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Utilizing Particle Swarm Optimization Algorithm for Energy Management Application

Submission Deadline: 01 January 2023 (closed)

Guest Editors

Wali Khan Mashwani, Kohat University of Science & Technology (KUST), Pakistan. Email: walikhan@ieee.org
Atila Goktas, Muğla Sıtkı Koçman Üniversitesi,Turkey. Email: gatilla@mu.edu.tr
Zakia Hammouch, Moulay Ismail University, Morocco. Email: zakia.hammouch@fste.umi.ac.ma


Swarm optimization is a new, popular and helpful approach for solving complex energy management application problems. The introduction of various parameters used in non-renewable energy sources and renewable energy sources is included in this work. The work also consists of the optimization techniques for both types of energy sources utilized in a wireless sensor network. The algorithm can ensure an appropriate energy consumption by demand response with sequence-dependent switching costs. The benefits are as follows: Saving energy; Reducing carbon dioxide emissions; Improving distribution efficiency; Improving customer satisfaction, and supporting other sustainable technologies. The main challenge associated with this algorithm is the need for exhaustive calculation done by every particle in the swarm. This implies that computation speed is limited and makes it unsuitable for large-scale applications in the power system environment.


Swarm Optimization, energy sources, electricity, renewable energy, PSO, WSN, challenges

Published Papers

  • Open Access


    Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

    Zhonghao Qian, Hanyi Ma, Jun Rao, Jun Hu, Lichengzi Yu, Caoyi Feng, Yunxu Qiu, Kemo Ding
    Energy Engineering, Vol.120, No.9, pp. 2013-2027, 2023, DOI:10.32604/ee.2023.028859
    (This article belongs to this Special Issue: Utilizing Particle Swarm Optimization Algorithm for Energy Management Application)
    Abstract The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms. To improve the voltage stability and reactive power economy of wind farms, the improved particle swarm optimization is used to optimize the reactive power planning in wind farms. First, the power flow of offshore wind farms is modeled, analyzed and calculated. To improve the global search ability and local optimization ability of particle swarm optimization, the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor. Taking the minimum active power loss of the offshore wind… More >

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