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An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units

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  • Kheshti, Mostafa
  • Kang, Xiaoning
  • Bie, Zhaohong
  • Jiao, Zaibin
  • Wang, Xiuli

Abstract

Generation units with multiple fuel options and valve-point loading effects on the generators are fundamental parts of power system generation. However, economic dispatch (ED) of these units have non-convex, non-continuous generation cost functions. In this paper, a new Lightning Flash Algorithm (LFA) is proposed to solve complex non-convex ED problems in large scale power systems considering multiple fuel options and valve point effects on the generators. Five case study systems including 10-units, 40-units, 80-units, 160-units and 640-units are conducted to validate the applicability and effectiveness of the proposed LFA method for solving ED problems. The results are compared with other published methods in literature and confirm the applicability and effectiveness of LFA against other existing methods. LFA can successfully conduct the best fuel types of the generators and adjust the optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using proposed LFA method can minimize the total generation costs and optimally satisfy the load demands in the power grid.

Suggested Citation

  • Kheshti, Mostafa & Kang, Xiaoning & Bie, Zhaohong & Jiao, Zaibin & Wang, Xiuli, 2017. "An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units," Energy, Elsevier, vol. 129(C), pages 1-15.
    • Handle: RePEc:eee:energy:v:129:y:2017:i:c:p:1-15
    DOI: 10.1016/j.energy.2017.04.081
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    References listed on IDEAS

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    1. Meng, Anbo & Li, Jinbei & Yin, Hao, 2016. "An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects," Energy, Elsevier, vol. 113(C), pages 1147-1161.
    2. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
    3. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    4. 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.
    5. Osório, G.J. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources," Energy, Elsevier, vol. 82(C), pages 949-959.
    6. Shukla, Anup & Singh, S.N., 2016. "Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem," Energy, Elsevier, vol. 96(C), pages 23-36.
    7. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    8. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    9. Vo, Dieu Ngoc & Ongsakul, Weerakorn, 2012. "Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network," Applied Energy, Elsevier, vol. 91(1), pages 281-289.
    10. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    11. Gherbi, Yamina Ahlem & Bouzeboudja, Hamid & Gherbi, Fatima Zohra, 2016. "The combined economic environmental dispatch using new hybrid metaheuristic," Energy, Elsevier, vol. 115(P1), pages 468-477.
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    Cited by:

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    2. El-Sayed, Wael T. & El-Saadany, Ehab F. & Zeineldin, Hatem H. & Al-Sumaiti, Ameena S., 2020. "Fast initialization methods for the nonconvex economic dispatch problem," Energy, Elsevier, vol. 201(C).
    3. 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.
    4. Chen, Xu, 2020. "Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects," Energy, Elsevier, vol. 203(C).
    5. 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.
    6. Mohammad Lotfi Akbarabadi & Reza Sirjani, 2023. "Achieving Sustainability and Cost-Effectiveness in Power Generation: Multi-Objective Dispatch of Solar, Wind, and Hydro Units," Sustainability, MDPI, vol. 15(3), pages 1-33, January.
    7. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    8. 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).
    9. Naila & Shaikh Saaqib Haroon & Shahzad Hassan & Salman Amin & Intisar Ali Sajjad & Asad Waqar & Muhammad Aamir & Muneeb Yaqoob & Imtiaz Alam, 2018. "Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution," Energies, MDPI, vol. 11(6), pages 1-20, May.

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