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Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems

Author

Listed:
  • Umair Hussan

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China)

  • Huaizhi Wang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China)

  • Muhammad Ahsan Ayub

    (College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518000, China)

  • Hamna Rasheed

    (College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518000, China)

  • Muhammad Asghar Majeed

    (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand)

  • Jianchun Peng

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China)

  • Hui Jiang

    (College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518000, China)

Abstract

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

Suggested Citation

  • Umair Hussan & Huaizhi Wang & Muhammad Ahsan Ayub & Hamna Rasheed & Muhammad Asghar Majeed & Jianchun Peng & Hui Jiang, 2024. "Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems," Mathematics, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3064-:d:1489317
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    References listed on IDEAS

    as
    1. Cheng Yang & Yupeng Sun & Yujie Zou & Fei Zheng & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods," Energies, MDPI, vol. 16(16), pages 1-42, August.
    2. Li, Ru & Tang, Bao-Jun & Yu, Biying & Liao, Hua & Zhang, Chen & Wei, Yi-Ming, 2022. "Cost-optimal operation strategy for integrating large scale of renewable energy in China’s power system: From a multi-regional perspective," Applied Energy, Elsevier, vol. 325(C).
    3. Amal Amin Mohamed & Salah Kamel & Mohamed H. Hassan & Mohamed I. Mosaad & Mansour Aljohani, 2022. "Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power," Mathematics, MDPI, vol. 10(3), pages 1-31, January.
    4. Wenchao Yi & Zhilei Lin & Youbin Lin & Shusheng Xiong & Zitao Yu & Yong Chen, 2023. "Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution," Mathematics, MDPI, vol. 11(5), pages 1-13, March.
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    1. Mohammed Goda Eisa & Mohammed A. Farahat & Wael Abdelfattah & Mohammed Elsayed Lotfy, 2024. "Multi-Objective Optimal Integration of Distributed Generators into Distribution Networks Incorporated with Plug-In Electric Vehicles Using Walrus Optimization Algorithm," Sustainability, MDPI, vol. 16(22), pages 1-37, November.

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