A two-layer algorithm based on PSO for solving unit commitment problem

  • Yu Zhai
  • Xiaofeng LiaoEmail author
  • Nankun Mu
  • Junqing Le
Methodologies and Application


It is well known that electric generators consume huge amounts of energy every year. Nowadays, research for the unit commitment problem (UCP) has become a very important task in a power plant. However, the existing optimal methods for solving UCP are very easy to fall into local optimum, resulting in poor performance. Moreover, as no separate layering of economic load distribution, the existing algorithms are very inefficient. Toward this end, a new algorithm named improved simulated annealing particle swarm optimization (ISAPSO) is proposed in this paper. The proposed algorithm consists of a two-layer structure which is designed to simplify the complex problem of UCP. Specifically, in the upper layer, the algorithm based on elitist strategy PSO and SA is much easier to jump out of the local optimum when solving UCP and thus gets a better solution. In the lower layer, convex optimization approach is used to improve the search efficiency of ISAPSO. Furthermore, several methods are also designed to solve the problem-related constraints, which can save a lot of computing resources. Finally, the experimental results show that the cost performance of ISAPSO is better than that of the existing algorithms.


Unit commitment problem Economic load distribution Particle swarm optimization Simulated annealing algorithm 



This work is supported in part by National Key Research and Development Program of China (Grant no. 2018AAA0100101), in part by National Natural Science Foundation of China (Grant no. 61932006, 61772434, 61403121, 61806169), in part by China Postdoctoral Science Foundation under Grant 2018M643085, and in part by Fundamental Research Funds for the Central Universities (Grant no. XDJK2018D005, XDJK2019C020).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information EngineeringSouthwest UniversityChongqingPeople’s Republic of China
  2. 2.Key Laboratory of Dependable Service Computing in Cyber Physical Society-Ministry of Education, College of Computer ScienceChongqing UniversityChongqingPeople’s Republic of China

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