Unit Commitment via Collaborative Neurodynamic Optimization

Chengshuo Zhang; Meng Xu; Shaofu Yang; Wenying Xu; Zhongying Chen

doi:10.1109/ICACI65340.2025.11096191

Unit Commitment via Collaborative Neurodynamic Optimization

Chengshuo Zhang, Meng Xu, Shaofu Yang, Wenying Xu, Zhongying Chen^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Unit commitment is essential for the operation of power systems. Optimal unit commitment is able to achieve significant cost savings. In this paper, a discrete optimization problem and a quadratic optimization problem are formulated for unit commitment. An approach based on collaborative neurodynamic optimization, named CNO-UC, is proposed to solve the formulated problems. Many pairs of neurodynamic models are used to solve the problems alternately. A metaheuristic mechanism is adopted to reinitialize the states of the pairs to move away from local optima and towards the global optimum. Simulation experiments are conducted on a five-unit system and an eight-unit system to validate the effectiveness of CNO-UC. Experimental results demonstrate the superiority of CNO-UC over several baseline methods for unit commitment. © 2025 IEEE.

Original language	English
Title of host publication	2025 17th International Conference on Advanced Computational Intelligence (ICACI)
Publisher	IEEE
Pages	303-311
Number of pages	9
ISBN (Electronic)	979-8-3315-0979-8
ISBN (Print)	979-8-3315-0980-4
DOIs	https://doi.org/10.1109/ICACI65340.2025.11096191
Publication status	Published - 2025
Event	17th International Conference on Advanced Computational Intelligence (ICACI 2025) - Bath, United Kingdom Duration: 7 Jul 2025 → 13 Jul 2025

Conference

Conference	17th International Conference on Advanced Computational Intelligence (ICACI 2025)
Place	United Kingdom
City	Bath
Period	7/07/25 → 13/07/25

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62406067, and in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20241298.

Research Keywords

collaborative neurodynamic optimization
discrete Hopfield network
projection neural network
Unit commitment

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10.1109/ICACI65340.2025.11096191

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title = "Unit Commitment via Collaborative Neurodynamic Optimization",

abstract = "Unit commitment is essential for the operation of power systems. Optimal unit commitment is able to achieve significant cost savings. In this paper, a discrete optimization problem and a quadratic optimization problem are formulated for unit commitment. An approach based on collaborative neurodynamic optimization, named CNO-UC, is proposed to solve the formulated problems. Many pairs of neurodynamic models are used to solve the problems alternately. A metaheuristic mechanism is adopted to reinitialize the states of the pairs to move away from local optima and towards the global optimum. Simulation experiments are conducted on a five-unit system and an eight-unit system to validate the effectiveness of CNO-UC. Experimental results demonstrate the superiority of CNO-UC over several baseline methods for unit commitment. {\textcopyright} 2025 IEEE.",

keywords = "collaborative neurodynamic optimization, discrete Hopfield network, projection neural network, Unit commitment",

author = "Chengshuo Zhang and Meng Xu and Shaofu Yang and Wenying Xu and Zhongying Chen",

year = "2025",

doi = "10.1109/ICACI65340.2025.11096191",

language = "English",

isbn = "979-8-3315-0980-4",

pages = "303--311",

booktitle = "2025 17th International Conference on Advanced Computational Intelligence (ICACI)",

publisher = "IEEE",

address = "United States",

note = "17th International Conference on Advanced Computational Intelligence (ICACI 2025) ; Conference date: 07-07-2025 Through 13-07-2025",

}

Zhang, C, Xu, M, Yang, S, Xu, W & Chen, Z 2025, Unit Commitment via Collaborative Neurodynamic Optimization. in 2025 17th International Conference on Advanced Computational Intelligence (ICACI). IEEE, pp. 303-311, 17th International Conference on Advanced Computational Intelligence (ICACI 2025), Bath, United Kingdom, 7/07/25. https://doi.org/10.1109/ICACI65340.2025.11096191

TY - GEN

T1 - Unit Commitment via Collaborative Neurodynamic Optimization

AU - Zhang, Chengshuo

AU - Xu, Meng

AU - Yang, Shaofu

AU - Xu, Wenying

AU - Chen, Zhongying

PY - 2025

Y1 - 2025

N2 - Unit commitment is essential for the operation of power systems. Optimal unit commitment is able to achieve significant cost savings. In this paper, a discrete optimization problem and a quadratic optimization problem are formulated for unit commitment. An approach based on collaborative neurodynamic optimization, named CNO-UC, is proposed to solve the formulated problems. Many pairs of neurodynamic models are used to solve the problems alternately. A metaheuristic mechanism is adopted to reinitialize the states of the pairs to move away from local optima and towards the global optimum. Simulation experiments are conducted on a five-unit system and an eight-unit system to validate the effectiveness of CNO-UC. Experimental results demonstrate the superiority of CNO-UC over several baseline methods for unit commitment. © 2025 IEEE.

AB - Unit commitment is essential for the operation of power systems. Optimal unit commitment is able to achieve significant cost savings. In this paper, a discrete optimization problem and a quadratic optimization problem are formulated for unit commitment. An approach based on collaborative neurodynamic optimization, named CNO-UC, is proposed to solve the formulated problems. Many pairs of neurodynamic models are used to solve the problems alternately. A metaheuristic mechanism is adopted to reinitialize the states of the pairs to move away from local optima and towards the global optimum. Simulation experiments are conducted on a five-unit system and an eight-unit system to validate the effectiveness of CNO-UC. Experimental results demonstrate the superiority of CNO-UC over several baseline methods for unit commitment. © 2025 IEEE.

KW - collaborative neurodynamic optimization

KW - discrete Hopfield network

KW - projection neural network

KW - Unit commitment

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U2 - 10.1109/ICACI65340.2025.11096191

DO - 10.1109/ICACI65340.2025.11096191

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3315-0980-4

SP - 303

EP - 311

BT - 2025 17th International Conference on Advanced Computational Intelligence (ICACI)

PB - IEEE

T2 - 17th International Conference on Advanced Computational Intelligence (ICACI 2025)

Y2 - 7 July 2025 through 13 July 2025

ER -

Unit Commitment via Collaborative Neurodynamic Optimization

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