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Optimization of integrated load dispatch in multi-fueled renewable rich power systems using fractal firefly algorithm

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  • Mehmood, Ammara
  • Raja, Muhammad Asif Zahoor
  • Jalili, Mahdi

Abstract

This manuscript applied fractal firefly optimization technique to the integrated load dispatch (ILD) problem involving loads allocation to power generating units with the minimum total fuel cost and emissions while handling the constraints for large-scale multi-fueled power plants and integration of stochastic wind to provide sustainable energy. The penetration of wind energy into conventional grids has resolved power system scheduling issues at the cost of rise in the stiffness of nonconvex ILD optimization problem. In order to enhance the dispatch performance with significant penetration of wind energy, an effective framework of fractal heritage is introduced inspired by Firefly algorithm. In order to validate the efficacy and flexibility of the proposed algorithm, sundry non-convex ILD optimization scenarios with valve-point effects, capacity constraints, multi-fuels power plants and Weibull's distributed stochastic wind uncertainty is implemented. The results reveal stupendous performance in term of the minimum fuel cost with higher efficiency and finer convergence rate than that of conventional algorithms.

Suggested Citation

  • Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Jalili, Mahdi, 2023. "Optimization of integrated load dispatch in multi-fueled renewable rich power systems using fractal firefly algorithm," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223011866
    DOI: 10.1016/j.energy.2023.127792
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    1. Raheela Jamal & Baohui Men & Noor Habib Khan & Muhammad Asif Zahoor Raja, 2019. "Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind," Energies, MDPI, vol. 12(13), pages 1-23, July.
    2. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations," Energy, Elsevier, vol. 246(C).
    3. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    4. Atangana, Abdon, 2017. "Fractal-fractional differentiation and integration: Connecting fractal calculus and fractional calculus to predict complex system," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 396-406.
    5. Zhao, Baining & Qian, Tong & Tang, Wenhu & Liang, Qiheng, 2022. "A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty," Energy, Elsevier, vol. 243(C).
    6. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    7. Xu, Shengping & Xiong, Guojiang & Mohamed, Ali Wagdy & Bouchekara, Houssem R.E.H., 2022. "Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options," Energy, Elsevier, vol. 256(C).
    8. Atangana, Abdon & Gómez-Aguilar, J.F., 2018. "Fractional derivatives with no-index law property: Application to chaos and statistics," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 516-535.
    9. Zou, Dexuan & Gong, Dunwei, 2022. "Differential evolution based on migrating variables for the combined heat and power dynamic economic dispatch," Energy, Elsevier, vol. 238(PA).
    10. Xiong, Guojiang & Shuai, Maohang & Hu, Xiao, 2022. "Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization," Energy, Elsevier, vol. 244(PB).
    11. Mousavi, Yashar & Alfi, Alireza, 2018. "Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 202-215.
    12. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2020. "An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    13. Ahmed, Ijaz & Rehan, Muhammad & Basit, Abdul & Malik, Saddam Hussain & Alvi, Um-E-Habiba & Hong, Keum-Shik, 2022. "Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations," Energy, Elsevier, vol. 261(PB).
    14. Muhammad, Yasir & Khan, Nusrat & Awan, Saeed Ehsan & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Kiani, Adiqa Kausar & Ullah, Farman & Shu, Chi-Min, 2022. "Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    15. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    16. 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.
    17. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    18. Benyekhlef Larouci & Ahmed Nour El Islam Ayad & Hisham Alharbi & Turki E. A. Alharbi & Houari Boudjella & Abdelkader Si Tayeb & Sherif S. M. Ghoneim & Saad A. Mohamed Abdelwahab, 2022. "Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
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