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Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review

Author

Listed:
  • Ismail Marouani

    (Control and Energy Management Laboratory, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia)

  • Tawfik Guesmi

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Hsan Hadj Abdallah

    (Control and Energy Management Laboratory, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia)

  • Badr M. Alshammari

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Khalid Alqunun

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Ahmed S. Alshammari

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Salem Rahmani

    (Research Laboratory of Biophysics and Medical Technology (BMT), Higher Institute of Medical Technologies, University of Tunis El-Manar, Tunis 2092, Tunisia)

Abstract

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4472-:d:842705
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    as
    1. Cai, Jiejin & Li, Qiong & Li, Lixiang & Peng, Haipeng & Yang, Yixian, 2012. "A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects," Energy, Elsevier, vol. 38(1), pages 346-353.
    2. Xiaobing Yu & Xianrui Yu & Yiqun Lu & Jichuan Sheng, 2018. "Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    3. Shilaja, C. & Ravi, K., 2017. "Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation," Renewable Energy, Elsevier, vol. 107(C), pages 550-566.
    4. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    5. Abdelaziz, A.Y. & Ali, E.S. & Abd Elazim, S.M., 2016. "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, Elsevier, vol. 101(C), pages 506-518.
    6. Fraga, Eric S. & Yang, Lingjian & Papageorgiou, Lazaros G., 2012. "On the modelling of valve point loadings for power electricity dispatch," Applied Energy, Elsevier, vol. 91(1), pages 301-303.
    7. F. Daniel Santillán-Lemus & Hertwin Minor-Popocatl & Omar Aguilar-Mejía & Ruben Tapia-Olvera, 2019. "Optimal Economic Dispatch in Microgrids with Renewable Energy Sources," Energies, MDPI, vol. 12(1), pages 1-14, January.
    8. Elattar, Ehab E., 2018. "Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources," Energy, Elsevier, vol. 159(C), pages 496-507.
    9. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    10. 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.
    11. Kuo, Cheng-Chien, 2010. "Wind energy dispatch considering environmental and economic factors," Renewable Energy, Elsevier, vol. 35(10), pages 2217-2227.
    12. 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.
    13. 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.
    14. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    15. Özyön, Serdar & Temurtaş, Hasan & Durmuş, Burhanettin & Kuvat, Gültekin, 2012. "Charged system search algorithm for emission constrained economic power dispatch problem," Energy, Elsevier, vol. 46(1), pages 420-430.
    16. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    17. K. Chandrasekaran & Sishaj P. Simon, 2014. "Wind-Thermal Integrated Power System Scheduling Problem Using Cuckoo Search Algorithm," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 5(3), pages 81-109, July.
    18. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    19. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
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    Cited by:

    1. Abdulaziz Almalaq & Tawfik Guesmi & Saleh Albadran, 2023. "A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem," Energies, MDPI, vol. 16(12), pages 1-34, June.
    2. Ismail Marouani & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Saleh Albadran & Hsan Hadj Abdallah & Salem Rahmani, 2023. "Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods," Sustainability, MDPI, vol. 15(12), pages 1-58, June.

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