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Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch

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  • Dubey, Hari Mohan
  • Pandit, Manjaree
  • Panigrahi, B.K.

Abstract

To maintain security and reliability of wind integrated power grid, additional spinning reserve is required to meet the demand under changing loads and unpredictable wind power generation. This paper presents a solution of dynamic multi objective optimal dispatch (DMOOD) for wind-thermal system using a hybrid flower pollination algorithm (HFPA). Simultaneous minimization of cost, emission and losses is carried out with complex constraints like valve point loadings, ramp limits, prohibited zones and spinning reserve. The cost of wind power uncertainty is also included in the cost function by using a probability density function model. The proposed HFPA improves the exploration and exploitation potential of the flower population which is conducting the search. In the HFPA the flower pollination algorithm (FPA) and differential evolution (DE) algorithm are integrated to preserve good solutions and to stop premature convergence. A 5-class, 3-step time varying fuzzy selection mechanism (TVFSM) is integrated with HFPA for solving multi-objective problems. The TVFSM finds a fuzzy selection index (FSI) by aggregating different conflicting objectives. The FSI is adopted as the merit criterion while updating the population. Guassian membership function is applied to compute FSI in such a manner that extreme solutions are filtered out and trade off solutions on the central portion of the Pareto-front are obtained. The HFPA-TVFSM approach effectively searches the best compromise solution (BCS) which satisfies all the three objectives maximally. The proposed approach is tested and validated on two wind-thermal test systems from literature.

Suggested Citation

  • Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2015. "Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch," Renewable Energy, Elsevier, vol. 83(C), pages 188-202.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:188-202
    DOI: 10.1016/j.renene.2015.04.034
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    References listed on IDEAS

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    3. Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2016. "Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index," Renewable Energy, Elsevier, vol. 99(C), pages 18-34.
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    5. Salil Madhav Dubey & Hari Mohan Dubey & Manjaree Pandit & Surender Reddy Salkuti, 2021. "Multiobjective Scheduling of Hybrid Renewable Energy System Using Equilibrium Optimization," Energies, MDPI, vol. 14(19), pages 1-20, October.
    6. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Adel El-Shahat & Mahmoud A. Attia, 2021. "Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method," Energies, MDPI, vol. 14(19), pages 1-19, September.
    7. Sangeeta Pant & Anuj Kumar & Mangey Ram, 2017. "Flower pollination algorithm development: a state of art review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1858-1866, November.
    8. 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.
    9. 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.
    10. Karar Mahmoud & Mohamed Abdel-Nasser & Eman Mustafa & Ziad M. Ali, 2020. "Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems," Sustainability, MDPI, vol. 12(2), pages 1-21, January.
    11. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.
    12. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    13. Abdullah B Nasser & Kamal Z Zamli & AbdulRahman A Alsewari & Bestoun S Ahmed, 2018. "Hybrid flower pollination algorithm strategies for t-way test suite generation," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-24, May.
    14. Wu, Xinyu & Wu, Yiyang & Cheng, Xilong & Cheng, Chuntian & Li, Zehong & Wu, Yongqi, 2023. "A mixed-integer linear programming model for hydro unit commitment considering operation constraint priorities," Renewable Energy, Elsevier, vol. 204(C), pages 507-520.

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