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Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems

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  • Kheshti, Mostafa
  • Ding, Lei
  • Ma, Shicong
  • Zhao, Bing

Abstract

The dearth of power generation from energy resources, environmental concerns and ever-increasing demand for electrical energy necessitate optimal economic dispatch with minimum costs and emissions. Due to the confined optimum convergence and non-convexity of realistic scenarios, classical optimization methods are not proficient to handle such problems. Instead, evolutionary optimization methods have gained more attention in recent years. Application of a new proposed double weighted particle swarm optimization (DWPSO) technique in solving non-convex combined emission economic dispatch (CEED) problems with wind power penetration and also solving non-convex multiple fuel option economic dispatch problem has been technologically proposed in this paper. The results on several case study systems are compared with other published methods in literature and confirm the effectiveness of DWPSO against other existing methods. DWPSO successfully reduces the production costs as well as hazardous emissions considering wind power penetration, selects the best fuel types of the generators and adjusts the feasible and optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using the proposed method can minimize the total generation costs and optimally satisfy the power demands in the grid while the computation performance remains satisfactory even in case of changes in the scale of the network.

Suggested Citation

  • Kheshti, Mostafa & Ding, Lei & Ma, Shicong & Zhao, Bing, 2018. "Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems," Renewable Energy, Elsevier, vol. 125(C), pages 1021-1037.
    • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:1021-1037
    DOI: 10.1016/j.renene.2018.03.024
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    Cited by:

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    3. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    4. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    5. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
    6. Chen, Min-Rong & Zeng, Guo-Qiang & Lu, Kang-Di, 2019. "Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources," Renewable Energy, Elsevier, vol. 143(C), pages 277-294.
    7. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    8. Kheshti, Mostafa & Ding, Lei & Nayeripour, Majid & Wang, Xiaowei & Terzija, Vladimir, 2019. "Active power support of wind turbines for grid frequency events using a reliable power reference scheme," Renewable Energy, Elsevier, vol. 139(C), pages 1241-1254.

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