IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3253-d805109.html
   My bibliography  Save this article

Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm

Author

Listed:
  • Eliton Smith dos Santos

    (Post-Graduate Program in Electrical Engineering, Federal University of Para—UFPA, Belem 66075-110, PA, Brazil)

  • Marcus Vinícius Alves Nunes

    (Post-Graduate Program in Electrical Engineering, Federal University of Para—UFPA, Belem 66075-110, PA, Brazil)

  • Manoel Henrique Reis Nascimento

    (Research Department, Institute of Technology and Education Galileo of the Amazon—ITEGAM, Manaus 69020-030, AM, Brazil)

  • Jandecy Cabral Leite

    (Research Department, Institute of Technology and Education Galileo of the Amazon—ITEGAM, Manaus 69020-030, AM, Brazil
    Jandecy Cabral Leite is a member of IEEE.)

Abstract

The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm.

Suggested Citation

  • Eliton Smith dos Santos & Marcus Vinícius Alves Nunes & Manoel Henrique Reis Nascimento & Jandecy Cabral Leite, 2022. "Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm," Energies, MDPI, vol. 15(9), pages 1-31, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3253-:d:805109
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3253/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3253/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    2. Ogunmodede, Oluwaseun & Anderson, Kate & Cutler, Dylan & Newman, Alexandra, 2021. "Optimizing design and dispatch of a renewable energy system," Applied Energy, Elsevier, vol. 287(C).
    3. Bustos, Cristian & Watts, David & Olivares, Daniel, 2019. "The evolution over time of Distributed Energy Resource’s penetration: A robust framework to assess the future impact of prosumage under different tariff designs," Applied Energy, Elsevier, vol. 256(C).
    4. Ma, Haiping & Yang, Zhile & You, Pengcheng & Fei, Minrui, 2017. "Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging," Energy, Elsevier, vol. 135(C), pages 101-111.
    5. Stavros P. Adam & Stamatios-Aggelos N. Alexandropoulos & Panos M. Pardalos & Michael N. Vrahatis, 2019. "No Free Lunch Theorem: A Review," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 57-82, Springer.
    6. Wei-Tzer Huang & Kai-Chao Yao & Ming-Ku Chen & Feng-Ying Wang & Cang-Hui Zhu & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2018. "Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch," Energies, MDPI, vol. 11(2), pages 1-19, February.
    7. Muhsen, Dhiaa Halboot & Nabil, Moamen & Haider, Haider Tarish & Khatib, Tamer, 2019. "A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods," Energy, Elsevier, vol. 174(C), pages 1158-1175.
    8. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tingli Cheng & Minyou Chen & Yingxiang Wang & Bo Li & Muhammad Arshad Shehzad Hassan & Tao Chen & Ruilin Xu, 2018. "Adaptive Robust Method for Dynamic Economic Emission Dispatch Incorporating Renewable Energy and Energy Storage," Complexity, Hindawi, vol. 2018, pages 1-13, June.
    2. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    3. Yuanyuan He & Luxin Wan & Manli Zhang & Huijuan Zhao, 2022. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    4. Masoud Zahedi Vahid & Ziad M. Ali & Ebrahim Seifi Najmi & Abdollah Ahmadi & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm," Energies, MDPI, vol. 14(16), pages 1-25, August.
    5. Kong, Xue & Wang, Hongye & Li, Nan & Mu, Hailin, 2022. "Multi-objective optimal allocation and performance evaluation for energy storage in energy systems," Energy, Elsevier, vol. 253(C).
    6. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    7. Jilong Zhang & Yuan Diao, 2024. "Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer," Mathematics, MDPI, vol. 12(17), pages 1-26, August.
    8. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
    9. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Mikulik, Jerzy, 2021. "A hybrid method for scenario-based techno-economic-environmental analysis of off-grid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    10. He, Yi & Guo, Su & Zhou, Jianxu & Wu, Feng & Huang, Jing & Pei, Huanjin, 2021. "The many-objective optimal design of renewable energy cogeneration system," Energy, Elsevier, vol. 234(C).
    11. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    12. Meinrenken, Christoph J. & Mehmani, Ali, 2019. "Concurrent optimization of thermal and electric storage in commercial buildings to reduce operating cost and demand peaks under time-of-use tariffs," Applied Energy, Elsevier, vol. 254(C).
    13. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).
    14. Sheha, Moataz & Mohammadi, Kasra & Powell, Kody, 2021. "Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage," Applied Energy, Elsevier, vol. 282(PA).
    15. Ridha, Hussein Mohammed & Gomes, Chandima & Hizam, Hashim & Ahmadipour, Masoud & Heidari, Ali Asghar & Chen, Huiling, 2021. "Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    16. Huang, Yan & Ju, Yuntao & Ma, Kang & Short, Michael & Chen, Tao & Zhang, Ruosi & Lin, Yi, 2022. "Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation," Applied Energy, Elsevier, vol. 305(C).
    17. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    18. Ahmed I. Omar & Ziad M. Ali & Mostafa Al-Gabalawy & Shady H. E. Abdel Aleem & Mujahed Al-Dhaifallah, 2020. "Multi-Objective Environmental Economic Dispatch of an Electricity System Considering Integrated Natural Gas Units and Variable Renewable Energy Sources," Mathematics, MDPI, vol. 8(7), pages 1-37, July.
    19. Qiu, Lihua & He, Li & Kang, Yu & Liang, Dongzhe, 2022. "Assessment of the potential of enhanced geothermal systems in Asia under the impact of global warming," Renewable Energy, Elsevier, vol. 194(C), pages 636-646.
    20. Yu, Xiaobing & Duan, Yuchen & Luo, Wenguan, 2022. "A knee-guided algorithm to solve multi-objective economic emission dispatch problem," Energy, Elsevier, vol. 259(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3253-:d:805109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.