Open AccessUnit commitment using improved adjustable robust optimisation for large‐scale new energy power stations
Author(s) -
Xiu Liancheng,
Kang Zhiliang,
Huang Peng
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8926
Subject(s) - robustness (evolution) , power system simulation , particle swarm optimization , computer science , wind power , scheduling (production processes) , electric power system , mathematical optimization , electricity , thermal power station , reliability engineering , power (physics) , engineering , algorithm , electrical engineering , mathematics , biochemistry , chemistry , physics , quantum mechanics , gene
In order to minimise the electricity costs of the power system, this paper proposes an optimal operation scheduling for large‐scale new energy power stations with the uncertainty of wind and light power. The proposed scheduling method provides the optimal unit commitment and the economic operation, and it not only considers the generating costs of new energy power stations and thermal power plant but also considers switch machine cost of conventional thermal generating unit. The unit commitment of wind and light integrated power systems has a numerous of non‐linear characteristics, so the uncertainties require algorithm that can handle large amounts of robustness. Adjustable Robust Optimisation (ARO) can handle the uncertainty very well and is determined to be the main optimisation. At the same time, Particle Swarm Optimisation Algorithm (PSOA) is used to speed up the convergence. In summary, PSOA and ARO combination scheduling is demonstrated by the standard IEEE 10‐generator 39‐bus system. The improved ARO of simulation results prove that the conclusion of this paper is correct.