychology , physics , social psychology , statistics , quantum mechanics , aerospace engineering , programming language
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.