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Solving the Unit Commitment Problem with Improving Binary Particle Swarm Optimization

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13344))

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Abstract

Unit commitment is a traditional mixed-integer non-convex problem and an optimization task in power system scheduling. The traditional methods of solving the Unit commitment problem have some problems, such as slow solving speed, low accuracy and complex calculation. Therefore, intelligent algorithms have been applied to solve the unit combination problem with continues and discrete feature, such as Particle Swarm Optimization, Genetic Algorithm. In order to improve the solution quality of Unit commitment, this paper proposes the adaptive binary Particle Swarm Optimization with V-shaped transfer function to solve the unit commitment problem, and adopts the policy of the segmented solution. By comparison with some classical algorithm in the same unit model, the experimental results show that solving the UC problem by using improved algorithm with segmented solution has higher stability and lower total energy consumption.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350108, China

    Jianhua Liu, Zihang Wang, Yuxiang Chen & Jian Zhu

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350108, China

    Jianhua Liu, Zihang Wang, Yuxiang Chen & Jian Zhu

Authors
  1. Jianhua Liu

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  2. Zihang Wang

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  3. Yuxiang Chen

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  4. Jian Zhu

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Corresponding author

Correspondence toJianhua Liu.

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Liu, J., Wang, Z., Chen, Y., Zhu, J. (2022). Solving the Unit Commitment Problem with Improving Binary Particle Swarm Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_16

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